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Third‐Person Effect

Article · September 2020

DOI: 10.1002/9781119011071.iemp0130

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Third-Person Effect MINA TSAY-VOGEL Boston University, USA

Third-person effect (TPE) is the tendency for people to perceive others as being consid- erably more influenced by mass media messages as compared to themselves (Davison, 1983). In other words, an individual believes that messages do not have their great- est effect “on me or you, but on them—the third persons” (Davison, 1983, p. 3). This perceptual bias to exaggerate the impact of mass communication on public opinion can have consequential effects on actions, often referred to as the behavioral compo- nent of TPE. An assumption made by TPE is that estimates of media influence on self and others are separate entities such that people have the potential to discriminate the impact of mass media on others and that on themselves (Perloff, 1999). Scholars have also referred to this seeming discrepancy in media influence as a perceptual distortion, implying that most individuals are willing to acknowledge this logical inconsistency (Tiedge, Silverblatt, Havice, & Rosenfeld, 1991).

Applications of TPE

The concept of TPE has been applied to a multitude of media channels, message types, media genres, and media sources. Early support for TPE (Davison, 1983) revealed that when asked to assess the influence of persuasive communication, people were more likely to report that everyone else is more easily influenced by media messages than themselves when evaluating the effect of television advertising on children and the impact of campaign themes, early presidential primaries, and campaign advertising on voters. The judgment of greater media effects on others than the self has also received empirical support in various contexts including pornography, defamatory news, news about controversial political issues, television violence, product commercials, and rap music (Perloff, 1993, 1999). Meta-analyses of TPE have yielded average effect sizes (r) of .50 (Paul, Salwen, & Dupagne, 2000) and .31 (Sun, Pan, & Shen, 2008) for the perceptual component of the hypothesis.

Recent work has applied TPE to new media to understand self–other disparities in the estimation of media impact in the context of social media (Golan & Lim, 2016; Schweisberger, Billinson, & Chock, 2014; Tsay-Vogel, 2016). Specifically, users reported that Facebook exerts stronger effects on others than themselves, yielding patterns consistent with the TPE hypothesis (Tsay-Vogel, 2016). Self–other differences in perceived media vulnerability have also been investigated in numerous online contexts as they pertain to the presumed influence of privacy, brand marketing,

The International Encyclopedia of Media Psychology. Jan Van den Bulck (Editor-in-Chief), David Ewoldsen, Marie-Louise Mares, and Erica Scharrer (Associate Editors). © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc. DOI: 10.1002/9781119011071.iemp0130

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social media posts, recruitment for political and social causes, blogs, news, and health information.

Theoretical mechanisms of TPE

The theoretical underpinnings of TPE have commonly focused on why social compar- isons and contrasts are made. According to attribution theory (Heider, 1958), individ- uals strive to understand relevant events and behaviors by making judgments based on observations. However, there are instances which result in discrepancies between attributes to self and others such that people tend to ascribe their own actions to sit- uational contexts, whereas observers are more likely to ascribe the same actions to personal characteristics. This actor–observer difference in causal attributions of behav- ior is referred to as attributional error and helps to explain why individuals believe they are more informed about and resistant to the persuasive appeal (e.g., source intention) of a media message than others as they are inclined to attribute dispositional shortcom- ings to other people (e.g., credulity or lack of knowledge) (Gunther, 1991).

Third-person effect is fundamentally grounded on the motivation to preserve a posi- tive self-image based on social desirability, also referred to as the self-serving bias (Gun- ther & Mundy, 1993). People tend to engage in downward comparisons (i.e., comparing themselves to those worse off) in order to maintain or enhance self-esteem (Gunther, 1991). Individuals may also make social comparisons to the point of upholding unre- alistic positive images of themselves such that they believe they are less susceptible to negative outcomes and conversely, more likely to experience positive ones as compared to others (Gunther & Mundy, 1993). This biased optimism can further explain why people deem themselves as being more impervious to the negative influence of persua- sive messages as they believe they are wiser than average people. However, this effect is assumed to only occur under conditions in which the perception of oneself as being less influenced by media has self-serving benefits or is ego-enhancing.

In addition to self-enhancement motivation as a psychological driver of TPE, Perloff (1993) suggests that media effects schemas, or mental structures of preconceived ideas about the media and the audience, partially explain the differential estimated effects of media on self versus others. Based on traditional conceptions of mass communication informed by the hypodermic needle model, individuals have a tendency to believe that media are powerful agents of change and that audience members are normally passive, unaware, and susceptible to media influence. In turn, people would logically judge others as falling prey to media persuasion as compared to themselves.

Scholars also suggest that perceived media exposure contributes to the assessment of media impact (Golan & Lim, 2016; McLeod, Eveland, & Nathanson, 1997; Tsay-Vogel, 2016). It is possible that people use simple heuristics of media exposure to guide their evaluation of media effects. For example, individuals may believe that if others are consuming media, they will inevitably be influenced by such messages, consistent with early theories of mass communication as having direct and prevailing effects on audiences.

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Conditions affecting TPE

Research has shown that a number of factors influence the degree to which TPE is facilitated or minimized. Perceived message desirability has been found to be negatively associated with the likelihood and magnitude of TPE (Duck & Mullin, 1995; Gunther & Mundy, 1993). Specifically, individuals have greater propensity to report that others are more affected by media messages than they are themselves when judging the impact of content that has harmful consequences (e.g., advertisements promoting diet pills, news stories in support of extreme right-wing political parties, and messages containing themes of violence, sexism, and racism). More pronounced TPE for antisocial messages or those not consonant with social norms are explained in light of the self-serving inclination to see oneself in a positive light (e.g., being intelligent enough to resist the effects of negative messages) and judge vulnerable others as being more likely to be duped. Furthermore, media formats that are generally perceived as “undesirable to be influenced by,” such as commercial advertisements, result in greater TPE than those lacking such negative connotation, such as news and public service announcements (Gunther & Mundy, 1993). In essence, denying the effect of media especially in the case when such messages or formats are deemed personally disadvantageous helps to project a superior self-image.

Conversely, in the case of socially desirable messages or those that advocate personally beneficial outcomes, scholars suggest that people are less likely to exhibit TPE and, instead, acknowledge stronger media influence on themselves than others. This phenomenon is referred to as first-person effect (FPE) or reversed TPE (Tiedge et al., 1991). FPE is also consistent with the self-enhancement perspective as the impact of prosocial messages is considered positive, highly regarded, and ego-boosting (Duck & Mullin, 1995; Gunther & Mundy, 1993). While there is robust support for antisocial messages producing TPE, studies examining FPE have yielded mixed results. There is some evidence corroborating that people who deem public service campaigns as desirable report greater self-influence (Duck & Mullin, 1995), however, most studies examining discrepancies in perceived media effects between self and others in the context of messages that convey positive social values (e.g., wearing seatbelts, adhering to traffic safety, showing resistance to antisocial temptations, and displaying concern for others) have revealed only diminished TPE and no FPE (Gunther & Mundy, 1993). The lack of support for FPE may be explained by the assumed perception of social desirability of media messages as opinions about issues or message desirability are not explicitly measured (Paul et al., 2000). It is possible that in these studies, desirable messages were not perceived as personally beneficial as they were presumed to be. Furthermore, while messages associated with violence, sexism, or racism are generally considered antisocial in nature, other message types such as news or advertising are more ambiguous in terms of message desirability. Individuals may also be inherently hesitant to admit to any personal influence as doing so would be ego-threatening. Therefore, even if people comply with positive messages, the mere acknowledgment of being affected by media is perhaps undesirable (Gunther & Mundy, 1993), potentially accounting for the less readily documented FPE.

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Another factor that can affect the magnitude of TPE pertains to the source of the message. Specifically, if the message communicator is judged to be negatively biased or if the audience believes there is persuasive intent behind the message, the disparity in perceptions of media effects between self and others increases (Cohen, Mutz, Price, & Gunther, 1988; Gunther, 1991; Gunther & Mundy, 1993). The influence of source bias on TPE has been examined in the context of libelous, defamatory, and sensationalistic news. Specifically, research has found that the self–other discrepancy in perceived influence of media was greater when the story was delivered by a biased source such as The National Enquirer, as compared to an unbiased source such as The New York Times (Gunther, 1991).

In addition to message desirability and source bias, the strength of TPE also hinges on the perceived social distance between the self and comparison other (Perloff, 1999). Social distance takes into consideration one’s perceived similarity, familiarity, and identification with the referent group (Perloff, 1993). According to the social distance corollary, as the perceived difference between self and others increases (i.e., referent others are more psychologically removed from the self), TPE is more pronounced. Explanations of this phenomenon have been informed by the process of downward social comparison and the evaluation of distant others as an outgroup. Additionally, if target referents have attributes that align with the prototype of being weak and vul- nerable to media influence, a much larger self–other difference in presumed message effects results (Sun et al., 2008). Considerable support for the social distance corollary has been documented, showing that people are more likely to estimate stronger effects of media on others when evaluating generalized and hypothetical third-persons (e.g., the public at large or people defined in broad, vague, and global terms) versus close others (e.g., friends) (Gunther, 1991).

Other individual difference factors that influence the strength of TPE include ego-involvement, knowledge, and age. When individuals are more ego-involved in a message (i.e., find the topic to be personally important or relevant to their self-concept), the tendency to perceive media as affecting others more than themselves becomes magnified (Perloff, 1999, 2003). Specifically, highly ego-involved individuals are apt to view media sources as negatively biased due to having strong and extreme attitudes about an issue. This fosters not only an increased disparity in perceptions of media effects between self and others, but also the hostile media phenomenon, the belief that neutral messages are biased against one’s own perspective (Vallone, Ross, & Lepper, 1985). Recent work in the context of social media reveals that people tend to evaluate personally relevant news on Facebook as exerting stronger influence on themselves as compared to non-personally relevant news (Schweisberger et al., 2014). Moreover, subjects in the study judged stories of low relevance to have greater effects on others than themselves, supporting the general patterns of TPE. TPE is also likely to occur among individuals with better education and those older in age (Tiedge et al., 1991). People with greater knowledge perhaps believe they are more impervious to mass media’s influence as they see themselves as having the intelligence and expertise to resist such effects, compared to others who lack this proficiency. Older people may assess mass communication as exerting minimal effects on themselves in light of their

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confidence in countering such influence due to life experiences and accessibility of social attitudes.

Criticisms of TPE

Although there has been robust empirical support for TPE, scholars have questioned whether the disparity in perceived media influence between self and others is an outcome of a measurement artifact (Perloff, 1999). Researchers have typically used two methods to empirically test self–other discrepancies in the evaluation of media influence. In particular, experiments have exposed individuals to certain types of media messages and then asked subjects to evaluate the influence of such content on their own attitudes and the attitudes of others. Alternatively, participants have been surveyed to simply report their assessments of the impact of particular types of messages on themselves and those on others without direct media exposure. One concern is that TPE emerges because of the order in which questions about communication effects on others versus self are presented. Specifically, TPE may be a result of a primacy effect in which people are asked to evaluate the influence of media on others before judging that on themselves. This phenomenon has been discounted by studies counterbalancing the order of self–other questions and still revealing findings that support TPE (Tiedge et al., 1991). Additional concerns over TPE as a result of a perceptual contrast (i.e., natural inclination for responses to favor a disparity in self–other effects due to the presentation of back-to-back questions about the effects of media on self vs. others) have also been disconfirmed (Price & Tewksbury, 1996).

Scholars have also questioned the degree to which TPE stems from a psychological distortion (Perloff, 1993, 1999). This debate raises concern about whether people are overestimating the effects of media on others or underestimating media influ- ence on themselves. Overestimation can be explained by schemas associated with the persuasive influence of media on gullible and susceptible audiences, whereas underestimation can be linked to one’s inability to understand their psychological functioning or motivation to boost self-worth via an illusion of invulnerability (Perloff, 1993). Mixed results of the nature of this psychological distortion have emerged when comparing perceptions of media effects with actual opinion change or opinions of equivalent groups. Whereas some evidence supports the notion that people accurately report the effects of mass media on themselves while overestimating their impact on others (Gunther, 1991), scholars have also found support for the underestimation of self-influence (Cohen et al., 1988). Despite these inconsistent patterns, the existence and nature of the distortion of self–other disparities in the estimation of media effects still warrant greater investigation.

Consequences of TPE

According to the behavioral component of the TPE hypothesis, the bias in the assess- ment of stronger media influence on others rather than the self should likely induce

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actions, such as support for censorship of media content or change in public policies (Davison, 1983). Although substantial research has focused on the perceptual aspect of TPE, the consequences that result from self–other discrepancies in perceived media effects have received less attention (Paul et al., 2000; Sun et al., 2008). Among the studies that have examined implications of TPE, most yield support for TPE in propelling the restriction of media messages that are considered to produce harmful outcomes (e.g., pornography, violence, and sexual content on television, rap music, and advertisements promoting cigarettes, alcohol, and gambling, with mixed results for news and political content [Perloff, 1999]). Recently, scholars have found support for corrective actions as behavioral outcomes of TPE in the context of social media (Golan & Lim, 2016). Specifically, the perception of others as being vulnerable to the influence of ISIS (Islamic State in Iraq and Syria) online recruitment was positively associated with people’s like- lihood to participate in social media activism or corrective behaviors, such as sharing anti-ISIS content on their social media pages.

In addition to supporting the link between third-person perception and restrictive or corrective actions, research has also examined the effects of TPE on people’s willingness to publicly express their opinions and perceptions of public opinion (Perloff, 1999). The latter implication is known as the persuasive press inference, which suggests that individuals make evaluations about public opinion based on their judgment of news coverage and the assumptions underlying the persuasive impact of that coverage on others (Gunther, 1998).

While much of TPE research is survey based, concerns over the causal order of TPE impacting behavioral outcomes have been raised (Perloff, 1999). It is reasonable that one’s motivation to support government censorship of media materials due to strong apprehensions about the influence of such messages on other people causes TPE, instead of the reverse. Furthermore, in studies that have examined behavioral outcomes of TPE, actual behaviors were not measured, but rather the tendency to exhibit these behaviors was assessed.

Due to theoretical and methodological limitations surrounding the behavioral com- ponent of TPE and the scarcity of behavioral investigations (Perloff, 1999), it has not been feasible for researchers to conduct a meta-analysis to capture the average effect size of the behavioral outcomes of third-person perception (Paul et al., 2000; Sun et al., 2008). Theoretical advances and more comprehensive empirical examination of the behavioral implications of TPE are necessary before a meta-analytical review of these outcomes is possible.

SEE ALSO: Hostile Media Effect; Influence of Presumed Media Influence; Question Wording and Item Formulation; Social Comparison Theory; Spiral of Silence; Survey Methods, Traditional, and Public Opinion Polling

References

Cohen, J., Mutz, D., Price, V., & Gunther, A. C. (1988). Perceived impact on defamation: An experiment on third-person effects. Public Opinion Quarterly, 52(2), 161–173. doi:10.1086/ 269092

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Davison, W. P. (1983). The third-person effect in communication. Public Opinion Quarterly, 47(1), 1–15. doi:10.1086/268763

Duck, J. M., & Mullin, B. (1995). The perceived impact of the mass media: Reconsidering the third-person effect. European Journal of Social Psychology, 25(1), 77–93. doi:10.1002/ ejsp.2420250107

Golan, G. J., & Lim, J. S. (2016). Third-person effect of ISIS’s recruitment propaganda: Online political self-efficacy and social media activism. International Journal of Communication, 10, 4681–4701. doi:1932–8036/20160005

Gunther, A. C. (1991). What we think others think: Cause and consequence in the third-person effect. Communication Research, 18, 355–372. doi:10.1177/009365091018003004

Gunther, A. C. (1998). The persuasive press inference: Effects of mass media on perceived public opinion. Communication Research, 25(5), 486–504. doi:10.1177/009365098025005002

Gunther, A. C., & Mundy, P. (1993). Biased optimism and the third-person effect. Journalism Quarterly, 70(1), 58–67. doi:10.1177/107769909307000107

Heider, F. (1958). The psychology of interpersonal relations. New York, NY: Wiley. McLeod, D. M., Eveland, W. P., Jr., & Nathanson, A. I. (1997). Support for censorship of misog-

ynic rap lyrics: An analysis of the third-person effect. Communication Research, 24, 153–174. doi:10.1177/009365097024002003

Paul, B., Salwen, M. B., & Dupagne, M. (2000). The third-person effect: A meta-analysis of the perceptual hypothesis. Mass Communication and Society, 3(1), 57–85. doi:10.1207/ S15327825MCS0301_04

Perloff, R. M. (1993). Third-person effect research 1983–1992: A review and synthesis. Interna- tional Journal of Public Opinion Research, 5(2), 167–184. doi:10.1093/ijpor/5.2.167

Perloff, R. M. (1999). The third person effect: A critical review and synthesis. Media Psychology, 1(4), 353–378. doi:10.1207/s1532785xmep0104_4

Price, V., & Tewksbury, D. (1996). Measuring the third-person effect of news: The impact of question order, contrast and knowledge. International Journal of Public Opinion Research, 8(2), 120–141. doi:10.1093/ijpor/8.2.120

Schweisberger, V., Billinson, J., & Chock, T. M. (2014). Facebook, the third-person effect, and the differential impact hypothesis. Journal of Computer-Mediated Communication, 19(3), 403–413. doi:10.1111/jcc4.12061

Sun, Y., Pan, Z., & Shen, L. (2008). Understanding the third-person perception: Evidence from a meta-analysis. Journal of Communication, 58(2), 280–300. doi:10.1111/j.1460– 2466.2008.00385.x

Tiedge, J. T., Silverblatt, A., Havice, M. J., & Rosenfeld, R. (1991). Discrepancy between perceived first-person and perceived third-person mass media effects. Journalism Quarterly, 68(1–2), 141–154. doi:10.1177/107769909106800115

Tsay-Vogel, M. (2016). Me versus them: Third-person effects among Facebook users. New Media & Society, 18(9), 1956–1972. doi:10.1177/1461444815573476

Vallone, R. P., Ross, L., & Lepper, M. R. (1985). The hostile media phenomenon: Biased percep- tion and perceptions of media bias in coverage of the Beirut massacre. Journal of Personality and Social Psychology, 49, 577–588. doi:10.1037/0022–3514.49.3.577

Further reading

Duck, J. M., Terry, D. J., & Hogg, M. A. (1995). The perceived influence of AIDS advertising: Third-person effects in the context of positive media content. Basic and Applied Social Psy- chology, 17(3), 305–325. doi:10.1207/s15324834basp1703_2

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Eveland W. P., Nathanson, A. I., Detenber, B. H., & McLeod, D. M. (1999). Rethinking the social distance corollary. Perceived likelihood of exposure and the third-person perception. Com- munication Research, 26(3), 275–302. doi:10.1177/009365099026003001

Lo, V., & Wei, R. (2002). Third-person effect, gender, and pornography on the Internet. Journal of Broadcasting & Electronic Media, 46(1), 13–33. doi:10.1207/s15506878jobem4601_2

McLeod, D. M., Detenber, B. H., & Eveland, W. P., Jr. (2006). Behind the third-person effect: Dif- ferentiating perceptual processes for self and other. Journal of Communication, 51(4), 678–695. doi:10.1111/j.1460–2466.2001.tb02902.x

Mutz, D. C. (1989). The influence of perceptions of media influence: Third person effects and the public expression of opinions. International Journal of Public Opinion Research, 1(1), 3–23. doi:10.1093/ijpor/1.1.3

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Perloff, R. M. (1989). Ego-involvement and the third person effect of televised news coverage. Communication Research, 16(2), 236–262. doi:10.1177/009365089016002004

Perloff, R. M. (2002). The third-person effect. In J. Bryant & D. Zillmann (Eds.), Media effects: Advances in theory and research (2nd ed., pp. 489–506). Mahwah, NJ: Erlbaum.

Price, V., Huang, L. N., & Tewksbury, D. (1997). Third-person effects of news coverage: Ori- entations toward media. Journalism & Mass Communication Quarterly, 74(3), 525–540. doi:10.1177/107769909707400307

Rojas, H., Shah, D. V., & Faber, R. J. (1996). For the good of others: Censorship and the third-person effect. International Journal of Public Opinion Research, 8(2), 164–186. doi:10.1093/ijpor/8.2.163

Salwen, M. B. (1998). Perceptions of media influence and support for censorship: The third-person effect in the 1996 presidential election. Communication Research, 25(3), 259–285. doi:10.1177/009365098025003001

Mina Tsay-Vogel is Associate Professor in the Department of Mass Communication, Advertising & Public Relations and Co-director of the Communication Research Center at Boston University. Her research specialization is in media effects with particular emphasis on the psychological and social influence of entertainment and new media on emotion, cognition, and behavior. Her work has appeared in journals such as New Media & Society, Human Communication Research, Journal of Broad- casting & Electronic Media, Communication Monographs, Mass Communication and Society, Journal of Media Psychology, and Psychology of Popular Media Culture, among others.

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Media Consumption and Perceptions of Social Reality: Effects and Underlying

Processes

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4

MEDIA CONSUMPTION AND PERCEPTIONS OF SOCIAL REALITY

Effects and underlying processes

L. J. Shrum University of Texas at San Antonio

Don’t come to television for the truth. TV’s a goddamned amusement park. We’ll tell you the good guys always win. We’ll tell you nobody ever gets cancer at Archie Bunker’s house. We’ll tell you any shit you want to hear.

Paraphrasing Howard Beale, Paddy Chayefsky’s character in Network (Chayefsky, 1976).

I opened this chapter in the second edition of Bryant and Zillmann’s Media Effects series with the same quote. I retained it for this updated volume because it still rings true, despite some significant changes in the media landscape. Although in the movie it is unclear whether his words were those of a madman or a sage, few would be likely to question Howard Beale’s claim that television presents a distorted view of reality. Cer- tainly, one can argue that aspects of media content, format, and presentation have changed significantly in just the last few years, with a rise in so-called “reality program- ming,” made popular by the initial success of programs such as Survivor, and more recently by programs such as American Idol. Yet charges such as scripting of outcomes of competitions, selection of contestants based on audience appeal, and product placements have undermined the claim that these programs present the world as it really is.

But even if most people do not question the premise that typical television fare distorts reality, what they do question is if the distortion has any effect, and if so, why and how. These interrelated questions about the why and how of media effects lie at the heart of scholarly debates and critiques of media effects research. Over the past few decades, there have been two persistent criticisms. One is that the evidence accumulated to date has provided little indication of sizable media effects on viewers’ thoughts, feelings, or actions, in spite of a generally held “myth of massive media impact” by many researchers (McGuire, 1986). The second criticism is that it has for the most part lacked any focus on explanatory mechanisms. That is, media effects research has been primarily concerned with relations between input variables (e.g., media information and its characteristics) and output variables (e.g., attitudes, beliefs, behavior), with little con- sideration of the cognitive processes that might mediate these relations (Hawkins & Pingree, 1990; Reeves, Chaffee, & Tims, 1982; see also Wyer, 1980).

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Although the purpose of this chapter is to address the criticism of the lack of a cognitive process explanation for media effects, the two criticisms just noted are not independent. One of the useful features of process explanations is that models are developed that specify both moderating and mediating variables. McGuire (1986) notes in his review that even though research to date has shown remarkably small media effects, there are a number of possibilities that may ultimately allow for the “salvaging” of the massive effects notion. In particular, he notes that small main effects may be obscured by mes- sages having different effects on different groups or as a function of different situations (moderators) and by focusing on direct effects at the expense of indirect ones (medi- ators). Thus, the development of cognitive process models for media effects has the potential to uncover new relations as well as make sense out of old ones.

The development of cognitive process models that can explain media effects has other advantages as well. For one, it has the potential to increase internal validity, or the extent to which we are confident that we are observing a true causal effect and not one that is spurious (Hawkins & Pingree, 1990), another common criticism of many media effects studies (see Hirsch, 1980; McGuire, 1986). A process model should provide clear links between the stimulus (e.g., media consumption) and the response (e.g., beliefs, behavior), and each link in the model should represent a testable proposition to be empirically verified. If these links stand on solid theoretical foundations and are empirically verified, then threats to internal validity such as spuriousness and reverse causality are rendered less plausible, as the threats would presumably have to occur at each stage. Another advantage is that process models may potentially address conflicting findings in previous research. A process model should provide boundary conditions for the effect; that is, a specification of the conditions under which the effect does not hold. To the extent that these boundary conditions are related to aspects of inconsistencies in previous research, disparate findings may be reconciled.

Given these advantages of a focus on process, my goals for this chapter are two-fold: 1) to discuss some of the general underlying principles in social cognition research that have particular implications for media effects, with reference to relevant media effects research that exemplify these principles; and 2) discuss research to date that has focused on explicating the underlying processes of certain media effects such as cultivation (see chapter 4).

SOCIAL COGNITION AND MEDIA EFFECTS

Social cognition can best be described as an orientation toward the cognitive processes that occur in social situations (Reeves, Chaffee, & Tims, 1982). To be more specific, social cognition research attempts to open the “black box” that operates between a stimulus (e.g., information) and a response (e.g., a judgment) (Wyer, 1980), and has its focus on the cognitive processes that mediate the relations between social information and judgment (Wyer & Srull, 1989).

Social cognition research has not only had a profound effect on the field of social psychology, but on numerous other fields as well (e.g., marketing communications, see chapter 19; political communications, see chapter 12; cross-cultural psychology; organ- izational behavior). Given the maturity of the field, there are a number of models that have been developed to account for how people acquire, store, and use social informa- tion, the most complete of which is that provided by Wyer and Srull (1989; but see Wyer, 2004; Wyer & Radvansky, 1999, for revisions of this model).1 Even though the

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various theories differ in important ways, they all share some basic underlying principles (Carlston & Smith, 1996; Wyer, 1980).

For the purposes of this discussion, there are two important and interrelated prin- ciples underlying social cognition research.2 Principle 1 (Heuristic/Sufficiency Principle) concerns what information is retrieved in the course of constructing a judgment. This principle states that when people construct judgments, they typically do not search memory for all information that is relevant to the judgment, but instead retrieve only a small subset of the information available. Moreover, the criterion for what is retrieved is “sufficiency”: That is, only the information that is sufficient to construct the judg- ment is retrieved, and the determinants of sufficiency are related to concepts such as motivation and ability to process information (Wyer & Srull, 1989; see also Chaiken, Liberman, & Eagly, 1989, for a similar perspective on attitude judgments).

Principle 2 (Accessibility Principle) concerns the role of the accessibility of informa- tion in the construction of judgments. In its simplest form, the principle states that the information that comes most readily to mind will be the information that comprises the small subset of available information that is retrieved, and in turn, is the informa- tion that is most likely to be used in constructing a judgment (Carlston & Smith, 1996; Higgins, 1996; Wyer, 1980).

Taken together, these two principles have important implications for explaining media effects. These implications revolve around the determinants and consequences of accessibility.

Determinants of Accessibility

There are a number of factors that may influence the ease with which something is recalled. Although a detailed discussion of these factors is beyond the scope of this chapter (for more extensive reviews, see Higgins, 1996; Higgins & King, 1981), certain ones have implications for media effects (Shrum, 1995). These factors are the frequency of construct activation, recency of construct activation, vividness of a construct, and relations with accessible constructs.

Frequency and Recency of Activation

Constructs that are frequently activated tend to be easily recalled (Higgins & King, 1981). This general finding has been shown both in studies of word recall and recogni- tion (Paivio, 1971) as well as trait concepts (Wyer & Srull, 1980). Moreover, if activated frequently enough, particular constructs may become chronically accessible (for a review, see Higgins, 1996) such that they are spontaneously activated under many different situations. The same general relation holds for recency of activation: The more recently a construct has been activated, the easier it is to recall (Higgins, Rholes, & Jones, 1977; Wyer & Srull, 1980). However, research suggests that the effect of recency of activation on accessibility is relatively transitory, with frequency effects tending to dominate after a short period of time (Higgins, Bargh, & Lombardi, 1985; Wyer & Radvansky, 1999; Wyer, 2004).

This general relation of frequency and recency with accessibility has implications for potential media effects. For example, cultivation theory rests on the premise that the fre- quency of television viewing has effects on the beliefs of viewers. In terms of frequency of activation, heavier viewers should more frequently activate constructs portrayed on television than light viewers, particularly if those constructs tend to be portrayed more

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heavily on television than in real life. Moreover, heavy viewers have a higher probability of having viewed recently than light viewers, thus accessibility may be enhanced for heavy viewers through the recency of viewing.

Vividness

More vivid constructs are more easily activated from memory than less vivid ones (Higgins & King, 1981; Nisbett & Ross, 1980; Paivio, 1971). Like frequency and recency, vividness has particular applicability to media effects. It seems reasonable to think that television portrayals of particular actions or events may be more vivid than real world experiences, given the drama-enhancing goal of entertainment. Examples might include a fist-fight, an execution, family conflict, a natural disaster, military conflict, and so forth.

Vividness may also play a role in news reports. As Zillmann and colleagues have noted (for a review, see Zillmann, 2002), news reports often convey information in the form of case studies or extreme examples. Such a bias in favor of vivid examples over precise but pallid statistical information may make those examples relatively easy to remember.

Relations with Accessible Constructs

As the accessibility of a particular construct increases, so does the accessibility of a closely related construct. This concept is consistent with the associative network/ spreading activation model of memory made popular in cognitive psychology as a means of explaining the interconnectedness of knowledge (Collins & Loftus, 1975). This model holds that constructs are stored in memory in the form of nodes, and links are formed between the nodes. When a particular node (stored construct) is activated, other constructs will also be activated to the extent that they are related to that node.

It seems likely that the relation between accessible constructs may have implications for media effects. One of the attributes of media portrayals, particularly on television programs and films, is the relatively consistent and formulaic way in which particular concepts (e.g., anger and aggression, particular classes of people, etc.) are portrayed. These portrayals may provide “scripts” (Schank & Abelson, 1977) or “situation models” (Wyer, 2004; see also chapter 6) for what represents a construct and how to react to it. Given the relations between accessible constructs, the activation of a particular con- struct (e.g., aggression, anger) may similarly activate scripts for behavior that are closely related to these constructs (e.g., crime, violence).

In summary, television consumption—whether it is the frequency, recency, or the con- tent features of viewing—may serve to enhance the accessibility of particular constructs. This “media effect” is an example of the interrelatedness of the Heuristic/Sufficiency Principle and the Accessibility Principle: Media consumption enhances accessibility, which influences the information that becomes a part of that small subset of available information.

Consequences of Accessibility

Simply demonstrating that media information may play a role in enhancing the accessi- bility of particular constructs is not sufficient to provide an explanation of media effects. It is also necessary to show that enhanced accessibility in turn produces effects that are consistent with the media effects literature.

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The consequences of accessibility are directly related to Principle 2: The information that is most accessible is most likely to be used to construct a judgment. Moreover, the way in which the most accessible information is used is a function of the type of judgment that is made.

Judgments about Persons

One of the more consistent findings in the social cognition literature is that when people make judgments about other persons, they tend to use the constructs that are most readily accessible from memory (Accessibility Principle). In the now-classic prim- ing studies (e.g., Higgins et al., 1977; Srull & Wyer, 1979), when participants were required to form trait judgments based on the ambiguous behaviors of a target person, they tended to use the trait concepts that had been primed to interpret those ambiguous behaviors (for a review, see Higgins, 1996; see also chapter 6). The interpretations influ- enced participants’ judgments about the target’s behaviors (e.g., reckless, persistent) as well as judgments about how much they liked the target. These results have been repli- cated numerous times, even under conditions of subliminal presentation of the prime (Bargh & Pietromonaco, 1982).

Attitude and Belief Judgments

Evaluations of an object may be constructed from beliefs that are most accessible (Fishbein & Ajzen, 1975). In the Fishbein and Ajzen model, attitude construction is a function of particular beliefs and evaluations of those beliefs. It follows, then, that which beliefs are put into the attitude construction equation may be a function of which beliefs are most accessible at the moment. In a series of experiments, Wyer and col- leagues (Henninger & Wyer, 1976; Wyer & Hartwick, 1984) examined the relation between accessible beliefs and evaluative judgments. In those experiments, which tested aspects of the Socratic effect (thinking about logically related beliefs makes those beliefs more consistent; McGuire, 1960), they showed that the accessibility of beliefs relating to premises increased the consistency between the beliefs in the premises and beliefs in the conclusions.

Judgments of Set-size and Probability

Set-size judgments pertain to judgments of the extent to which a particular category occurs within a larger, superordinate category (e.g., the percentage of women [sub- ordinate category] in the U.S. population [superordinate category]; Manis, Shedler, Jonides, & Nelson, 1993). Probability judgments pertain to estimates of likelihood. A finding that has been documented consistently is the relation between the accessibil- ity of a construct and judgments of set-size and probability (Sherman & Corty, 1984). In their seminal work on the availability heuristic, Tversky and Kahneman (1973) demonstrated that people tend to infer the frequency of a class or the probability of occurrence on the ease with which a relevant example can be recalled. For example, participants in one experiment estimated that words beginning with k occur more frequently in the English language than words having k as the third letter, even though the opposite is true. Presumably, words beginning with k are easier to recall because of how words tend to be organized in memory (by initial letters). Later work also identified a related heuristic, the simulation heuristic, in which people judge frequency

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and probability on the ease with which an example can be imagined (Kahneman & Tversky, 1982)

Media Effects and Accessibility Consequences

The three types of judgments just discussed and their relation to accessibility by no means exhausts the discussion of the types of judgments that have been shown to be influenced by the accessibility of information (Higgins & King, 1981). Rather, those judgments are singled out because of their relevance to the types of judgments that are often used in media effects studies.

Effects of News Reports on Issue and Person Perceptions

One domain in which information accessibility has been implicated is that of how information about particular issues presented in news reports affects judgments about those issues (e.g., attitudes, likelihood estimates). For example, research by Zillmann and colleagues has shown that information presented in the form of exemplars (e.g., case studies, vivid examples, etc.) tends to influence judgments to a greater degree than does more accurate but pallid base rate information. This general finding has been replicated for a variety of exemplar conditions, including manipulating the proportion of exem- plars that are consistent with a story’s focus, the degree of exaggeration of the exem- plars, and the emotionality of the exemplars (for a review, see Zillmann, 2002). Other research has produced similar findings, with Iyengar (1990) reporting effects of the presence (vs. absence) of exemplars and Brosius and Bathelt (1994) finding an effect of number of exemplars on issue perceptions. Most of this research has conceptualized the results in terms of accessibility and the use of heuristics: The more vivid or fre- quent examples are easier to remember than less vivid or infrequent examples, and thus tend to be used to construct judgments.

Iyengar and colleagues have also argued that media coverage can create an accessibil- ity bias through its frequency of coverage of particular issues. In turn, this accessibility bias has been shown to influence a number of judgments, including issue salience, evaluations of politicians’ performances, and voting behavior (Iyengar, 1990). Findings reported by Lichtenstein, Slovic, Fischhoff, Layman, and Combs (1978) have also been conceptualized in terms of accessibility and the application of the availability heuristic. They observed that roughly 80% of study participants estimated that death due to an accident is more likely to occur than death due to a stroke, even though strokes cause about 85% more deaths than accidents. Lichtenstein et al. suggest that examples of accidental deaths are easier to recall than examples of death by stroke, and at least partially because the former tend to be reported more than the latter in the media.

Effects of Television Viewing on Social Perceptions

Another media effects domain in which accessibility has been used as an explanatory variable is in the relationship between television viewing and perceptions of social real- ity. This domain differs from news reports in that it considers all types of television viewing (e.g., fictional portrayals such as soap operas, action/adventure, dramas, situation comedies, etc.) rather than just news programs.

The results of a number of studies can be conceptualized in terms of the enhanced accessibility afforded by heavy television viewing, and the subsequent application of

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judgmental heuristics, particularly when the dependent variables involve estimates of frequency of a class or likelihood of occurrence. For example, Bryant, Carveth, and Brown (1981) exposed participants, over a six-week period, to either heavy or light viewing of films depicting crime, and those in the heavy exposure condition saw crime portrayals that featured either just or unjust resolutions. They found that those in the heavy exposure conditions indicated a greater likelihood of being a victim of violence and more fear of victimization than those in the light exposure conditions, regardless of whether the resolutions were just or unjust. As with the other studies just discussed, these results are consistent with predictions made by the availability heuristic: The heavy viewing conditions made examples of crime more accessible than the light view- ing conditions, and this accessibility, or ease of recall, influenced judgments of preva- lence and likelihood of occurrence. Other studies have made this same connection between accessibility as a function of viewing and judgments (cf. Ogles & Hoffner, 1987; Tamborini, Zillmann, & Bryant, 1984).

The concepts of accessibility and the use of heuristics have also been used to explain the effects of sexual portrayals in the media (see chapter 16). Zillmann and Bryant (1982) found that participants who viewed portrayals of explicit sex scenes gave higher estimates of the prevalence of unusual sex practices among the general population, were less likely to object to public display of pornography, and recommended shorter jail sen- tences for a convicted rapist than did participants who viewed films that were not sexually explicit.

Effects of Media Portrayals on Aggression

Although the research just reviewed has focused predominantly on cognitive measures as dependent variables, the concept of accessibility has also been useful in explaining the effects of exposure to media violence on behavior. Berkowitz’s cognitive-neoassociationistic perspective (1984; see also chapter 6) on the effects of violent media consumption posits that frequent viewing of violent media portrayals primes particular constructs (e.g., aggression, hostility) and thus makes these constructs more likely to be used in behavioral decisions as well as judgments about others. Note that this notion is very similar to the original trait priming studies that were discussed earlier: A particular trait concept is made accessible and thus is used disproportionately as a basis for subsequent judgments.

The relation between the activation of a construct such as aggression through media portrayals and the accessibility of aggression-related constructs has been demonstrated in several studies. For example, Bushman and Geen (1990) showed that viewing violent films elicited more aggressive thoughts than viewing nonviolent films. Berkowitz, Parker, and West (cited in Berkowitz, 1973) produced similar findings, showing that children who read a war comic book were more likely to select words with aggressive meanings than children who read a neutral comic book. Other studies have made the connection between activation (and presumed enhanced accessibility) of aggression constructs and subsequent judgments. Carver, Ganellen, Froming, and Chambers (1983) found that people who viewed a brief film portraying a hostile interaction between a business executive and his secretary perceived more hostility in an ambiguous target person than did people who viewed a non-hostile portrayal, and Berkowitz (1970) showed that similar effects of aggressive portrayals on judgments can be observed even when the aggressive behavior is in the form of comedy.

It is also worth noting that what is primed does not necessarily have to be directly

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related to an imminent judgment, but may only have to share similar features to a judgment situation. Recall that one of the antecedents of a construct’s accessibility is its relation to other accessible constructs. This notion is useful in explaining possible media effects in which the type of aggressive action viewers observe in media content is only tangentially related to the type of aggressive action taken by viewers, a pattern of results that theories of learning, imitation, or “modeling” (Bandura, 1973; see chapter 7) have difficulty addressing (Berkowitz, 1984). In fact, as Berkowitz notes, the behavioral aggression measures that are used in studies are often quite different from the aggres- sion observed in the media portrayals (whether they are experiments or field studies). For example, Phillips (1983) presented correlational data that showed that heavy media coverage of heavyweight championship boxing matches tended to be followed by an increase in homicides in the U.S. on certain days within a 10-day period following the fight (but see Freedman, 1984, for a criticism of this study). Similar aggression-related effects of viewing boxing matches have been reported in experimental studies as well (Turner & Berkowitz, 1972).

Indirect vs. Direct Investigations of Cognitive Processes

The research just presented is suggestive of the role of accessibility as a cognitive mediator of media effects. However, much of the evidence is still indirect in that many of the studies fall short of actually investigating the processes themselves, but rather offer process explanations for the obtained results. Exceptions to this generalization include Zillmann’s work on excitation-transfer theory (Zillmann, 1983) and Berkowitz’s cognitive-neoassociationistic perspective (Berkowitz, 1984).

In the following section, I discuss a series of studies that directly investigates such potential cognitive processes. The results of these studies are then used as the basis for the development of cognitive processing models that can account for a particular media effect, the cultivation effect. This model builds on the general principles discussed earlier (heuristic/sufficiency and accessibility) that underlie social cognition research.

PSYCHOLOGICAL PROCESSES UNDERLYING CULTIVATION EFFECTS

One area of media effects research that has generated considerable controversy is the research on the cultivation effect (see chapter 4). For the purposes of this discussion, a cultivation effect is defined as a positive relation between frequency of television viewing and social perceptions that are congruent with the world as it is portrayed on television, with the presumption that television viewing is the causal factor. Although considerable evidence has accumulated that supports the existence of at least a small-sized cultivation effect (Morgan & Shanahan, 1996), other researchers have challenged the validity of the effect. Some research suggests that the relationship between viewing and perceptions is not causal, but rather a spurious one resulting from third variable influences (e.g., direct experience, available time to view) on both television viewing and social perceptions (Doob & Macdonald, 1979; Hirsch, 1980; Hughes, 1980; Wober & Gunter, 1988). Other research suggests that the causal relation between viewing and social perceptions may be reversed; that is, aspects of the individual (including pre-existing social perceptions) may influence the amount and content of viewing (Zillmann, 1980).

As noted earlier, one of the advantages of developing a cognitive process model of

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media effects is that it has the potential to render implausible certain alternative explan- ations for the effect (e.g., spuriousness, reverse causality, etc.). Two caveats should be noted, however. First, rendering a particular alternative explanation implausible in a study merely means that the explanation cannot completely account for a particular pattern of results; it does not mean that the alternative explanation may not be operat- ing simultaneously but independent of other effects. Second, the power of a process model is in the cumulative effect of a pattern of results, not a focus on a single study. Thus, even though alternative explanations may be possible for any one study, in the interest of parsimony, the alternative explanations should address the entire pattern of results to be an effective challenge.

In the following sections, I describe models that attempt to explain the underlying processes of cultivation effects. These models are grounded in the theories of social cognition that were described earlier. The models incorporate advances that have been made over the last few years and thus represent refinements of the model presented in the previous edition of Media Effects (Shrum, 2002). In fact, the models are now mul- tiple ones that separately explain the processes underlying different types of cultivation effects, in particular what are generally referred to as effects on first-order (e.g., esti- mates of prevalence, probability) and second-order (attitudes, values, beliefs) judg- ments. Recent findings suggest that the processes by which television viewing influences judgments depend on the type of judgment that is made (Shrum, 2004, 2007; Shrum, Burroughs, & Rindfleisch, 2004).

PROCESS MODEL FOR FIRST-ORDER CULTIVATION EFFECTS

The process model for first-order effects, which has been referred to as the heuristic processing model of cultivation effects (Shrum, 2002; Shrum, Wyer, & O’Guinn, 1998) and the accessibility model (Shrum, 2007a), starts with two general propositions that are based on the principles of heuristic/sufficiency and accessibility. The first general proposition is that television viewing enhances construct accessibility. As discussed earlier, aspects of television viewing may plausibly be related to the accessibility of constructs encountered in typical television fare. The second general proposition is that the social perceptions that serve as indicators of a cultivation effect are memory-based judgments that are constructed through heuristic processing. Specifically, rather than constructing judgments through an extensive search of memory for all available rele- vant information (systematic processing), only a subset of relevant information is retrieved, and specifically, the information retrieved is that which is most accessible from memory. A corollary of this second general proposition is that, at least for cases in which the judgments pertain to perceptions of frequency of a class (set-size) or likelihood of occurrence, judgments are constructed through the application of the availability heuristic; that is, the magnitude of the judgments is positively related to the ease with which an example can be brought to mind (Tversky & Kahneman, 1973).

Testable Propositions

These general propositions can themselves be used to generate testable propositions regarding the relation between television viewing and social perceptions and the cognitive mechanisms that may mediate this relation.

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Proposition 1: Television Viewing Influences Accessibility

Proposition 1 is a necessary condition for testing whether the availability heuristic can explain cultivation effects. This proposition was initially tested by operationalizing accessibility as the speed with which judgments could be constructed. Shrum and O’Guinn (1993) had participants provide prevalence and likelihood estimates of con- structs frequently portrayed on television (e.g., crime, prostitution, etc.) and measured the time it took participants to answer each question. If television information was more accessible for heavy viewers than for light viewers, heavy viewers should not only provide higher estimates than light viewers (a cultivation effect), but should also con- struct their judgments faster (an accessibility effect). The results of the study confirmed these hypotheses, even when controlling for individual baseline latencies, grade point average, and use of other media. These same general relations have been replicated using a variety of dependent variables, different operationalizations of television viewing, and multiple control variables (cf. O’Guinn & Shrum, 1997; Shrum, 1996; Shrum, O’Guinn, Semenik, & Faber, 1991).

Although the initial findings linking speed of constructing judgments to judgment magnitude were consistent with theory, speed of judgment is a relatively indirect way of measuring exemplar accessibility. Recent findings provide more direct evidence that television influences accessibility. Busselle and Shrum (2003) had participants recall examples of various constructs, some of which are portrayed frequently in television programs (trial, murder, highway accident), and rate the ease of that recall experience. Consistent with predictions, media examples were more frequently recalled for con- structs that are portrayed often in television programs but infrequently experienced personally, whereas personal experiences were more frequently retrieved for events occurring often in real life, regardless of their frequency of occurrence in the media (highway accidents, dates). More important, rated ease of recall of the examples was positively related to frequency of television viewing, but only for the viewing of televi- sion programs in which the events were frequently portrayed (e.g., soap operas, dramas, news). Rated ease of recall was unrelated to viewing frequency for program categories in which the constructs were infrequently portrayed (e.g., comedies, sports) and for con- structs in which personal experience (direct or indirect) was high (e.g., date, highway accident). These results not only bolster the proposition that television viewing increases accessibility, but also are consistent with research showing the direct experience with constructs enhances their accessibility. It is also consistent with research that shows that it is the subjective ease of recall (the metacognitive experience) that influences judgments, not frequency of recall per se (Schwarz, Bless, Strack, Klumpp, Rittenauer-Schatka, & Simons, 1991; Schwarz, Song, & Xu, in press).

Proposition 2: Accessibility Mediates the Cultivation Effect

Proposition 1 (viewing influences accessibility) is a necessary but not sufficient condi- tion to implicate the availability heuristic as an explanation for cultivation effects. It is also necessary to demonstrate that accessibility mediates the relation between level of viewing and magnitude of judgments (Manis et al., 1993); that is, it is also necessary to demonstrate that the enhanced accessibility leads to higher estimates. Otherwise, it could be argued that television viewing impacts accessibility and the magnitude of the judgments independently.

Some indirect evidence of the mediating role of accessibility was provided by Shrum

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and O’Guinn (1993). When accessibility (speed of response) was controlled, the cultiva- tion effect was for the most part reduced to nonsignificance. More direct evidence of mediation was provided by Shrum (1996). Path analyses were used to demonstrate that level of television viewing was related to accessibility (again, operationalized as response latencies), which in turn was related to the magnitude of the estimates. However, the path analyses also revealed that the mediation was only a partial one: Television viewing still had a direct effect on the magnitude of the estimates even when the influence of accessibility was controlled.

Busselle (2001) also provided evidence of the mediating role of accessibility by manipulating the conditions under which the prevalence estimates for particular con- structs (e.g., a shooting) were constructed. Some participants provided their prevalence estimates before recalling an example of the construct (judgment-first condition) whereas other participants recalled an example before providing their estimates (recall- first condition). Level of television viewing was expected to make an example easier to recall in the judgment-first condition, whereas recalling an example before judgment was expected to make an example equally accessible for all participants, regardless of television viewing level. The results confirmed these expectations.

Proposition 3: Television Exemplars Are Not Discounted

An implicit assumption in the notion that the availability heuristic can explain cultiva- tion effects is that the examples that are retrieved and used as a basis for judgment are considered applicable to the judgment. This is an important assumption because research has shown that accessibility effects typically obtain only when this condition is met (Higgins, 1996). Moreover, the judged applicability of the construct is a function of the overlap between its attended features and the features of the judgment.

In terms of the cultivation effect, the recalled construct would presumably be a tele- vision example. However, it is counterintuitive that people would perceive a television example (e.g., doctor, lawyer) as applicable to a judgment about its real-world preva- lence. If they do not perceive the example as relevant, alternative information would be retrieved and used as a basis for judgment (Higgins, 1996; Higgins & Brendl, 1995; Shapiro & Lang, 1991).

One way in which a television example could be perceived as relevant to a real-world judgment is if people generally do not consider the source of the example they retrieve in the course of judgment construction. Note that perceived applicability is a function of the overlap between the attended features of the recalled construct and the features of the judgment. It may be that source characteristics of the retrieved construct are not salient features that are attended to, particularly when judgments are made with little effort. This may be a function of either lack of motivation to attend to source features (consistent with low involvement processing; Petty & Cacioppo 1990) or lack of ability to recall source information (consistent with research on errors in source monitoring; Johnson, Hashtroudi, & Lindsay, 1993; Mares, 1996; Shrum, 1997). This process is also consistent with the weighing and balancing mechanism proposed by Shapiro and Lang (1991) to explain cultivation effects (for a review, see Shrum, 2007a).

To test Proposition 3, Shrum, Wyer, and O’Guinn (1998) conducted two experiments in which source characteristics were primed prior to judgments. In the first experiment, the priming events consisted of a source-priming condition, in which participants pro- vided information regarding their television viewing habits prior to providing prevalence and likelihood judgments of crime and occupations; and a relation-priming condition

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in which participants were told that the constructs they would be estimating appeared more often on television than in real life. In a third, no-priming condition, participants provided their estimates prior to providing television viewing information. Analyses revealed that when participants provided estimates under no-priming conditions, a culti- vation effect was noted, but when they provided estimates under either source- or relation-priming conditions, the cultivation effect was eliminated. Follow-up analyses indicated that the estimates of light viewers did not differ as a function of priming con- ditions, but the priming conditions served to bring the estimates of heavy viewers more in line with those of light viewers. This pattern of results can be seen in Figure 4.1.

A second study replicated this pattern of results, and further suggested that the priming conditions induced a source-discounting process (heavy viewers discounted television information to a greater degree than light viewers) rather than an automatic adjustment process (heavy viewers adjusted their estimates downward because they were aware they were heavy viewers, but light viewers saw no need to adjust).

Proposition 4: Motivation to Process Information Moderates the Cultivation Effect

Proposition 4 is based on research showing that there are certain conditions under which heuristic processing (as opposed to systematic processing) is expected to occur (Sherman & Corty, 1984; see chapter 8). If so, then manipulating the types of process- ing in which people engage should have implications for whether a cultivation effect is obtained. To be specific, if people generally process heuristically in the course of con- structing their judgments of prevalence or likelihood of occurrence, then inducing people to process heuristically should produce a cultivation effect that does not differ in magnitude from the cultivation effect obtained when people receive no such manipu- lation. But suppose people are induced to process systematically when constructing their judgments. Compared to heuristic processing, systematic processing is associated

Figure 4.1 Prevalence Estimates as a Function of Priming Condition and Level of TV Viewing. Represents Pattern of Results Across Dependent Variables (see Shrum et al., 1998).

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with the consideration of more information and greater scrutiny of the information that is considered. Systematic processing is used when it is important to determine the validity of information and has been shown to attenuate the effects of heuristics (Chaiken et al., 1989).

Under systematic conditions, it seems likely that the relation between level of viewing and social perceptions would be weakened or eliminated entirely. When people process systematically, they should be more likely to retrieve examples other than simply the first ones that come to mind, should be more likely to scrutinize the retrieved information, and thus should be more likely to ascertain and discount information from unreliable sources such as television programs, than when they process heuristically.

One condition that is related to whether heuristic or systematic processing strategies are adopted is the motivation to process information (Sherman & Corty, 1984). When motivation is high, systematic processing predominates; when motivation is low, heur- istic processing predominates. Moreover, motivation is determined by a number of factors, including level of issue involvement (Petty & Cacioppo, 1990) and level of task involvement (Chaiken & Maheswaran, 1994).

To test Proposition 4, Shrum (2001) manipulated the processing strategies that parti- cipants used to construct their estimates of the prevalence of crime, marital discord, affluence, and certain occupations. Some participants were induced to process sys- tematically via an accuracy motivation/task importance manipulation (Chaiken & Maheswaran, 1994), others were induced to process heuristically by asking them to give the first answer that came to mind, and a third (control) group received no manipula- tion, but were simply instructed to provide their estimates. Television viewing was then measured after the judgments were made. The results were as expected. Both the con- trol group and the heuristic group produced significant cultivation effects that did not differ from each other, whereas the systematic group showed no cultivation effect. Moreover, the pattern of results was very similar to those obtained by Shrum et al. (1998, Study 1): The estimates of light viewers did not differ as a function of condition, but the systematic condition affected only heavy viewers, bringing their estimates more in line with those of all light viewers, regardless of processing condition. This pattern of results can be seen in Figure 4.2.

Proposition 5: Ability to Process Information Moderates the Cultivation Effect

As with Proposition 4, this proposition is based on the conditions that facilitate or inhibit the use of systematic or heuristic processing strategies. In addition to motiv- ation to process information, the ability to process information is also associated with processing strategies (Petty & Cacioppo, 1986; Chaiken et al., 1989). One factor that relates to the ability to process information is time pressure (Moore, Hausknecht, & Thamodaran, 1986; Ratneshwar & Chaiken, 1991): the more time pressure, the greater the likelihood of adopting a heuristic processing strategy.

To test Proposition 5, Shrum (2007b) used an experimental procedure that not only tested the proposition but also has implications for data collection methods. The experimental manipulation of time pressure was operationalized as either a mail survey (low time pressure) or a telephone survey (high time pressure) using a general popula- tion random sample. Pretests had indicated that the two data collection methods differed with respect to time pressure but did not differ in terms of respondents’ self-reported level of involvement. The reasoning and predictions for the experiment were similar to

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Shrum (2001). If the cultivation effect is a function of heuristic processing, then larger effects should be noted under conditions that favor more heuristic processing (phone survey) than under conditions that favor less heuristic processing (mail survey). The results confirmed this speculation. Across five composite variables representing percep- tions of societal crime, societal vice (e.g., prevalence of prostitution, drug abuse, etc.), marital discord, affluence, and the prevalence of particular occupations, the magnitude of the effects was significantly larger in the phone survey condition than in the mail survey condition for four of the five measures (as with Shrum, 2001, all but marital discord).

Other evidence also supports the notion that ability to process information has implications for the cultivation effect. Mares (1996) found that people who tend to make particular kinds of source confusions (mistaking fiction for fact) tend to exhibit a larger cultivation effect than those who do not tend to make such confusions. Thus, even in instances in which people may be motivated to process information (see Shrum, 1997), inability to properly process information (in this case, accurately ascertain source characteristics) may facilitate a cultivation effect.

Model Integration

The next step in model development is to integrate the testable propositions, and the implications of their supportive results, into a coherent conceptual framework. This conceptual framework, which is presented in the form of a flow chart in Figure 4.3, specifies a series of links, or steps, which lead from television viewing to the production of a cultivation effect. For the most part, each link (designated by an arrow) represents a testable proposition that has been empirically verified. As the figure indicates, there are in fact a number of ways in which media exposure will not have an effect on judgments (no cultivation effect), but only one way (path) in which a cultivation effect will be produced.

Figure 4.2 Prevalence Estimates as a Function of Processing Condition and Level of TV Viewing. Represents Pattern of Results Across Dependent Variables (see Shrum, 2001).

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Figure 4.3 Flow Diagram of the Heuristic Processing Model of Television Effects. Circles Represent Mental Processes. The Thicker Arrow from Heavy TV to Memory Search Indicates a Greater Contribution to the Search Process.

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In order to present as simple a model as possible, some misleading aspects arise that should be clarified. One of the misleading aspects of Figure 4.3 is that the links (Yes/ No) and the outcomes (Effect/No Effect) are portrayed as dichotomous variables. In fact, it is more accurate to think of each as a continuum, and movement along the con- tinuum has implications for the size of the outcome. For example, rather than interpret- ing the figure as “high motivation to process results in no cultivation effect,” it may be better interpreted as “the higher the motivation to process, the smaller the cultivation effect.”

Implausible Alternative Hypotheses

Although some, if not all, of the studies that have formed the basis of the model have potential alternative explanations, it is difficult for concepts such as spuriousness or reverse causality to account for the general pattern. For example, the initial studies that tested Propositions 1 and 2 (accessibility) were strictly correlational and thus could be explained in terms of either spuriousness or reverse causality. However, these alternative explanations cannot account for the results of the experiments that tested Propositions 3 through 5, particularly the pattern showing that both the experimental manipulations of source priming and of processing strategy produced nearly identical results, with the manipulations reducing estimates of heavy viewers to the equivalent of light viewers, but leaving the estimates of light viewers unaffected.

The consistency of results across the different types of dependent variables also argues against explanations other than a causal effect of television viewing. Consistent results tended to be found for judgments of occupational prevalence (doctors, lawyers, police officers), crime, and affluence (and to a lesser extent, marital discord). Although reverse causality or spuriousness explanations can be used (and often are) to explain the results for any one variable, it is difficult to account for the effects on all variables. Rather, the more parsimonious explanation is that the causal factor is the one that they most have in common: They are constructs over-represented in television portrayals relative to their real-world incidence.

Explaining Small Cultivation Effect Sizes

As mentioned earlier, one of the useful features of a process model for cultivation effects is that it has the potential to reconcile conflicting findings that have been reported. The myriad of paths toward little or no cultivation effect that are shown in Figure 4.3 has the potential to explain some of these conflicts.

Source-priming Explanations

The source-priming manipulation used by Shrum et al. (1998) had participants provide information on how much television they watch prior to providing their prevalence and likelihood estimates. This order of data collection was sufficient to eliminate the culti- vation effect. As Morgan and Shanahan (1996) note, a number of studies that have reported finding no evidence of a cultivation effect either measured television viewing prior to measuring social perceptions or introduced the study as one pertaining to television. Although Morgan and Shanahan’s meta-analysis did not find support for such source-priming as a moderator, their results showed that the effect sizes for the non-source-primed studies tended to be slightly higher than the effect sizes for studies

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in which source was (inadvertently) primed. Thus, it seems possible that the inability to observe cultivation effects in previous studies may have been due to the inadvertent priming of source information.

Note also that it is not necessary to prime source through data collection. Priming simply refers to making a construct more accessible in memory. For some people, par- ticular constructs may be chronically accessible (Higgins, 1996). So for whom might the construct of television, and its potential effects, be particularly accessible? One group may be communications majors, or for that matter, any student who might have had a course that deals with potential effects of television; in other words, people who may often comprise the subject pools that academics (and especially those in communications departments) use in their studies. Thus, it is plausible that null findings for cultivation effects in some studies may be due to the special characteristics of the sample.

Involvement Explanations

A number of factors may relate to level of involvement with constructing judgments. For example, level of involvement may differ as a function of sample composition. College students may be less intimidated than older adults or younger people by the university setting that may be used to collect data (Shrum, 1997). Alternatively, indi- vidual differences may exist that relate to involvement, such as interest in the topic (e.g., crime by those with direct experience with it) or general interest in solving problems (e.g., those high on need for cognition; Cacioppo & Petty, 1982). Involvement may also vary as a function of data collection method. Data that are collected through anonym- ous questionnaires may induce less accuracy motivation than data collected in, say, personal interviews (Shrum, 1997, 2001).

Time Pressure Explanations

Shrum (2007b) showed that simple differences in data collection methods, presumably related to differences in time pressure, can have a significant impact on the magnitude of cultivation effects. In that study, the difference was whether the data were collected via a phone or mail survey. Other situations can contribute to time pressure, whether real or imagined. Although not entirely independent of involvement, it has been my experience that a majority of the college students that comprise subject pools seem to be in quite a hurry to finish their task and leave. College students may be less interested in answering survey questions or in more of a hurry to complete the survey than older adults. If so, they would be more likely to use heuristics in their judgments, and thus should show a larger cultivation effect. There is actually some evidence that supports this possibility. Unreported data from Morgan and Shanahan’s (1996) meta-analysis (reported in Shrum, 2007b) showed that college student samples produced markedly larger effect sizes than general population adult samples despite their lower incidence of television viewing.

Summary

The process model for first-order cultivation effects just discussed has provided robust findings that help explain the processes underlying the effect. This process explanation provides much needed support for the validity of the effect by explicating and testing the mediating processes. The model also specifies important boundary conditions or

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moderating effects. Cultivation effects tend to be strongest when motivation or ability to process information is low, and the effects tend to be reduced or eliminated when motivation or ability to process is high.

However, first-order effects are only a part of the picture. Although process research to date has tended to focus on first-order effects (perhaps because they have been shown to be more reliable; Hawkins & Pingree, 1982), second-order effects that look at televi- sion’s influence on values, attitudes, and beliefs are arguably more important. As will be noted in the following section, first-order judgments are fairly uncommon and seldom spontaneous, usually coming only at the behest of one of life’s researchers (Hastie & Park, 1986), whereas second-order judgments are typically spontaneous, everyday judgments that influence many aspects of our lives. In the next section, I provide a rough model of the processes underlying second-order cultivation effects and discuss evidence that supports this model.

PROCESS MODEL FOR SECOND-ORDER CULTIVATION EFFECTS

As just noted, second-order judgments differ from first-order judgments in some important ways, including how they are constructed (Shrum, 2004). First-order judg- ments tend to be memory-based judgments. Memory-based judgments are constructed by recalling information from memory and constructing the judgment in real time. In contrast, second-order cultivation judgments such as attitudes and values tend to be online judgments.3 Online judgments are constructed by relying on information as it comes into memory storage from an outside source (e.g., an ad, a speech, etc.). As Hastie and Park (1986) note, memory-based judgments are actually relatively rare, and often hard to produce, even in the lab. In contrast, online judgments are much more common and tend to be made spontaneously as information is received.

If indeed first-order and second-order judgments differ in how they are constructed, then it follows that the processes that underlie television effects on those judg- ments may also differ. In fact, as the next section illustrates, not only do the underlying processes differ, but in some cases appear to be exact opposites.

Cultivation as Online Persuasion

The premise of cultivation theory is that frequent viewing influences attitudes, values, and beliefs in the direction of the television message. Put this way, television viewing can be conceptualized as a persuasive communication. If so, and if in fact the attitudes, values, and beliefs are formed in an online fashion, there are a number of implications for the processes underlying the cultivation of second-order cultivation judgments. For one, it suggests that the influence of television on judgments occurs during viewing. Note that this differs from the influence of television on first-order judgments, in which the recall of television information influences judgments of frequency and probability at the time the judgment is requested. Second, if the cultivation is viewed as a per- suasive communication, then it follows that factors that facilitate or inhibit persuasion would likewise facilitate or inhibit the cultivation effect. In particular, research on dual- process models of persuasion such as the ELM (see chapter 8) specify that motivation and ability to process information moderates the effects of persuasion: Persuasion is enhanced when motivation and ability to process information are high.4 Given this,

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it follows that cultivation should be enhanced when motivation and ability are high during viewing.

Initial Tests

A few studies have provided initial support for this proposition. Shrum, Burroughs, and Rindfleisch (2005) conducted two studies that sought to determine whether motiv- ation and ability to process information during viewing moderates the cultivation effect. The first study consisted of a randomly sampled general population survey of U.S. viewers that looked at the relation between frequency of television viewing and the personal value of materialism. Motivation to process information was operationalized as the extent to which viewers tend to elaborate during viewing (need for cognition, Cacioppo & Petty, 1982) and ability to process was operationalized as self-reported tendencies to pay attention to the program while viewing (Rubin, Perse, & Taylor, 1988). As expected, viewing frequency was positively related to level of materialism, but the effect was stronger for those who tend to elaborate more during viewing (high need for cognition) and for those who tend to pay attention more while viewing. A follow-up experiment confirmed the hypothesis that high need for cognition people who are heavy viewers tend to elaborate more, and also more positively, than low need for cognition heavy viewers. The heavy-viewing high need for cognition participants also reported being the most immersed into the program while they viewed. Note that these particu- lar moderating effects for motivation and ability on second-order cultivation judgments are exactly the opposite of their moderating effects on first-order cultivation judgments. For the latter, high motivation and high ability to process information during recall decreased the cultivation effect (Shrum, 2001, 2007b).

The online nature of second-order cultivation judgments also has some implications for the accessibility of attitudes.5 If in fact currently existing attitude and value struc- tures get continuously accessed and updated during viewing, then the accessibility of those attitudes should be positively related to frequency of viewing. This proposition was confirmed in a study that measured television viewing and speed with which atti- tude judgments were made (Shrum 1999). As expected, heavy viewers provided their attitude judgments faster than light viewers, and this effect held over-and-above the effects of attitude extremity.

CONCLUSION

When combined with the previous studies on memory-based processing and first-order cultivation judgments, the results of the most recent studies on online processing of second-order cultivation judgments makes a convincing case that the processes under- lying media effects such as cultivation depend on the type of judgment being made. This has important implications for reconciling various disparate findings in the media effects literature. To start, the articulation of boundary conditions for the cultivation effect can be extrapolated to certain conditions that may inhibit the cultivation effect, making it small and at times nonsignificant. The different “routes to cultivation,” coupled with those boundary conditions, may also help explain why effects are often noted for one type of judgment but not the other (e.g., first-order effects may be more common and stronger than second-order effects; Hawkins & Pingree, 1982).

Specifying and documenting the underlying effects does more than simply contribute

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to the construct validity of the cultivation effect. The boundary conditions specified by the process models also imply ways in which unwanted effects of media consumption (e.g., increased materialism, less trust, inaccurate perceptions of society) may be miti- gated. The models just articulated suggest that media literacy programs not only need to teach viewers to “read the media,” they also need to teach viewers to “read the judgment” by educating viewers as to the types of judgments that are often affected by television viewing and how to devise (different) strategies based on the different underlying processes.

Acknowledgement

Preparation of this chapter was supported in part by a Faculty Development Leave grant and a Faculty Research Award from the University of Texas at San Antonio and by a Faculty Summer Research Grant from the College of Business.

Notes

1 The comprehensive aspect of the Wyer and Srull (1989) model is that it specifies precise mech- anisms for all stages in the information processing system (i.e., from input to output), and not necessarily that it is superior or more valid than other models. Most other models tend to focus on only selected aspects of the processing system (e.g., comprehension, storage, retrieval, response, etc.).

2 These two principles are discussed at more length by Carlston and Smith (1996) and Wyer (1980), who each use slightly different names for the principles. I have taken the liberty of renaming the principles to provide a better fit with the definitions and context of the discussions.

3 Of course, not all attitudes are formed in an online fashion. In certain instances, particularly when a current attitude is not very accessible or we are not confident in its validity, we may recompute our attitude from information we recall from memory (e.g., attitude toward a per- son, product, etc.). However, most of our attitudes, impressions, and perceptions are made spontaneously (and often automatically). As new information is encountered, new attitudes are formed or old ones are updated.

4 This is true only for strong (i.e., compelling) arguments. However, it is reasonable to think that heavy viewers find the story arguments compelling given that they watch frequently.

5 Note that this type of accessibility is different than the accessibility noted for first-order effects. Accessibility for first-order judgments refers to the accessibility of exemplars, which are used to construct memory-based judgments. Accessibility of attitudes or beliefs refers to the accessibility of a prior evaluative judgment.

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Sara Prot and Craig A. Anderson

Th e 20th century witnessed a mass media explo- sion after the invention of television, digital com- puters, and the Internet. Th is rapid technological development was followed by rapid growth in the fi eld of media psychology.

Researchers have gone from asking relatively sim- ple, basic questions such as, “Does observing fi lmed aggression increase aggressive behavior?” (Bandura, Ross, & Ross, 1963a) to asking complex and highly specifi c questions, such as, “Th rough which cogni- tive and aff ective mechanisms do violent media exert their infl uence on aggression?” (Anderson et al., 2003), “How robust and consistent are media violence eff ects on diff erent outcomes?” (Anderson & Bushman, 2001; Anderson et al., 2010) and “What are the long-term consequences of habitual media violence exposure?” (Huesmann et al., 2003; Bartholow, Bushman, & Sestir, 2005).

Th is chapter off ers a broad review of contemporary methodology used in the fi eld of media psychology

Abstract

This chapter provides an overview of contemporary research methods used in the field of media

psychology. Basic scientific principles are discussed. Commonly used research designs are described.

Some methodological pitfalls in media psychology research are explained and suggestions are given on

how to avoid them. Finally, guidelines are given on how to convey scientific methodology and findings

to the general public (see Chapter 26). We hope that this chapter will aid readers from other fields in

becoming informed consumers of media psychology research and aid media psychology researchers in

continuing the trend toward better methodological quality in the field.

Key Words: media psychology, research designs, research methods

in studying the eff ects of exposure to media violence on the consumer of such media. Although the basic prin- ciples and ideas described here apply more broadly to other domains of media-related research, such as motivations underlying media choices and prefer- ences (e.g., Ryan, Rigby, & Przybylski, 2006), we focus on the eff ects of exposure domain—and within this domain, we focus on media violence eff ects.

We discuss basic scientifi c principles that are at the foundation of all psychological research. An overview of widely used research designs is given. Common methodological pitfalls in media psychol- ogy are described as well as some suggestions on how to avoid them. Finally, guidelines are given on how to convey scientifi c methodology and fi ndings to the general public. We hope that this chapter will aid media psychology researchers in continuing the trend toward better methodological quality in the fi eld, aid journal editors and reviewers in doing a better job of screening out weak and promoting

7 C H A P T E R

Research Methods, Design, and Statistics In Media Psychology

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110 Research Methods, Design, and Statistics In Media Psychology

strong research, and aid readers from other fi elds in becoming informed consumers of media psychology research.

Science, Causality, and Media Psychology Empirical Research and Th eory Development test/revise/test/revise cycle

Research in the fi eld of media psychology can generally be divided into two approaches: quanti- tative and qualitative. Qualitative methods (e.g., content analyses, ethnographic studies and phe- nomenological studies) generate descriptive fi nd- ings and are usually conducted without forming a priori hypotheses (for a discussion of qualitative methods see Chapters 8 and 23). Th e majority of media eff ects research, however, is quantitative and follows a diff erent pattern, progressing through a cyclic interaction between theory and empirical research. Researchers identify a question of inter- est (e.g., What eff ects does media violence have on viewers?). One or more hypotheses are generated (e.g., Observing media violence will increase the likelihood of later aggression. Exposure to media violence will decrease helping.) and tested using multiple research methods. Empirical results lead to revisions and refi nement of the original hypotheses. Over time, a set of related hypotheses and empirical fi ndings is developed, a set that can be integrated into a larger conceptual model or theory. Th e theory can then be used to develop novel hypotheses that can be tested further through empirical research. Th e cycle is repeated, leading to further refi nement of the theory. Th is extensive test/revise/test/revise process leads to the development of theoretical mod- els based on sound principles which are unlikely to be invalidated by future research. For example, the general aggression model (GAM) (Anderson & Bushman, 2002b; Anderson & Huesmann, 2003; DeWall & Anderson, 2011) and the general learn- ing model (GLM) (Buckley & Anderson, 2006; Barlett, Anderson, & Swing, 2009; Gentile et al., 2009) integrate a number of earlier models and are based on more than 100 years of psychologi- cal research on learning, emotion, cognition, and behavior. Well-tested models such as these provide a solid foundation for interpreting fi ndings, mak- ing new predictions, and developing interventions. Nonetheless, specifi c interpretations can always be changed as a result of new discoveries. It is for this reason that scientists are reluctant to use the words fact, or proven, or truth, even when speaking with audiences and individuals who do not understand

this perpetual cycle of theory and data. Th us, the general public may view the “theory” of evolution as a mere guess or hypothesis, whereas the scientifi c community knows that the basic tenants of the the- ory are as well established and as factual and basic as the law of gravity. Th is diff erential understand- ing of the meaning of “theory” and other common words leads to much unnecessary miscommunica- tion among scientists and nonscientists, a topic that is addressed in a later section of this chapter.

translations from conceptual to empirical

One frequently overlooked (or underevaluated) aspect of scientifi c theory development and testing concerns the multiple translations that take place between the conceptual/theoretical level and the spe- cifi c procedures used to conduct empirical tests. Th at is, one must translate the conceptual hypothesis into specifi c empirical realizations of the independent and dependent variables (Carlsmith, Ellsworth, & Aronson, 1976; Anderson & Anderson, 1996). Figure 7.1 illustrates some of the multiple levels and translations that underlie an experimental manipula- tion of violent versus nonviolent violent game expo- sure. As can be seen, there are lots of levels between the most basic (and therefore the conceptually broad- est) theoretical level and the specifi c manipulation that a researcher creates in an empirical study. Keep in mind that a similar set of translations are needed to get from the conceptual dependent variable (e.g., aggression) and its empirical realization. Th us, there are lots of ways one can test the same conceptual hypothesis. Furthermore, although theory provides many constraints on what should be considered reasonable tests of any given conceptual hypothesis, there is no such thing as a perfect empirical realiza- tion of that hypothesis. For this reason, multiple studies using multiple methods give a better overall picture of the validity of any conceptual hypothesis than any single method or study can give. Further discussion of this appears in the next section.

Causality Th e majority of scientifi c theories and models in

nomothetic scientifi c disciplines (those that seek to uncover general laws that underlie phenomena, such as natural sciences and psychology; M ü nsterberg, 1899) are causal. Widely used theories in media eff ects psychology such as social learning theory and social cognitive theory (Bandura, 1973, 1983), general aggression model (Anderson & Bushman, 2002b; Anderson & Huesmann, 2003), cultivation

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theory (Comstock & Scharrer, 2007), and social information processing theory (Crick & Dodge, 1994) all imply causal relationships among vari- ables. Th e central characteristics of any good theory, the ability to predict and control outcomes, require causal models. Of course, establishing causality is often a diffi cult task, one that is seldom understood by public policy makers or the general public, and too often is misunderstood even by members of the scientifi c community. What follows is a partial list- ing of the most common diffi culties.

scientific causality is probabilistic Th e old Logic 101 principles of establishing cau-

sality do not apply to most modern science (Anderson & Bushman, 2002a). Scientifi c causality is probabilis- tic, instead of “necessary and suffi cient.” Stating that X causes Y means that variable X causes an increase in the likelihood of outcome Y (Anderson, 2004). For example, saying that smoking causes lung cancer means that smoking increases the likelihood of devel- oping lung cancer. Th is does not mean that all smok- ers get lung cancer; many do not (a violation of the principle of “suffi cient” causality). Furthermore, some nonsmokers do get lung cancer (a violation of the principle of “necessary” causality). Correspondingly, saying that violent video games cause aggression does not mean that every person who plays violent video games will necessarily become aggressive, or that all aggressive behavior is a result of violent video game play. It means that exposure to violent video games increases the likelihood of future aggression.

Probabilistic causality is a result of the fact that most (if not all) biological outcomes, disease pro- cesses, and human behaviors are multicausal (Gentile & Sesma, 2003). Complex behaviors of interest, such as prosocial behavior and aggression, are infl u- enced by a large number of factors (e.g., genetic pre- dispositions, parental practices, cultural infl uences) (Anderson & Huesmann, 2003; DeWall, Anderson, & Bushman, 2012). Media use is just one of many relevant factors that infl uence the likelihood of these behaviors. In most cases, it is neither a necessary nor a suffi cient cause. Nonetheless, media eff ects are not negligible and have important practical consequences in many domains, including aggression among oth- ers (Anderson & Dill, 2000; Gentile et al., 2004; Anderson et al., 2010), helping (Greitemeyer, 2009; Greitemeyer & Osswald, 2010), risk taking (Fischer et al., 2011), and school performance (Sharif & Sargent, 2006; Anderson, Gentile, & Buckley, 2007; Rideout, Foehr, & Roberts, 2010).

A methodological diffi culty in the fi eld of media psychology stems from the fact that many media eff ects are subtle, cumulative, and unintentional. For example, advertisements can have a subtle infl u- ence on viewers without their awareness (Gentile & Sesma, 2003). Although such short-term infl u- ences may be small, over time they can produce large cumulative eff ects. To use the cigarette smok- ing analogy, although short-term eff ects of smok- ing are relatively harmless and transient, long-term cumulative eff ects of this risk factor are lasting and severe. Likewise, although eff ects of watching

Learning Theory

Social Learning

Other types of Learning

Direct Experience

Observational Learning

Other Observational

Sources

Stories

Video Games

Violent

Nonviolent

IV Empirical Realization:

Specific games,

instructions, context

Stories

Other Types

Figure 7.1 Illustration of Multiple Translation Levels from Learning Th eory to Empirical Realization of the Independent Variable: Experimental Manipulation of Video Game Violence.

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112 Research Methods, Design, and Statistics In Media Psychology

a single violent TV show dissipate fairly quickly, habitual exposure to violent media has long-lasting eff ects on desensitization to violence (Bartholow, Bushman, & Sestir, 2005), hostile attribution biases (Anderson, Gentile, & Buckley, 2007), development of an aggressive personality (Bartholow, Sestir, & Davis, 2005), and aggressive behavior (Huesmann, Moise-Titus, Podolski, & Eron, 2003; Anderson, Sakamoto, Gentile, Ihori, Shibuya, Yukawa, Naito, & Kobayashi, 2008; M ö ller & Krah é , 2009).

An interesting solution to the methodological diffi culty of studying cumulative eff ects of media exposure is proposed by Potter (see Chapter 23). Th e author suggests that, instead of measuring group diff erences in eff ects of media exposure, as is done in the majority of media eff ects studies, attention should be shifted to patterns of eff ect score changes for individuals over time. Th is approach would allow researchers to directly examine the course of long-term changes produced by media infl uences, identifying how mass media infl uences gradually change a person’s baseline. Indeed, this is essentially what longitudinal studies of media violence do (e.g., Huesmann, Moise-Titus, Podolski, & Eron, 2003; Anderson, Sakamoto, et al., 2008).

role of plausible alternative explanations

Testing scientifi c theories largely involves creat- ing alternative explanations for a given phenomenon and then empirically testing whether the originally hypothesized relations among variables fi ts the data better than the alternative explanation. In essence, establishing causality involves testing and ruling out plausible alternative explanations. We emphasize “plausible” because the total number of alternative explanations—plausible + implausible—approaches infi nity. Furthermore, only alternative explanations that are empirically testable (at least in principle) qualify as plausible. Alternative explanations that cannot be empirically tested (e.g., god did it) usu- ally fall outside the realm of science. Of course, technological advances often create opportunities to test alternative hypotheses that previously had been untestable, which is why the “in principle” aspect of plausible alternative explanations is important. For example, recent advances in genetics and in neuroimaging have allowed tests of numerous new hypotheses about aggression and violence (DeWall, Anderson, & Bushman, 2011, 2012).

Relevant empirical results can cast doubt on alternative explanations and lend support to the tar- get theory. Or, such tests can support an alternative

explanation, thereby pointing to parts of the theory that need further revision. As the number of plausi- ble alternative explanations is reduced, the strength of the remaining theoretical explanation increases.

triangulation and alternative explanations

No single test of a theory-based hypothesis is defi nitive, irrespective of whether it confi rms or disconfi rms the prediction (Anderson & Anderson, 1996). One reason for this is because theoretical models involve abstract conceptual variables, whereas empirical tests involve concrete operationalizations of those variables (Carlsmith, Ellsworth, & Aronson, 1976). In other words (and as noted earlier), there are multiple levels of interpretation and specifi ca- tion between the theoretical model and empirical tests of the implications of that model (see Anderson & Anderson, 1996, for an example concerning the heat/aggression hypothesis). Operationalization of a conceptual hypothesis involves making several assumptions concerning the empirical methods being used (e.g., reliability and validity of the mea- sures, adequacy of the sample of variables and par- ticipants). Because of this, null results are often less informative than confi rming results, especially when new measures or procedures are used. Findings that are in line with theory-based predictions give sup- port not only to the target conceptual hypothesis, but also to various implicit assumptions made in the study. If, on the other hand, the study fails to support the hypothesis, a common reaction of researchers is to acknowledge that there are many possible reasons for those fi ndings. Th e unexpected results possibly refl ect the fact that the original hypothesis is wrong, but also might be a product of methodological weak- nesses of the specifi c study. Typically, for null results to be informative and result in a major change in theory they have to be replicated many times, shown to be not the result of mere poor methods or small samples, and have to lead to a more comprehensive theory that accounts not only for the null results but also accounts for the many results explained by the original theory (Kuhn, 1962). Unfortunately, occasional unreplicated null results based on small samples or poor methods, are frequently misinter- preted in the media violence domain as evidence of a lack of eff ects overall. Nonsignifi cant fi ndings from specifi c studies have been regularly used by media industry apologists to question the validity of stud- ies showing signifi cant harmful media eff ects, a criti- cism media violence scholars have faced many times (Huesmann & Taylor, 2003; Bushman, Rothstein,

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& Anderson, 2010). For example, one of the meth- odologically poorest media violence studies ever published (Williams & Skoric, 2005) is frequently promoted by the video game industry and gamers as proof that violent games don’t increase aggression. Th ey conveniently ignore that fact that that study didn’t measure aggression, had severe dropout rate problems, had diff erential dropout rates in the two conditions, and failed to show that the “violent” and “nonviolent” game conditions actually diff ered on the amount of exposure to violent games. 1

Far greater support for a conceptual hypothesis is given if conceptual relations are repeatedly tested and confi rmed using diff erent methodologies in diff erent contexts. Th is is the logic of multiple operationalism or triangulation (Campbell & Fiske, 1959; Anderson, 1987, 1989; Anderson, Gentile, & Buckley, 2007). Diff erent types of research designs make diff erent methodological assumptions, so if a conceptual rela- tionship is confi rmed time after time in studies using diff erent designs, it is extremely unlikely that the results are just a byproduct of methodological fl aws. Similarly, conceptual relationships that yield similar results using diff erent (but theoretically compatible) measures or manipulations greatly strengthen one’s confi dence in the basic conceptual model. When weaknesses of a particular type of study do not apply to other types, this enables researchers to triangu- late or home in on a true causal factor (Anderson, 1989). When a hypothesis survives many potential falsifi cations using varied methods, a robust eff ect is established. For example, Bandura’s initial fi nd- ings concerning the eff ect of televised violence on modeling of aggressive behavior may have been falsifi ed by several possible alternative explanations (Bandura, Ross, & Ross, 1961, 1963a). However, today researchers have no doubt that televised vio- lence increases aggression because this eff ect has been repeatedly shown using correlational studies (Eron, Huesmann, Lefkowitz, & Walder, 1972; McLeod, Atkin, & Chaff ee, 1972), experimental studies (Bjorkqvist, 1985; Josephson, 1987), and longitu- dinal studies (Huesmann, Moise-Titus, Podolski, & Eron, 2003). Th e interpretation of Bandura’s early studies has changed slightly as a result of changes in defi nitions of aggression. But our main point in this example is that when studies using various research designs and measures, done in a number of diff er- ent contexts and with samples from diverse popu- lations all converge on the same answer, we can be much more confi dent that this answer is indeed true. In the words of Richard Cardinal Cushing when asked about the propriety of calling Fidel Castro a

communist, “When I see a bird that walks like a duck and swims like a duck and quacks like a duck, I call that bird a duck” ( Th e New York Times, 1964).

Although this chapter is focused almost exclu- sively on quantitative research methods, it is impor- tant to emphasize that qualitative methods also play a signifi cant role in the fi eld of media eff ects, providing rich knowledge on the content of media messages and people’s individual experiences that cannot be obtained through experimentation or other forms of quantitative research (see Chapter 8). Also note that the line between qualitative and quantitative research is sometimes quite blurry.

Outcome Measures, Research Designs, and Review Types Outcome Measures

Choice of outcome measure is crucial to any study, both because it infl uences the likelihood that the study will yield useful results and because of the- oretical relevance. A measure of aggressive behavior that is appropriate in one research context may well be inappropriate in another. For example, a count of how often each child trips, pushes, or bites another child in a daycare setting can be a useful measure of physical aggression in that research context (i.e., young children at daycare), but would not be a valid measure of physical aggression for college students in a laboratory setting. A less obvious but equally important example frequently arises in the study of violent video game eff ects. Because violent video games involve a lot of physical aggression and almost no indirect or relational aggression, the dominant theoretical models of social learning and develop- ment all predict that playing such games is most likely to infl uence physical aggression. Measures of verbal and indirect aggression are unlikely to provide sensitive tests of the main hypothesis that exposure to violent video games increases the likeli- hood of aggressive behavior. Similarly, the measure of physical aggression has to match the age of the participants and the research context. For example, a measure of trait physical aggression in which the participant reports the frequency of aggressive acts over the past year is inappropriate as the main out- come measure in a short-term experimental study in which participants have just played a randomly assigned violent or nonviolent video game. Th e recent game play cannot change the frequency of aggressive acts that the person committed in the year before the game play, unless of course time travel is involved. Of course, such a trait physical aggression measure might be infl uenced by the content (violent

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114 Research Methods, Design, and Statistics In Media Psychology

versus nonviolent) of a recently played game, but in such a case it would represent some type of memory or reporting bias, not a true measure of the video game eff ect on physical aggression (Anderson & Bushman, 2001). Yet, several short-term experimen- tal studies have used traitlike measures of aggression as the main dependent variable.

It is impossible to succinctly describe all of the measures that have been used or could be used in the study of the eff ects that media have on consum- ers. We focus here on a few of the measures related to antisocial eff ects (e.g., aggressive behavior, cogni- tion, and aff ect) and to prosocial behaviors, cogni- tions, and aff ect.

aggressive behavior measures Because defi nitions of conceptual variables such

as “aggression” and “violence” diff er somewhat between disciplines and even over time, clarity of defi nition is critical in theory development and in translating conceptual variables into empirical real- izations (e.g., Carlsmith et al., 1976). Social psy- chologists have come to rely on a specifi c defi nition that is much narrower than what is used by the gen- eral public and in some other areas of psychology. Specifi cally, human aggression is “ . . . any behavior directed toward another individual that is carried out with the proximate (immediate) intent to cause harm. In addition, the perpetrator must believe that the behavior will harm the target, and that the tar- get is motivated to avoid the behavior” (Anderson & Bushman, 2002b, p. 28; see also Berkowitz, 1993; Baron & Richardson, 1994; Geen, 2001). Aggression and aggressive behavior are used inter- changeably throughout this chapter. It is important to note that aggression is always a behavior; it is not an emotion, thought, or desire. Also note that it is not the outcome of a behavior that defi nes it as aggressive or not, but the intent of the behavior, that is, the intent to harm. Th us, shooting an arrow at another person with the intent to harm them is an act of aggression, regardless of whether the arrow strikes or missed the target person. A shortcoming of many media eff ects studies arises from failure to use this defi nition.

Physical Aggression in a Lab Setting Numerous methods have been developed that

allow direct observation and measurement of aggres- sive behavior in laboratory settings. A common pro- cedure used to measure physical aggression is the teacher/learner paradigm , sometimes known as the Buss aggression machine paradigm (Buss, 1961; Geen

& O’Neal, 1969; Milgram, 1974; Donnerstein & Berkowitz, 1981). In this procedure, participants are told that purpose of the study is to explore eff ects of punishment on learning. Th ey are paired with a supposed second participant (actually a con- federate). Th e real participant is selected to be the “teacher” and the confederate is selected to be the “learner.” Th e participant presents stimuli to the confederate who seemingly tries to learn them. When the “learner” gives an incorrect response, the participant is supposed to punish him or her with an electric shock. Aggression is measured by the intensity and/or the duration of the shock the par- ticipant chooses to give the confederate. For exam- ple, Donnerstein and Berkowitz (1981) used this procedure to measure eff ects of combining violent and sexual content on aggression of males toward a female target. Participants who had viewed a violent, sexual fi lm delivered shocks of a higher intensity to a female “learner” than did those who viewed fi lms containing only violent or sexual content. Th ere have been many variations of this task, including use of diff erent types of punishments (e.g., hand in ice water instead of electric shock) (Ballard & Lineberger, 1999) and diff erent rationales for why the participant is delivering punishments (Baron & Richardson, 1994, pp. 69–75).

Another common method of measuring physi- cal aggression in the laboratory is the competitive reaction time task (Taylor, 1967; Bushman, 1995; Giancola & Parrott, 2008). Participants in this task compete against a supposed opponent on a reac- tion time task in which the winner delivers aversive stimulation (an electric shock or a noise blast) to the loser. In actuality, the pattern of wins and losses is predetermined by the experimenter. Provocation can be manipulated by increasing the intensity of shocks set by the “opponent.” Aggression can be measured as the intensity, duration, or number of high-intensity blasts given. For example, Anderson and Carnagey (2009) used this paradigm to test the eff ects of violent and nonviolent sports video games on aggression. Th ey found that playing violent sports games increased aggressive behav- ior, even after controlling for competitiveness. In other words, competitive reaction time task mea- sures aggression, not competitiveness (Gaebelein & Taylor, 1971; Bernstein, Richardson, & Hammock, 1987). Like the teacher/learner paradigm described in the preceding, the competitive reaction time task has been used in various modifi ed forms in hun- dreds of studies, and is one of the most extensively validated measures of physical aggression.

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115Prot, Anderson

Another commonly employed method to study direct physical aggression is to place the participant and the confederate in a situation that requires the confederate to evaluate the participant and later requires the participant to evaluate the confederate. In the evaluation paradigm (Berkowitz, 1962), for example, participants are led to believe that they will be evaluating another student’s performance on an assigned task. Solutions are evaluated using any- where from one to ten electric shocks, in which one shock indicates a very favorable evaluation and ten shocks indicates a very unfavorable evaluation. In some studies, the confederate evaluates the partici- pant’s solution. Generally, half of the participants receive a positive evaluation from the confederate (e.g., one shock), whereas the other half receive a negative evaluation (e.g., seven shocks). After expo- sure to some treatment (e.g., a violent or nonviolent fi lm), the participant then evaluates the confeder- ate’s solution. Th e measure of aggression is the num- ber of shocks the participant gives the confederate.

Barlett, Branch, Rodeheff er, and Harris (2009) used a more recently developed laboratory mea- sure of physical aggression, the hot sauce paradigm (developed by Lieberman, Solomon, Greenberg, & McGregor, 1999) to measure how long the eff ects of brief exposure to violent video games persist. In this procedure, participants decide how much hot sauce another person (known to dislike spicy food) must consume. Alternatively, one can have the participant determine the degree of hotness of the sauce that the other person must consume. Aggression is measured as the amount of hot sauce given to the target and/ or the degree of hotness of the sauce selected.

Indirect, Verbal, and Other Laboratory Aggression Measures

Some laboratory based studies use verbal aggres- sion measures. For example, in some studies the participant is given the opportunity to provide a potentially harmful written or verbal evaluation of another person (e.g., another participant, a confed- erate, or the experimenter), and does so knowing that the evaluation could hurt the other person. Sometimes the verbal aggression is direct, meaning that the participants believe that the target of their harmful evaluations will see or hear it. For exam- ple, Wheeler and Caggiula (1966) had participants listen and later evaluate another person’s (actually, a confederate’s) extreme and socially undesirable statements. Th e participants believed that the other person would hear their evaluations, so anything negative in the evaluations would presumably cause

some harm. Th ese evaluations were recorded and later coded for the degree of hostility.

Sometimes the evaluation is in the form of rat- ings that the target will not see, but that the par- ticipant believes will harm the target indirectly . For example, Berkowitz (1970) randomly assigned some female undergraduates to an anger induction con- dition (in which they listened to a job applicant’s insulting statements about university women) or a control condition. Half in each condition then lis- tened to either a hostile or a nonhostile comedian. All participants then rated the job applicant on sev- eral measures, with the knowledge that their ratings could aff ect the applicant’s chances of getting the job. Interestingly, the women who had heard the hostile humor gave the applicant worse ratings than those who had heard the neutral humor. Other simi- lar indirect verbal aggression measures have ranged from ratings of competence, to liking, job perfor- mance, and grades (Obuchi, Kameda, & Agarie, 1989; Dill & Anderson, 1995).

Perhaps the most recent addition to the list of lab- oratory aggression tasks is the tangram task (Gentile et al., 2009). In one study Gentile et al. randomly assigned participants to play a violent video game, a prosocial video game, or a game that was neither violent nor prosocial. Later, participants assigned an anonymous partner a set of 11 easy, moderately complex, or diffi cult tangram puzzles to attempt to solve within 10 minutes. Participants were led to believe that the partner would win a prize if they completed a suffi cient number of puzzles in 10 min- utes. Th e number of hard puzzles chosen constituted a measure of aggression, whereas the number of easy puzzles measured helping behavior. As expected, the violent video games increased aggressive choices, whereas the prosocial games increased helpful choices. Because this measurement task is the new- est, it has received less empirical attention that the older measures described earlier, and thus does not yet have the extensive network of validation studies.

Aggression Measures Outside the Lab Th e variety of ways that one can measure aggres-

sive behavior outside of controlled laboratory setting is huge, limited only by the combination of the con- ceptual defi nition and the creativity of researchers. Generally, they can be categorized as self-reports, other reports, and archival.

Self-reports may be very specifi c, such as report- ing how many physical fi ghts one has been in during the past school year. Or, they may be broad trait- like measures of habitual aggressiveness. Th ey may

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116 Research Methods, Design, and Statistics In Media Psychology

include any type of aggression (e.g., verbal, physi- cal) at any severity level (e.g., said mean things about a classmate, attacked a peer with a knife or gun). Common self-report measures of trait aggression in the media eff ects domain include the physical and verbal aggression subscales of the Buss and Perry (1992) Aggression Questionnaire and the physical violence subscale from the National Youth Survey (Elliot, Huizinga, & Ageton, 1985; Anderson & Dill, 2000). Other commonly used self-report trait aggression scales that include relatively more items that are not strictly aggressive behaviors are the Caprara Irritability scale (Caprara et al., 1985) and the Cook-Medley Hostility Inventory (Cook & Medley, 1954). Of course, there are many self-report measures, and researchers create new ones as the empirical and theoretical need arises.

Others’ reports of aggression include a wide range of measures, usually subcategorized into peer reports, teacher/supervisor reports, parent reports, and direct observation. Peer reports are frequently used in pre– high school settings. Often these involve asking each student in a classroom to rate each of their classmates on specifi c behaviors, or to nominate classmates who do certain aggressive behaviors. For example, it is common to ask, “Who pushes, shoves, or hits other kids to get what they want?” Teacher and supervisor reports ask similar questions about those under their care or supervision. Parent reports often ask about the frequency with which their child has done specifi c aggressive behaviors; other parent reports are vaguer, asking for ratings of “how aggressive” is your child. Direct observation studies often involve the record- ing of behavior in some naturalistic setting, followed by standardized coding of the recorded behavior. Sometimes, however, trained observers watch and code behaviors directly in the setting, such as while watching children on a playground.

Archival measures are derived from written records, such as crime reports and school incident records. Frequently archival measures are combined with other types of aggression measures.

aggressive cognition measures Exposure to violent media has a host of cognitive

consequences, which in turn can lead to aggressive behavior. A number of outcome measures have been used to assess infl uences of violent media on cogni- tion in both short- and long-term contexts.

Aggressive Cognition in Lab Settings Laboratory experiments measure short-term

infl uences of exposure to violent media on cognitive

processing. Such short-term eff ects mainly occur through priming of aggressive knowledge struc- tures, making them more accessible (Anderson & Huesmann, 2003). Various methods have been successfully used in laboratory settings to measure these increases in aggressive thinking.

A number of studies have shown an increased frequency of aggressive thought content during or immediately after media violence exposure. For exam- ple, Calvert and Tan (1994) used a thought-listing questionnaire to measure diff erences in aggressive thoughts while observing or playing a violent game in virtual reality. In a study by Bushman (1998), par- ticipants made free associations to nonaggressive words and to homonyms with one meaning more aggressive than the other (e.g., cuff , mug). More aggressive associations were made to both types of words by participants who had just watched a violent video.

Several studies have used a word completion task to measure accessibility of aggressive thoughts (Anderson, Carnagey, & Eubanks, 2003; Anderson, Carnagey, Flanagan,, Benjamin, Eubanks, & Valentine, 2004; Barlett et al., 2008). In this kind of task, participants are given a list of word frag- ments and are asked to fi ll in the missing letters to form the word. Some of the fragments can be completed to form either an aggressive word or a nonaggressive word (e.g., “h_t” can become hit or hat ). Aggressive thought accessibility can be calcu- lated as the proportion of word completions that were aggressive. Similar tasks have been commonly used to measure implicit memory (e.g., Roediger, Weldon, Stadler, & Riegler, 1992), and have been used to assess accessibility of prosocial thoughts as well (e.g., Greitemeyer, 2011).

A number of studies have used reading reaction times to aggressive and nonaggressive words as a measure of accessibility of aggressive cognitions (also called the word pronunciation task ) (e.g. (Bushman, 1998; Anderson & Dill, 2000; Anderson, Carnagey, & Eubanks, 2003). In the reading reaction time task (e.g. (Anderson et al., 1996; Anderson, 1997; Anderson, Benjamin, & Bartholow, 1998), partici- pants are timed as they read aggressive and nonag- gressive words. Average reaction times to aggressive and nonaggressive words can be compared to assess relative accessibility of aggressive thoughts. An advan- tage of this task is that suspicion or hypothesis-related demand characteristics are unlikely to infl uence responses because participants are taxed with trying to read all words as quickly as possible (Anderson, 1997). Furthermore, attempts by suspicious partici- pants to bias the results in either direction can be

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detected by examining the distribution of reaction times, because such biasing attempts typically yield unusually long reaction times.

In a somewhat diff erent approach, Uhlmann and Swanson (2004) measured the eff ects of violent video game play on automatic aggressive thoughts using the implicit association test. Th is study showed that media violence exposure can teach a person to automatically associate the self with aggressive traits and actions. More recently, Saleem and Anderson (in press) have used another version of this task to assess anti-Arab bias.

Interesting methods have been used to assess cog- nitive biases that result from media violence expo- sure. For example, to assess hostile expectation bias Bushman and Anderson (2002) had participants read ambiguous story stems about potential inter- personal confl icts. Participants were then asked to list what the main character will think, feel, say, and do next and their responses were coded for aggres- sive content. Several media eff ect studies (Kirsh, 1998; Anderson, Gentile, & Buckley, 2007; M ö ller & Krah é , 2009) have also used ambiguous provoca- tion stories to assess hostile attribution bias. In each story, an actor causes a negative event to happen, but the intent of the actor is unclear. After each story, participants are asked a series of questions concern- ing the provocateur’s intent. It has been shown that exposure to media violence leads to the development of a hostile attribution bias, a tendency to interpret ambiguous behaviors of others as malevolent (Kirsh, 1998).

Yet another method of assessing accessibility of aggressive cognitions is the word pair similarity rating task. Th is task was originally developed by Bushman (1996) to assess individual diff erences in aggressive cognitive-associative networks. But a minor revision to the task has been used to examine the eff ects of short-term experimental manipulations of variables, including pain (K. Anderson, Anderson, Dill, & Deuser, 1998), cooperative versus competi- tive video game instructions (Anderson & Morrow, 1995), and violent versus nonviolent music lyrics (Anderson, Carnagey, & Eubanks, 2003). Th is task consists of rating the degree of meaning similarity of each paired combinations of 20 words. Ten of these words have both aggressive and nonaggressive con- notations (e.g., bottle, night, stick). Th ese words are referred to as ambiguous words. Th e remaining ten words are more obviously related to aggression (e.g., butcher, choke, hatchet). Ratings of each word pair are made on a 1 to 7 scale of how “similar, associ- ated, or related” they are. Each participant gets three

scores, the average similarity rating of all ambiguous/ aggressive word pairs, ambiguous/ambiguous pairs, and aggressive/aggressive word pairs. Anderson, Carnagey, and Eubanks (2003) found that partici- pants who had just listened to songs with violent lyrics gave higher similarity ratings to ambiguous/ aggressive word pairs than did participants who had just listened to nonviolent songs. In other words, the violent lyrics increased the accessibility of the aggres- sive meaning of the ambiguous word pairs.

Aggressive Cognition Outside the Lab Correlational studies and longitudinal studies

provide an opportunity to explore long-term infl u- ences of violent media on cognition. Repeated expo- sure to media violence strengthens aggression-related knowledge structures and can make them chroni- cally accessible. Additionally, long-term exposure reinforces normative beliefs that violence is com- mon and appropriate (Carnagey & Anderson, 2003; Bushman & Huesmann, 2006). Dependent variables in correlational and longitudinal studies of aggres- sive cognition often include normative beliefs about violence (Gerbner, Gross, Jackson-Beeck, Jeff ries- Fox, & Signorelli, 1978; Gerbner, Gross, Morgan, & Signorelli, 1980; Bryant, Carveth, & Brown, 1981), positive attitudes toward violence (Funk et al., 2004; Anderson, Gentile, & Buckley, 2007) and hostile attribution bias (Anderson, Gentile, & Buckley, 2007). Th ese long-term consequences are can be assessed using self-report measures, such as the Normative Aggressive Beliefs Scale (Anderson, 2004; Anderson, Gentile, & Buckley, 2007), Huesmann’s NOBAGS scales (Huesmann et al., 1992), Funk’s Attitudes toward Violence Scales (Funk, Elliott, Urman, Flores, & Mock, 1999), and the Revised Attitudes toward Violence Scale (Anderson, Benjamin, Wood, & Bonacci, 2006). Some studies also use trait measures of aggressive cognition, such as the hostility subscale of the Buss-Perry Aggression Questionnaire (Anderson, Carnagey, Flanagan, Benjamin, Eubanks, & Valentine, 2004; Shibuya, Sakamoto, Ihori, & Yukawa, 2004; Bartholow, Sestir, & Davis, 2005).

aggressive affect measures Another route through which violent media

can increase aggression is by producing feelings of anger and hostility (Anderson et al., 2003; Swing & Anderson, 2010). Brief exposure to media vio- lence has been shown to lead to temporary increases in aggressive aff ect (Barlett et al., 2009), whereas chronic exposure leads to the development of a

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hostile personality (Bartholow, Sestir, & Davis, 2005; Bushman & Huesmann, 2006).

Aggressive Aff ect in Lab Settings Experimental studies in laboratory settings mea-

sure eff ects of media violence exposure on short-term aff ective states. Short-term aff ective consequences are most often assessed using self-report scales such as the State Hostility Scale (SHS) (Anderson, Deuser, & DeNeve, 1995), the Multiple Aff ect Adjective Checklist (Zuckerman, 1960; Zuckerman, Lubin, Vogel, & Valerius, 1964), or the State Anger sub- scale of the State-Trait Anger Expression Inventory (STAXI) (Spielberger, 1988). Many other studies have used the Positive and Negative Aff ect Schedule (PANAS) (Watson, Clark, & Tellegen, 1988). Th is widely used self-report scale has the advantage of assessing both positive and negative aff ect, as well as several more specifi c subtypes of aff ect. However, it is a less sensitive measure of hostility/anger, most likely because of fewer items for this more specifi c aff ective state (Anderson, Deuser, & DeNeve, 1995; Anderson, Anderson, & Deuser, 1996; Anderson, Anderson, Dorr, DeNeve, & Flanagan, 2000).

Such self-report measures can be complemented with physiological indicators such as heart rate, blood pressure, or skin conductance (Ballard, Hamby, Panee, & Nivens, 2006; Carnagey, Anderson, & Bushman, 2007). Additionally, researchers have started examining neural bases of short- and long- term media eff ects on emotional processing by using techniques such as magnetic resonance imaging (Weber, Ritterfeld, & Mathiak, 2006) and event- related brain potentials (Bartholow, Bushman, & Sestir, 2005; Bailey, West, & Anderson, 2011a).

Aggressive Aff ect Outside the Lab Long-term changes in aff ective processing as a

result of habitual media violence exposure can be assessed outside the laboratory using trait measures such as the Caprara Irritability Scale (CIS) (Caprara et al., 1985), the Cook-Medley Hostility Inventory (Cook & Medley, 1954), and the anger subscale of the Buss-Perry Aggression Questionnaire (Buss & Perry, 1992). Once again, more general trait-aff ect scales may be appropriate in some research contexts, but researchers need to be aware that general mea- sures of positive and negative aff ect usually are less sensitive measures of any give specifi c aff ect type.

physiological arousal measures For most people, exposure to media violence

tends to produce physiological arousal (Anderson

et al., 2003; Swing, Gentile, & Anderson, 2008). Arousal can be measured in experimental studies using indicators such as heart rate, blood pressure, or skin conductance (Ballard & Wiest, 1996; Fleming & Rickwood, 2001; Anderson et al., 2004; Barlett et al., 2008).

How lasting are these eff ects? Barlett et al. (2009) showed that heightened arousal immediately after playing a violent video game lasts between 4 and 9 minutes. However, these short-term changes can start aggression promoting processes that last much longer than 4 to 9 minutes, such as long-term desen- sitization to violence. Even within a short-term con- text, exposure to violent media may increase arousal (and hostile aff ect) for longer periods of time if the violent media episode increases processes that typi- cally last long and that increase anger arousal, such as rumination on a perceived unjust harm.

A popular belief in our culture is that playing violent video games or watching violent television and fi lms allows people to “vent” their aggression, decreasing arousal and reducing subsequent aggres- sive behavior (Anderson, Gentile, & Buckley, 2007). According to the catharsis hypothesis, engaging in real or imagined aggression helps relieve angry feel- ings, leaving us emotionally calmed (Dollard et al., 1939; Campbell, 1993). However, the bulk of research evidence opposes the catharsis hypothesis (Mallick & McCandless, 1966; Geen, Stonner & Shope, 1975; Geen & Quanty, 1977; Bushman, Baumeister, & Stack, 1999; Geen & Quanty, 1977Bushman, 2002). Although physiological arousal can decrease after the initial aggressive act, later aggressive behavior does not (Geen & Quanty, 1977). Instead, studies show that viewing, think- ing about or performing aggressive acts increases the likelihood of aggressive behavior (Dill & Dill, 1998; Geen, 2001).

desensitization/empathy measures Repeated exposure to violence can lead to desen-

sitization, best defi ned as a reduction in emotional and physiological reactivity to violence (Carnagey, Anderson, & Bushman, 2007). Empathy can be defi ned as the degree to which a person identifi es and commiserates with a victim and feels emotional distress (Anderson et al., 2010). A small number of high-quality studies exist in this domain (Anderson et al., 2010). However, media violence has been clearly linked to both short-term desensitization as a result of brief exposure (Carnagey, Anderson & Bushman, 2007), and chronic desensitization and decreased empathy as a result of habitual, long-term

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exposure (Mullin & Linz, 1995; Funk et al., 2003; Bartholow, Bushman, & Sestir, 2006).

Short-Term Eff ects on Desensitization/Empathy Desensitization to violence after brief periods of

exposure is typically explored in experimental stud- ies using physiological indicators such as heart rate, blood pressure, and galvanic skin response (Th omas, Horton, Lippincott, & Drabman, 1977; Linz, Donnerstein, & Adams, 1989; Carnagey, Anderson & Bushman, 2007). For example, participants in the Carnagey et al. (2007) experiment played a violent or nonviolent video game, and then watched fi lm clips of real violent behavior, including shootings, stabbings, and fi ghts. Heart rate and skin conduc- tance were recorded before and during video game play, and during observation of the violent fi lm clips. Both physiological indicators of emotional arousal increased in both game conditions while playing the assigned video games, but only those who had played a violent game showed decreases in arousal while watching the violent fi lm clips.

Long-Term Eff ects on Desensitization/Empathy Neurological evidence of chronic desensitiza-

tion to violence through playing video games also exists. Bartholow, Bushman, and Sestir (2005) found that habitual violent game players have reduced amplitudes of the P300 component of the event-related brain potential while viewing vio- lent images. Other laboratory studies have found similar eff ects (Kronenberger et al., 2005; Bailey et al., 2011a). Outside the laboratory, long-term eff ects on desensitization and empathy can be measured using self-report scales such as the Basic Empathy Scale (Jolliff e & Farrington, 2006), the Interpersonal Reactivity Index (Davis, 1980), the Index of Empathy for Children and Adolescents (Bryant, 1982) or Children’s Empathic Attitudes Questionnaire (Funk et al., 2008). Indeed, longi- tudinal studies have yielded evidence of long-term changes in desensitization/empathy as a result of media violence exposure (see Anderson et al., 2010, for the video game case).

prosocial behavior/helping measures Prosocial behavior involves helping or reward-

ing others, especially when this behavior brings no benefi t to the helper (Barlett, Anderson, & Swing, 2009). Eff ects of violent media on prosocial behav- ior have been less frequently explored than eff ects on aggression. In spite of this, several measures have been developed that make it possible to perform

reliable and valid measurement of prosocial behav- ior and helping.

Prosocial Behavior in Lab Settings Several procedures have been used in media vio-

lence research that allow direct observation and mea- surement of prosocial behavior in the laboratory. For example, Chambers and Ascione (1987) showed that children who had played a violent game donated less to charity. Ballard and Lineberger (1999) employed a variation of the teacher/learner paradigm in which participants could award jelly beans to their partner. Th e number of jelly beans awarded served as a mea- sure of helping and it was shown that participants who had just played a violent game tended to award a smaller number of jelly beans.

Bushman and Anderson (2009) simulated a fi ght in a laboratory experiment and found that partici- pants who had played a violent video game were less likely to help and took more time to help the “vic- tim.” Th ese participants perceived the fi ght as less serious and were less likely to notice the fi ght than the participants who played a nonviolent game.

Sheese and Graziano (2005) used a prisoner’s dilemma game in which participants were given a choice to cooperate with their partner for mutual gain, exploit their partner for their own benefi t or withdraw. Participants in the violent condition were signifi cantly more likely to choose to exploit their partner.

Gentile et al. (2009) developed a new task to measure helping—the previously mentioned tan- gram task . In this task, participants are asked to assign easy, moderately complex or diffi cult tan- gram puzzles to an anonymous partner. Participants are told that the partner will win a prize if they com- plete a suffi cient number of puzzles in 10 minutes. Th e number of easy puzzles represents a measure of helping behavior. Participants who had just played a prosocial video game assigned the most easy tan- grams, where as those who had just played a violent game assigned the fewest.

Prosocial Behavior Outside the Lab Th e most common type of measure chosen out-

side laboratory settings are self-report questionnaires such as the Prosocial Orientation Questionnaire (Cheung, Ma, & Shek, 1998). For example, a cor- relational study by Gentile et al. (2009) assessed video game habits of a large sample of children, along with several prosocial measures. Playing vio- lent video games was negatively related to helping behavior, whereas prosocial gaming was positively associated with helping.

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A longitudinal study by Anderson, Gentile, and Buckley (2007) measured children’s media violence exposure and prosocial behaviors twice during a school year and showed that video game violence at time 1 signifi cantly predicted a relative decrease in prosocial behavior over time. In this study, prosocial behavior was measured using teacher ratings and peer ratings.

Another less common measurement procedure is naturalistic observation. In an unusual fi eld experi- ment by Bushman and Anderson (2009), violent and nonviolent movie attendees saw a young woman with an injured leg struggle to pick up her crutches. Participants who had just watched a violent movie took longer to help than those who had just watched a nonviolent movie. Violent and nonviolent movie- goers did not diff er in their helpfulness before seeing the movie.

It is important to emphasize that prosocial and antisocial behaviors are not simply opposite sides of the same coin. Measures of aggressive and prosocial behavior tend to be negatively correlated, but not strongly so. One can be high both in helpful and in hurtful behaviors—for example, hostile toward ene- mies and kind toward friends (Gentile et al., 2009).

Research Designs Researchers generally use three broad types of

research designs: experimental studies, cross-sectional correlational studies, and longitudinal studies (Anderson & Bushman, 2001; Swing & Anderson, 2010). Each design has its own advantages and dis- advantages and is appropriate for certain kinds of research problems. Findings from diff erent kinds of studies complement each other and help researchers form a complete picture of media eff ects.

experimental studies Advantages

In experimental studies, researchers manipu- late exposure to media content and measure brief, short-term eff ects. Participants are randomly assigned to treatment and control groups; for example, play- ing a violent or nonviolent video game (Anderson & Dill, 2000). With all other factors controlled, a diff er- ence between two groups, for example, in aggression, establishes a causal link between violent media and subsequent aggression. Random assignment ensures that there are no preexisting diff erences between the two comparison groups (within certain statisti- cal limits) and allows researchers to rule out a host of alternative explanations. If a diff erence in aggres- sive behavior of the two groups is found, it is very

likely that this diff erence was caused by experimental manipulation (exposure to video game violence). It is very improbable (although not impossible) that highly aggressive individuals just happened to be ran- domly assigned to the experimental group and non- aggressive individuals were assigned to the control group. Th e larger the sample size, the less likely it is that a disproportionate percentage of highly aggres- sive people were randomly assigned to any one condi- tion, just as tossing a coin 100 times is less likely to yield 80% “heads” than tossing it only ten times.

If the researcher has additional information about the research participants before they are assigned to condition, information that may be relevant to the dependent variables of interest such as gender or trait aggressiveness, they may decide to “block” on these other variables in the random assignment pro- cedure. For example, they may separately random- ize males and females to the diff erent experimental conditions to ensure that each gender is represented equally across the conditions; but the logic and power of true experiments does not require this.

Methodologically sound experimental stud- ies in the fi eld of media psychology share several characteristics—they are designed so that they con- trol for many possible alternative explanations (i.e., high internal validity), have adequate sample sizes, employ eff ective experimental manipulation, and use a reliable and valid measure of the dependent variable.

High-quality laboratory experiments use well-validated paradigms to test relevant hypotheses. For example, Anderson and Dill (2000) conducted a laboratory experiment to test short-term eff ects of playing a violent video game on aggressive thoughts and behavior. In this study, a large sample of 227 college students participated. Participants were ran- domly assigned to play a violent or a nonviolent game. Games used in the study were carefully pretested and matched on several relevant dimensions (e.g., diffi – culty, frustration, and the physiological arousal lev- els they produce). Aggressive behavior was measured using a modifi ed version of the Competitive Reaction Time Task (Taylor, 1967), a widely used measure of aggressive behavior that has well-established internal and external validity (Carlson, Marcus-Newhall, & Miller, 1989; Anderson & Bushman, 1997; Giancola & Chermack, 1998; Anderson, Lindsay, & Bushman, 1999). Aggressive cognition was measured with a reading reaction time task that had been successfully used in previous aggression studies (Anderson, 1997; Anderson, Benjamin, & Bartholow, 1998) as well as in many studies in cognitive psychology. Violent

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video game play led to signifi cant increases in aggres- sive cognition and aggressive behavior. Th is study made an important contribution to the violent video game eff ects literature because previous experimental studies in this area had methodological weaknesses that put their results into question. A number of early experiments testing for violent video game eff ects (Cooper & Mackie, 1986; Silvern & Williamson, 1987; Schutte, Malouff , Post-Gorden, & Rodasta, 1988; Irwin & Gross, 1995) did not match violent and nonviolent games on important dimensions and thus could not rule out the possibility that other vari- ables such as arousal, diffi culty, or frustration caused the observed diff erence in aggressive behavior.

High-quality fi eld experiments use measures of real-life behavior in natural settings. For example, as mentioned earlier, Bushman and Anderson (2009) tested eff ects of media violence on helping behavior by staging a minor emergency outside movie the- aters that were showing either a violent or a nonvio- lent movie. Moviegoers saw a young woman with a wrapped ankle “accidentally” drop her crutches outside the theater and struggle to pick them up. Th e emergency was staged either before the movie (to control for helpfulness of people who choose to view violent versus nonviolent movies) or after the movie (to test for the eff ect of viewing media vio- lence on helping). In this case, the randomization was whether the measure of helpfulness occurred before or after viewing the movie. Before watch- ing a movie, no diff erences in helping were found between those going to a violent versus nonviolent movie. However, after the movie, participants who had just viewed a violent movie took signifi cantly longer to help the confederate than those who had viewed a nonviolent movie.

Disadvantages Th e main advantage of experimental studies is

that they enable strong causal inferences. A poten- tial disadvantage concerns the ability to generalize results to real-life conditions. Field experiments don’t suff er this concern. But, because most experi- ments are conducted in the laboratory, the general- izability of fi ndings from such studies to real-world settings is sometimes questioned. However, such doubts have been challenged and refuted both by rational arguments (e.g., Mook, 1983) and empiri- cal studies of external validity of laboratory experi- ments (e.g., Anderson & Bushman, 1997).

Th e main purpose of most laboratory studies is to explore conceptual relationships between variables and thus test and develop theories. Th e goal is to be

able to generalize these underlying theoretical prin- ciples, not specifi c features of the sample, manipula- tion, or measure (Berkowitz & Donnerstein, 1982; Henshel, 1980; Mook, 1983; Banaji & Crowder, 1989; Anderson & Bushman, 1997). Conceptual relationships between variables generalize, even if specifi c operationalizations do not.

Th e external validity of laboratory experiments is also supported by empirical fi ndings from several studies. For example, in the aggression domain it has been shown that laboratory measures of aggression are positively associated with each other, and that variables that infl uence aggression and violence in the real world have the same kind of eff ects on laboratory measures of aggression (Carlson, Marcus-Newhall, & Miller, 1989; Anderson & Bushman, 1997; Bushman & Anderson, 1998). Similarly, Anderson, Lindsay, and Bushman (1999) explored the consis- tency between fi ndings obtained in laboratory and fi eld settings across several domains in psychology (e.g., aggression, helping, leadership style, social loafi ng, self-effi cacy, depression, and memory). Th is study found considerable correspondence between lab- and fi eld-based eff ect sizes, suggesting that labo- ratory experiments have high external validity.

Laboratory settings also enable researchers to explore relationships between variables that may never be suffi ciently isolated in real life to enable precise testing (Mook, 1983). If increasing the similarity of the laboratory situation to real-world conditions interferes with the internal validity of the study, external dissimilarity (to achieve high internal validity) is strongly favored (Anderson & Bushman, 1997).

Th ere are two additional potential disadvantages of experimental designs in media eff ects studies. Both involve ethical considerations. First, one cannot ethically conduct an experiment in which one of the experimental treatments is expected to increase a seri- ously harmful behavior, such as aggravated assault or homicide. One can’t randomly assign a group of 10 year olds to play either a violent or nonviolent video game, then give each a handgun, and turn them loose on the playground to see which group does the most killing during recess. For this reason, alternative measures of aggressive behavior have been developed and used. Field experiments typically measure milder forms of physical aggression, such as hitting, pushing, shoving, and biting. Laboratory experiments use a variety of measures of aggression, including measures of physical and verbal aggression. And as noted ear- lier, these measures have been well validated, showing high levels of external validity.

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Th e second potential disadvantage of experi- mental designs in this domain concerns duration of the manipulation. It is not ethical to intentionally expose a group of participants to a long-term high media violence diet to see whether this randomly assigned group becomes more aggressive than a ran- domly assigned control group. One can’t randomly assign a group of 4 year olds to grow up in either a high or low media violence household, and then measure their level of aggressiveness in school or criminal records at age 18. One can, however, use a long-term experimental design to see if an interven- tion designed to reduce exposure to media violence has any eff ect on aggression. A few such experi- mental intervention studies have been done (e.g., Huesmann, Eron, Klein, Brice, & Fischer, 1983; Robinson, Wilde, Navracruz, Haydel, & Varady, 2001), and have found that such interventions can reduce aggression.

cross-sectional correlational studies Advantages

Cross-sectional correlational studies explore the direction and magnitude of associations among relevant variables. Th e independent variable is measured instead of manipulated, and both the independent and dependent variable are measured once, usually at the same point in time. Strengths of correlational studies include the ability to measure real-world outcomes, test diff erent alternative expla- nations, and suggest new hypotheses about causal relationship.

Disadvantages Th e main weakness of correlational research is

diffi culty in establishing causality. Results of a single correlational study in which variables are measured at the same single point in time cannot ascertain cause-and-eff ect relationships. In other words, corre- lational studies generally have lower internal validity than experimental studies (Anderson & Bushman, 1997). Of course, some correlational studies are more informative about causality than others. For example, some of the early violent video game eff ect studies had serious methodological diffi culties (Dominick, 1984; Lin & Lepper, 1987; Fling et al., 1992). Th ese studies showed signifi cant associations between playing video games and aggression, but did not distinguish between playing violent versus nonviolent games. In contrast, Anderson, Gentile, and Buckley (2007) tested the strength of the asso- ciation between aggression and violent video game play, while controlling for several key competitor

variables (total screen time, normative aggression beliefs, positive orientation toward violence and sex). Th is example leads us to the important con- cept of destructive testing.

Destructive Testing Because of the critical role played by testing plau-

sible alternative explanations in theory development, even cross-sectional correlational studies can play an important part in testing causal hypotheses. Th ey can provide an opportunity for falsifi cation of the causal hypothesis as well as for testing and ruling out alter- native hypotheses. Well-designed correlational stud- ies can measure many theoretically relevant variables along with the target independent variable and the target dependent variable, and then statistically con- trol for eff ects of those other variables. For example, Anderson and Dill (2000, Study 1) used the destruc- tive testing approach (Anderson & Anderson, 1996) to assess the strength of the relationship between violent video game exposure and aggression. In this approach, a predicted relationship between variables is fi rst established. Th en one attempts to break the relationship by adding competitor variables. Th e key question is not whether the relationship can be bro- ken—even strong truly causal links can eventually be rendered nonsignifi cant in a correlational study by adding more correlated predictors into the model. Instead, the focus of destructive testing is on how diffi cult it is to break the relation, considering the theoretical and empirical strength of the competitor variables used to test it. If the inclusion of several rel- evant competitor variables fails to break the relation- ship, this gives strong support to the validity of the target link. For example, in the study by Anderson and Dill (2000), the eff ect of violent video game play on aggression remained signifi cant even with the inclusion of variables such as time spent playing any kind of video game and sex. Statistically controlling for these covariates invalidated several possible alter- native explanations of the video game violence eff ect, thereby strongly supporting the authors’ prediction that playing violent games will increase aggression. When using destructive testing, relevant covariates may include confounds (e.g., sex), potential com- petitors (e.g., total time spent playing), and potential mediators (e.g., aggressive personality). Occasionally, researchers also have mistakenly included as covari- ates variables that are better conceived as additional outcome (dependent) variables.

If the target link is broken by a single competitor variable or a single confounded variable, this puts the validity of the original causal hypothesis into

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question. However, mediating variables and second- ary outcome variables have a very diff erent theoreti- cal status in correlational studies. Mediating variables are those that theoretically link the predictor vari- able to the outcome variable. In essence, they are another outcome variable of the same independent variable. For example, repeated exposure to violent video games (the predictor or independent vari- able) may increase aggressive behavior (outcome or dependent variable) because such exposure increases trait aggressiveness (the mediator variable). Th us, a proposed mediator variable should signifi cantly weaken or even break the link between the predic- tor and outcome variables, even when that link is causal. When this happens, it lends support for the predicted theoretical model. Unfortunately, some gamers/media researchers (e.g., Ferguson et al., in press) either don’t understand this principle or they choose to ignore it when promoting their position. Th ey have incorrectly concluded that when mediator variables such as trait aggressiveness weaken the cor- relation between habitual exposure to violent video games and aggressive behavior, this weakening of the key link contradicts the main theoretical hypothesis; in reality, such a result supports the causal model.

Figure 7.2A displays this issue with a Venn dia- gram. Th e three circles represent the variance of three variables, media violence, trait aggression, and bullying behavior. Th e area represented by sec- tions A + B represents the correlation (or overlap) between media violence and trait aggression. C + B represents the correlation between media violence and bullying. B + D is the correlation between trait

aggression and bullying behavior. Signifi cance tests of the various relations can be thought of tests of whether overlapping areas are signifi cantly greater than zero. If media violence truly causes an increase in the likelihood of bullying behavior, and it does so at least in part because it increases trait aggres- sion as a mediating variable, then the theoretically most appropriate test of whether media violence is signifi cantly related to bullying is the B + C area. But when trait aggression is treated as a nuisance variable that is statistically controlled, then the test of the hypothesis includes only area C, an unrealisti- cally conservative test. By adding more restrictions on what gets counted as media violence/bullying variance, such as by adding additional covariates that themselves are theoretical outcomes of high media violence exposure, one can further inappro- priately reduce the “unique” overlap between media violence and bullying.

A related problem occurs when two conceptu- ally related predictors are used in the same regression model. For example, one study included both tele- vision violence and video game violence as separate predictors of aggression (Ferguson, San Miguel, & Hartley, 2009). Th is also removes considerable pre- dictive variance inappropriately because television violence and video game violence are highly corre- lated (in that sample: r (602) = .47, p < .001) yet both contribute to the same theoretical explanation (i.e., media violence increases aggression). Figure 7.2B displays this problem. Testing the video game eff ect on aggression after controlling for the televi- sion violence eff ect, that is, testing area B, is overly

Media Violence

Trait Aggression

Bullying Behavior

A

B

C

D

Panel A.

Video Game Violence

Television Violence Aggression

A

B

C

D

Panel B.

Figure 7.2 Inappropriate Uses of Covariates in Regression AnalysesA. When a mediator variable is added as a covariate, it can signifi – cantly weaken or even break the link between the predictor and outcome variables, even when that link is causal. B. When two concep- tually related predictors are used in the same regression model, considerable predictive variance is removed which results in an overly conservative signifi cance test.

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124 Research Methods, Design, and Statistics In Media Psychology

conservative. It may be appropriate to include two conceptually and empirically overlapping predictors in a model if one wants specifi cally to compare the unique contributions of these two predictors (as in Anderson et al., 2007 tests of the relative strength of old versus new media), but otherwise the hypoth- esis that media violence increases aggression is better tested by including these two variables in separate regression models or by statistically combining them into a single “media violence” predictor. Th us, mod- els that test the hypothesis of media violence eff ects on aggression with more than one distinct media violence predictor are unnecessarily biased against this hypothesis, losing a substantial portion of the eff ect(s) of interest to a covariate that is not an alter- native explanation at all.

A third version of this problem concerns cases in which a control variable (e.g., sex of participant) is correlated with both the main independent variable (e.g., video game violence) and the main dependent variable (e.g., physical aggression). Males spend more time playing violent video games than females, and also are more likely to use physical aggression in many contexts. If playing violent video games is truly a causal risk factor for later physical aggression, then at least part of the confounded variance pre- dicting physical aggression truly belongs to the vio- lent video game eff ect. Controlling for the sex eff ect in essence overcorrects for the confound between sex and exposure to violent video games. Th us, cor- relational studies that control for sex likely underes- timate the true eff ect size of violent video games on physical aggression (Anderson et al., 2010).

Th is problem is not unique to the media vio- lence domain; indeed, this is pretty basic to research design, statistics, and methodology. Th is diagram also illustrates another point. Although the media violence critics are quick to note that “correlation is not causation,” they seem to miss the necessary counterpoint that “lack of correlation is not lack of causation.” Th at is, the same third variable problems that make it risky to conclude on the basis of one or several cross-sectional studies showing signifi cant overlap between X and Y that “X causes Y,” also make it risky to conclude that “X is not a cause of Y” based on studies showing that X and Y do not signifi cantly overlap, especially if theoretically inappropriate cova- riates are fi rst controlled for the overlap tests.

longitudinal studies Advantages

In longitudinal studies, independent and depen- dent variables are measured at two or more points in

time. Such studies provide an opportunity to assess real-life consequences of long-term media expo- sure. Causality is easier to establish in longitudinal studies than in cross-sectional correlational studies because temporal relations among variables make it possible to rule out a host of alternative explana- tions. For example, media habits and school perfor- mance can be assessed both early and late in a school year (as was done in a study by Anderson, Gentile, & Buckley, 2007). Results can be analyzed to see if the amount of habitual entertainment screen time (television, fi lm, video games . . . ) at measurement Time 1 predicts school performance at Time 2 after statistically controlling for Time 1 school perfor- mance. Th e fi nding that total habitual screen time measured at Time 1 is a signifi cant negative pre- dictor of grades at Time 2 provides much stronger support for the hypothesis that time spent on televi- sion and video games has a negative eff ect on school performance than results from cross-sectional cor- relational studies showing a signifi cant association at a single point in time (Anderson & Dill, 2000; Gentile et al., 2004; Sharif & Sargent, 2006).

In cases in which experimentally manipulating a particular independent variable would be diffi cult or unethical, longitudinal studies represent an excellent way for making sound causal inferences. For exam- ple, in a study by Hopf, Huber, and Wei ß (2008), cumulative long-term infl uences of media violence exposure on adolescents’ violence and delinquency were investigated—two behaviors that cannot be ethically investigated in an experimental study. Th e frequency of adolescents’ exposure to media violence was measured over a 2-year period as well as expo- sure to eight other risk factors. Exposure to media violence at age 12 was a signifi cant predictor of vio- lence ( b = .28) and delinquency ( b = .39) at age 14, even after controlling for earlier levels of violence and delinquency and several other relevant variables.

Disadvantages Th e main disadvantages of longitudinal designs

are that they are time consuming and expensive. Repeated measurement requires researchers to keep track of participants and pay them to stay in the study. Large samples need to be taken to com- pensate for dropout rates. Another potential con- cern is nonrandom attrition. For example, in a 3-year study of television violence eff ects commis- sioned by the NBC television company (Milavsky, Kessler, Stipp, & Rubens, 1982), data from a large portion of the most aggressive participants in the sample were deleted because they allegedly had not

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given accurate reports of their television viewing. Although the original authors concluded that there was little evidence of a television violence eff ect, closer examination of this study reveals diff erent conclusions (Kenny, 1984; Anderson et al., 2003; Huesmann & Taylor, 2003).

mixed designs Many high-quality media eff ects studies com-

bine multiple design features. Adding a correla- tional component in experimental designs can have several advantages. Including measures of relevant covariates makes it possible to perform additional tests of key hypotheses and explore eff ects of pos- sible mediating and moderating variables. Including individual diff erence variables can also decrease error variance and increase the precision or power of statistical analyses, maximizing the likelihood that true eff ects will be detected. For example, an experi- mental study by Konijn, Bijvank, and Bushman (2007) elucidated the role of wishful identifi ca- tion as a possible moderator/mediator of violent video game eff ects. In this study, exposure to video game violence was experimentally manipulated. In addition to the dependent variable (aggressive behavior), several relevant covariates were measured (e.g., trait aggressiveness, general exposure to video games, immersion, and wishful identifi cation). It was shown that playing a violent video game had the strongest eff ect on aggression for participants who wished they were like a violent character in the game. Furthermore, identifi cation was associated with realism of the game and with immersion.

Some experimental studies also include a lon- gitudinal component. For example, Huesmann, Eron, Klein, Brice, and Fischer (1983) conducted a 2-year intervention study that aimed to mitigate eff ects of television violence on aggressive behavior of school-aged children. Children selected because of their high exposure to violent television were ran- domly assigned either to a control group or an exper- imental group that received treatments designed to decrease eff ects of television violence (lessons about the unreality of television violence and an attitude change treatment). After the intervention, children in the experimental group were rated as signifi cantly less aggressive by peers and showed a lower associa- tion between viewing violence and aggression.

A potential methodological diffi culty in long- term experiments concerns the eff ective manipula- tion of the independent variable and control and measurement of possible confounds over a period of time. For example, an experimental study by

Williams and Skoric (2005) attempted to measure eff ects of violence in a massively multiplayer online role-playing game (MMORPG, a type of online game in which a large number of players interact and play the roles of diff erent characters) on aggres- sion after 1 month of game play. However, exposure to other violent games was not controlled or mea- sured during this 1-month period so no evidence existed that participants in the violent game condi- tion actually spent more time playing violent video games than participants in the control condition. Furthermore, the MMORPG used in this study was not very popular, which apparently resulted in play- ers being unable to do much fi ghting in the game because of a lack of opponents. Th e participants in this study were recruited from online gaming sites. Furthermore, the overall dropout rate was huge, especially in the control condition, thus ruining the main advantage of experimental studies. Th erefore, it is possible that during the study period partici- pants in the control condition were exposed to as much (or even more) violent video game play than those in the violent game condition.

Scientifi c Literature Reviews Each research design plays an important role in

the study of media eff ects. Sound causal conclu- sions are based on consistent results across each of these designs (Abelson, 1995; Swing & Anderson, 2010). When a suffi cient number of studies have been done on a specifi c topic, the results can be combined in a literature review. Such a review can answer additional questions, support or refute theo- retical models, and point toward areas that are in need of further research. Reviews enable researchers to draw more advanced conclusions than would be possible on the basis of results from any single study. Two types of reviews can be performed—narrative and meta – analytic reviews.

narrative reviews In traditional narrative literature reviews fi nd-

ings from relevant published empirical studies are described, categorized, and summarized. Possible goals of narrative reviews include providing an overview and integration of an area, theory evalua- tion and development, identifi cation of weaknesses or contradictions in a specifi c fi eld of investiga- tion, and generating new problems and hypoth- eses (Baumeister & Leary, 1997). By searching for connections among a large number of empirical fi ndings, narrative reviews can address much wider questions than any single empirical study. Th e major

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126 Research Methods, Design, and Statistics In Media Psychology

strength of narrative literature reviews is their focus on conceptual relationships between key variables that can lead to rich theoretical and methodological insights (Anderson, Gentile, & Buckley, 2007).

However, diff erent studies necessarily yield some- what diff erent fi ndings. Even if a study was repli- cated perfectly using the exact same methods, the results would be diff erent because of eff ects of ran- dom factors. How should these diff ering results be interpreted and what conclusions should be drawn? A weakness of narrative literature reviews is that many critical decisions that are made while selecting and interpreting studies are subjective. Th is opens the door for reviewer biases that can result in drasti- cally diff erent interpretations of the empirical fi nd- ings by diff erent reviewers. People generally have a tendency to disregard evidence that contradicts their beliefs (Lord, Ross, & Lepper, 1979; Kunda, 1990; Koehler 1993), and reviewers are not exempt from such biases. Diff ering theoretical and empiri- cal orientations can lead reviewers to form diff er- ent inclusion criteria and organizational schemes, leading to diff erent conclusions (Dill & Dill, 1998; Griffi ths, 1999). Because of this, it’s important that reviewers pay attention to counterexamples and allow themselves to be led by evidence rather than rigidly imposing a priori beliefs and expectations (Baumeister & Leary, 1997).

meta-analytic reviews Meta-analytic reviews use statistical techniques to

combine the results of a number of empirical stud- ies that tested the same hypothesis. Meta-analyses describe the typical strength of an eff ect, its vari- ability, its statistical signifi cance, and variables that moderate it (Rosenthal, 1995). When a suffi ciently large number of studies are available that tested the same hypothesis and a meta-analysis is usable, it is generally the preferred review method (Baumeister & Leary, 1997). By combining results from mul- tiple studies, meta-analytic reviews can resolve inconsistencies caused by small sample sizes. Th e main strength of meta-analytic reviews is objectiv- ity (Anderson, Gentile & Buckley, 2007). Unlike narrative reviews, meta-analyses done to answer a particular research question tend to give similar answers irrespective of diff erent perspectives held by diff erent reviewers. However, the meta-analytic reviewer still has to make important decisions con- cerning what studies to include and what studies to exclude from the sample. Th us, poorly conducted meta-analyses, those that do not include all relevant studies (Ferguson et al., in press), can be just as

misleading as a biased narrative review. Th e major weakness of meta-analyses is that the focus on sta- tistical aspects sometimes leads the researchers to ignore important conceptual aspects.

In well-conducted meta-analyses, researchers attempt to fi nd all available published and unpub- lished studies that might be eligible for inclusion in the sample, construct a clear and explicit set of inclusion criteria, and conduct publication bias analyses. For example, the most recent and com- prehensive meta-analysis in the violent video game eff ects domain was conducted by Anderson et al. (2010). Th is meta-analysis combined a total of 136 research papers with 381 eff ect size estimates involv- ing more than 130,000 participants from Eastern and Western countries. Six outcome variables were included in the meta-analysis: aggressive behavior, aggressive cognition, aggressive aff ect, physiological arousal, desensitization/low empathy, and prosocial (helping) behavior. Newer studies of higher method- ological quality made it possible to use more strin- gent inclusion criteria in this meta-analytic review than in prior reviews, and allowed tests of the eff ects of a number of relevant moderators (e.g., sex, cul- ture, player’s point of view). Both the best practices sample and the full sample yielded the same results: Violent video games had signifi cant eff ects on all six outcome variables, showing that video game violence is indeed a causal risk factor for increased aggression and decreased prosocial behavior.

Methodological Pitfalls in the Field of Media Psychology Conducting Studies in a “Th eoretical Vacuum”

When attempting to understand underlying processes of media eff ects, it’s important to keep in mind general knowledge in the fi eld of psychol- ogy concerning mechanisms of memory, learning, social cognition, and development. Media eff ects research is informed by extensively replicated fi nd- ings and well-validated theoretical models from sev- eral disciplines, including, among others, cognitive psychology, developmental psychology, personality psychology, social psychology, and neuroscience. Well-tested and generally accepted theories such as schema theory (Alba & Hasher, 1983; Schmidt & Sherman, 1984), social learning theory and social cognitive theory (Bandura, 1973, 1983), script theory (Huesmann, 1986, 1988, 1998), and risk and resilience models (Glantz & Johnson, 1999; Gentile & Sesma, 2003) provide a solid foundation for predicting and explaining fi ndings in the fi eld

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127Prot, Anderson

of media psychology. As Kurt Lewin (1951) put it, “Th ere is nothing so practical as a good theory.” However, even though there are well-developed and well-validated theoretical models behind media vio- lence eff ects (Anderson et al., 2003), some other research domains involving media eff ects suff er from a lack of theoretical focus (see Chapter 23).

A dangerous error sometimes made by media eff ects researchers is planning studies and interpreting results as if they are completely disconnected from the fi eld’s general knowledge of psychological function- ing. For example, researchers who deny the existence of media violence eff ects on aggression are ignoring reliable and extensively replicated fi ndings regard- ing priming (Bargh, 1982; Bargh & Pietromonaco, 1982), observational learning and imitation (Bandura, Ross, & Ross, 1961, 1963a,b; Meltzoff & Moore, 1977), excitation transfer (Zillmann, 1971, 1983) and desensitization (Wolpe, 1982). Given this extensive literature concerning ways that aggressive and nonaggressive social behaviors are learned and induced, it would indeed be very surprising if media violence did not aff ect us.

Using Inadequate Sample Sizes Because most eff ects found in media psychology

are small or medium in size, adequately large sam- ples are needed to detect media eff ects. If the average eff ect size is about r = .20 (Anderson & Bushman, 2001), a sample of at least 200 participants should be taken to have .80 power. If sample sizes being used are too small, this will lead to results that are unstable and seemingly inconsistent. More reliable estimates can be obtained through combining such studies using meta-analytic techniques, of course, but researchers need to use adequate sample sizes in every study.

Using Inappropriate Experimental Manipulations

Any experimental manipulation represents an attempt by the researcher to construct a valid empirical realization of the conceptual independent variable (Carlsmith et al., 1976). Ideally, the various experimental manipulations: (1) diff er from each other on the conceptual independent variables that they are supposed to represent, and (2) do not diff er on other aspects that might (theoretically) infl uence responses on the dependent variable. For example, an experimental study to test the theoretical hypothesis that violent video game content increases the likeli- hood or amount of physical aggression minimally requires two conditions that diff er in the amount

of violent content (one should have a lot, the other should have none). Some early experiments (which shall remain nameless) did not successfully do this, in part because the researcher used an inappropriate defi nition of “violent content.” Th at is some experi- ments used violent video games in the nonviolent control condition, because the researcher defi ned violent content as content that contained blood and gore, rather than the now-accepted defi nition of violent content as content in which player–charac- ters try to harm other game characters. Also, recall our earlier comments on the failure of the Williams and Skoric (2005) “experiment” to appropriately manipulate exposure to violent video games.

Th e second requirement of the ideal case, that the relevant comparison conditions do not diff er in aspects that might infl uence the dependent variable, also requires careful attention. We know, for example, that variables such as excitement, arousal, and frus- tration can sometimes increase aggressive behavior in some circumstances. Th erefore, such extraneous factors (extraneous to the violent content T physical aggression hypothesis) need to be controlled.

Th ere are two basic strategies for controlling such extraneous factors. One is to pretest several possible empirical realizations of the independent variable on the extraneous factors (using the same participant population as will be used in the main experiment), and then choose those that meet the theoretical and empirical requirements for use in the main experi- ment. For example, in a pilot study one could use several violent and several nonviolent video games, measure excitement, arousal, and frustration, and then select games that diff er in violent content but that do not diff er in induced excitement, arousal, and frustration for use in the main experiment (e.g., Anderson et al., 2004).

Th e second strategy is measure the extraneous fac- tors in the main experiment on the main participants, and then statistically control for those factors in anal- yses of the violent content manipulation on aggres- sive behavior. If it turns out that an extraneous factor (e.g., excitement) doesn’t contribute signifi cantly to aggressive behavior, then one doesn’t need to control for it. However, if it does relate to aggressive behavior, then one can use the measure of excitement as a cova- riate in the statistical analysis. And of course, both of these strategies can be used in the same program of research as has been done in many of the meth- odologically strongest studies (e.g., Anderson & Dill, 2000, Study 2; Anderson et al., 2004).

It is important to keep in mind that the com- parison conditions still will likely diff er in other

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128 Research Methods, Design, and Statistics In Media Psychology

ways. Th is is especially likely in media psychology studies and any domain in which the stimuli tend to be selected rather than created. Although beyond the scope of this article, one solution to this issue is to use multiple stimuli (e.g., games) of each type (violent and nonviolent) such as in Anderson and Carnagey’s (2009) study of violent and nonviolent competitive games. In addition, if one uses many examples of each game type, one could use random eff ects statistical models rather than the more com- mon fi xed-eff ect models.

Yet another approach to equating experimental conditions on extraneous factors is to use the same stimuli with changes only to the violent content. For example, Carnagey and Anderson (2005) repro- grammed the violent video game Carmageddon (a driving game in which one gets points for running over pedestrians) so that in the nonviolent condi- tion there were no pedestrians to kill. Similarly, Anderson et al. (2004, Experiment 3) modifi ed the violent video game Marathon 2 . However, even this approach guarantees that the comparison games will be “equal” on relevant extraneous factors. So, it is useful to control such factors by pretests, mea- suring and statistically controlling for them in the main study, or both.

Finally, combining the eff ects of well-designed experiments in a meta-analysis also helps eliminate alternative explanations based on potential extrane- ous factors coinciding with the experimental vari- able of interest. Because diff erent researchers have used a wide range of violent and nonviolent video games in their experiments, the likelihood that some extraneous factor existing in all or most of them is quite remote. Indeed, if one comes up with a plau- sible alternative explanation that might account for some of the results, one can test that alternative in a meta-analysis. For example, some gamer/scholars have proposed that the violent video game eff ect in experimental studies only works with the competi- tive reaction time task. Anderson et al. (2010) tested this alternative hypothesis, and found that the aver- age eff ect size of such CRT studies is actually slightly smaller than the average eff ect found in the other experimental studies of violent video games, thus disproving that alternative explanation.

It is important to keep in mind that this type of reasoning, development, and assessment of experi- mental manipulations, and theory testing can and should be done in other media psychology domains, once suffi cient numbers of studies are available. We use the media violence domain as an example because it is large, has had many excellently designed

and executed studies published, has had a number of poorly executed studies published, and also because we are most familiar with this domain.

Using Poor or Inappropriate Measures Diff erences in the direction of fi ndings and in

eff ect sizes can sometimes be a result of diff erent measures of the independent, the dependent, or the control variables in particular studies. To detect eff ects and accurately assess their magnitude, reliable and valid measures need to be used. For example, the meta-analysis by Anderson et al. (2010) showed that the way one measures violent video game expo- sure in nonexperimental studies signifi cantly infl u- ences the magnitude of eff ects found. Using specifi c measures of the length of exposure and violence levels in particular games (Anderson & Dill, 2000) yielded larger eff ect sizes than did other methods of assessing exposure to violent games.

Another potential pitfall involves using depen- dent measures that don’t fi t the research context. Th is can happen in multiple ways. For example, some short-term experimental studies of violent media eff ects have used traitlike measures as the depen- dent variable. Such traitlike measures essentially assess how frequently one has behaved aggressively in recent years. How can a 15-minute experimental manipulation today (violent versus nonviolent video game) infl uence how often one has behaved aggres- sively before today? Another version of this problem concerns what is an appropriate measure of aggres- sion. Is having an argument with a friend or spouse a measure of aggression, as claimed by Williams and Skoric? Is the proximal intent of such an argument to harm the friend or spouse? In most cases, the answer is probably “no,” so this is a very poor measure of the conceptual variable “aggression.” It is even more inappropriate in a study designed to test the eff ects of violent video games on the kinds of aggression most frequently modeled in violent games, physical aggression. And it is even more inappropriate when the most of the participants don’t have a spouse with which to argue (Williams & Skoric, 2005). Certainly, there is evidence that school children arguing with teachers and other authority fi gures is one valid aspect of antisocial tendencies, but that is very diff erent from using arguments with friends/ spouses as a measure of video game–induced aggres- sion in adult participants.

Often, the most important fi ndings are acquired by using multiple measures. For example, in a lon- gitudinal study of media violence eff ects, Anderson, Gentile, and Buckley (2007) obtained multiple

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measures of children’s aggressive behavior through self-report, peer nominations, and teacher nomina- tions. Sometimes such measures can be usefully com- bined into an overall index of aggression (Study 3).

An interesting recent direction in the media psy- chology fi eld concerns examining neurocognitive bases of media eff ects through techniques such as event-related brain potentials (ERPs) and functional magnetic resonance imaging (fMRI). For example, Bartholow, Bushman, and Sestir (2005) showed that habitual violent video game players have reduced amplitudes of the P300 component of the event-related brain potential while viewing violent images and that this reduced response predicts more aggressive behavior. Research by Kronenberger et al. (2005) has shown similar fMRI and Stroop attention diff erences in conduct disordered and high violent gaming adolescents (Mathews et al., 2005; Weber et al., 2006). Similarly, Bailey, West, and Anderson (2010, 2011a,b) have used ERPs, Stroop tasks, and photo rating tasks to compare high and low action gamers on their attention control and emotional reactions to violence. Relative to low gamers, high gamers show defi cits in proactive control, other more general attention defi cits, and brain activa- tion patterns suggesting desensitization to violent images. Overall, these various fi ndings, each using multiple ways to measure theoretically related pro- cesses, provide converging support on desensitizion and decreased empathy as results of media violence exposure (Mullin & Linz, 1995; Dexter, Penrod, Linz, & Saunders 1997).

However, obtaining multiple measures some- times comes at a cost. A potential pitfall stems from the fact that measurement of one variable of interest may infl uence the values of other related variables. Similar to the Heisenberg uncertainty principle in physics, the psychological uncertainty principle states that that measurement of one variable may change the psychological processes at work and thus change the values of downstream variables (Lindsay & Anderson, 2000). For example, measuring attitudes toward aggression after watching a violent movie may reveal the purpose of the study to participants and infl uence their later behavior. Th e possibility of such an infl uence can be controlled by experimen- tally varying the order in which variables are assessed and then testing for order eff ects. If signifi cant order eff ects are found, this shows that the psychological uncertainty principle is at work. To test for media- tion eff ects in such cases, multiple experiments need to be conducted, each of which assesses one of the key variables (Lindsay & Anderson, 2000).

Signifi cance Testing A problematic statistical practice employed

in many media violence studies consists of using null-hypothesis signifi cance testing without report- ing eff ect sizes and confi dence intervals. Th is widely used approach (in psychology as well as other social sciences) has been the subject of much criticism (Rozeboom, 1960; Cohen, 1994; Kirk, 1996; Th ompson, 1998; Bonett & Wright, 2007). Unfortunately, null hypothesis tests are often misin- terpreted (Nickerson, 2000). Failing to reject the null hypothesis is frequently viewed as proof that the null hypothesis is true, whereas rejection of a null hypoth- esis is taken as evidence of a practically and theoreti- cally relevant fi nding (Bonett & Wright, 2007).

In the media violence domain, in which eff ect sizes are in a small to medium range (Anderson & Bushman, 2001; Anderson et al., 2010), interesting fi ndings may be overlooked because of Type II errors (failure to reject the null hypothesis when it is true) and may go unpublished. Th e absence of signifi cant diff erences found in particular studies are some- times misinterpreted as evidence that there indeed are no eff ects, without taking into account other possible reasons for the nonsignifi cant result (e.g., inadequate control of extraneous variables, inappro- priate overcontrol of mediating outcome variables, unreliable measurement techniques, and small sam- ple sizes). A wide confi dence interval immediately indicates to the reader that the sample estimate may not be reliable and may be quite diff erent from the true eff ect in the population. Meta-analytic tech- nique can then be used to combine such studies and enable researchers to draw fi rmer conclusions.

Th e American Psychological Association (APA) Task Force on Statistical Inference advocated for an improvement of statistical practices by including eff ect size estimates along with confi dence inter- vals more than 10 years ago (Wilkinson & the Task Force on Statistical Inference, 1999). However, these changes have not yet been widely implemented in psychology journals (Finch et al., 2004; Cumming & Finch, 2005; Cumming et al., 2007). As the APA Publication Manual now strongly encour- ages authors to include confi dence intervals (APA, 2011), it is our hope that this change in reporting styles will reduce miscommunication and misun- derstanding in the media violence literature.

Eff ect Size Interpretation Media eff ects research has sometimes been

criticized on the grounds that eff ect sizes found in most studies are small and are thus unimportant

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(Ferguson & Kilburn, 2010). However, it is danger- ous to assume that just because most studies fi nd small eff ect sizes, media do not have important prac- tical consequences.

Th e eff ect sizes found in most media eff ects stud- ies conform to the range of eff ect sizes usually found in social psychology studies in general (Richard, Bond, & Stokes-Zoota, 2003). Because complex behaviors are determined by a multitude of personal and situational factors, no one causal factor by itself can explain more than a small proportion of the vari- ance in a particular behavior. Because of this, some authors suggest that eff ect size conventions should be revised so that r = .1 is small, r = .2 is medium, and r = .3 is large (Hemphill, 2003).

Some of the eff ects found in media psychol- ogy are, in fact, considerably larger than eff ect sizes found in medical research that are seen as extremely important (Bushman & Huesmann, 2001). For example, the eff ect of violent video games on aggres- sion outweighs eff ects of substance use, abusive par- ents, and poverty (U.S. Department of Health and Human Services, 2001) and is larger than the eff ects of passive smoking on lung cancer and the eff ect of calcium intake on bone mass (Anderson, 2004). Furthermore, because a large proportion of the pop- ulation is exposed to violent mass media, even small statistical eff ects can have important societal conse- quences (Abelson, 1985; Rosenthal, 1986; Prentice & Miller, 1992; Anderson et al., 2003).

Communicating Research Findings and Methodology to the General Public

An important role for many scientists involves disseminating knowledge gained from their research not only among the scientifi c community, but also among the general public. Indeed, several American Psychological Association presidents have urged its members to “give psychology away” to the public. One of the goals of media psychology as an applied fi eld is to benefi t society with its insights, a goal that requires eff ective communication between media researchers and the media, public policy makers, parents, teachers, and so on. Unfortunately, the sci- entifi c community has not always been successful in communicating research fi ndings to the general pub- lic. For example, a content analysis of research papers and newspaper articles conducted by Bushman and Anderson (2001) revealed a large disparity between news reports and the actual state of scientifi c knowl- edge concerning media violence eff ects.

Researchers often do not see themselves as pub- lic educators. Diff erences in terminology and basic

assumptions between scientists and nonscientists can impede eff ective communication and contrib- ute to misinterpretation of scientifi c fi ndings in the general public. Additionally, public involvement comes with costs (e.g., time, eff ort, money, and personal costs)—a price that researchers frequently are unwilling to pay. Th e costs are especially large when the research suggests that certain products are harmful (e.g., lead, tobacco, violent media), and when there is a large and committed group of product users and industry leaders who are highly threatened (e.g., by threats to self-image, profi ts) by the research fi ndings. Th ere is a long history of industries in the United States spending large sums of money attacking research fi ndings that they don’t like, attacking the integrity or scientifi c reputations of researchers whose work discovered the harmful eff ects. Th ere is such a history in the television and fi lm violence domain. For example, both Albert Bandura and Leonard Berkowitz were excluded from key governmental review panels on media violence because of pressure brought by the entertainment media industry. Similar attacks are widespread in the video game violence domain, and with the rise of the Internet, the personal attacks on and outright fabrications about key researchers has taken on a new dimension. One need only Google the names of the leading video game violence researchers to fi nd such fabrications about them and their research.

However, it is our belief that the benefi t of eff ec- tive communication between scientists and the gen- eral public outweighs such costs. Th erefore, a fi nal task of successful researchers in the fi eld of media psychology is to be able to clearly and eff ectively inform general audiences concerning their fi ndings and methods used to obtain them.

Notes 1 . We fi nd it ironic that the lead author of that study,

Dmitri Williams, in 2005 criticized the experimental study reported in Anderson and Dill (2000) for selecting a violent and a nonviolent game based on pilot testing of several games that included self-reported ratings on a variety of dimensions and physiological measures of arousal. Williams apparently didn’t like the two games chosen because they didn’t fi t his intuitions about excitement levels induced by the games. What he fails to note is that: (1) Anderson and Dill reported that there were diff erences in self-reported excitement; (2) there were not diff erences in heart rate or blood pressure; (3) excite- ment was statistically controlled in the main experiment; (4) the excitement did not infl uence the results of the main exper- iment. Furthermore, in science when intuition confl icts with empirical data, it is intuition that has to yield. In fact, the Anderson and Dill studies set the methodological standard for

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later video game studies (both experimental and correlational), and their basic fi ndings have been replicated numerous times by numerous research teams from many countries around the world. We are not saying that this early experimental study was perfect; no single study is perfect. In fact, several more recent studies from our and other labs are, in our view, stronger methodologically; they built on the insights and knowledge gained from the earlier study.

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Why It Is Hard To Believe That Media Violence Causes Aggression

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Print Publication Date: Dec 2012 Subject: Psychology, Social Psychology Online Publication Date: Jan 2013 DOI: 10.1093/oxfordhb/9780195398809.013.0009

Why It Is Hard To Believe That Media Violence Causes Aggression

, , and

The Oxford Handbook of Media Psychology Edited by Karen E. Dill

Abstract and Keywords

Research studies on how media violence influences aggressive and violent behaviors face unusual hurdles in having an impact on the public, journalists, and even other scientists. Despite the existence of compelling empirical evidence that media violence causes in­ creased aggression in the observer or game player, intelligent people still doubt the ef­ fects. A fundamental reason is that the outcomes of such research have implications not only for public policy, but also for how one views oneself. Through several well-under­ stood psychological processes, this leads to many people denying the results of the scien­ tific research. There are four psychological processes that together can account for most denials of media violence effects: (1) the need for cognitive consistency; (2) reactance; (3) the “third-person effect”; and (4) desensitization. This chapter illustrates how these processes lead to disbelief. Finally, it offers conclusions and ideas for future directions of how research may contribute to public opinion and public policy.

Keywords: media violence, psychological processes, public perceptions of research, violent video games

Long before the introduction of video games into the everyday lives of children, the ques­ tion of whether exposure to violence in the mass media makes the viewer more violent was being widely debated. It was debated with regard to oral and written communica­ tions even in antiquity; it was debated with regard to movies when they were introduced; and it became a major topic of research and debate with the emergence of television as a fundamental part of every child's development by the end of the 1950s. Both the research and the debate have accelerated, however, as modern electronic recording and communi­ cation media make movie and television portrayals of violence available to everyone everywhere, and as the modern electronic video game has become a central part of every child's life. We now have reached the point at which not only are all children being social­ ized as much by electronic media as by parents and peers, but also today's researchers, policy makers, and debaters have mostly now been raised in these environments them­ selves.

L. Rowell Huesmann Eric F. Dubow Grace Yang

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To the current authors, the research of the last 50 or so years is compelling in demon­ strating at least two cause–effect relations about media socialization: (1) short exposures of almost anyone to violent scenes or playing violent games cause an increase in the like­ lihood of behaving aggressively immediately afterward; and (2) habitual exposure to vio­ lent scenes or playing violent games changes children's developing brain structures to cause an increase in the likelihood of behaving more aggressively even many years later. These statements are true of violence observed in the family, neighborhood, or school or true of violent games played with peers, and they are true of violence in the mass media or electronic games. There is a clear consensus of opinion among most scholars who actu­ ally do research on the topic (p. 160) of the truth of these statements. Surveys have shown that more than 80% of those doing research on the topic have long ago concluded from the evidence that media violence is causing aggression (Murray, 1984). Most major health professional groups and governmental organizations have issued statements citing expo­ sure to media violence as one cause of youth violence (Eron, Gentry, & Schlegel, 1994; Joint Statement of Congress, 2000; Anderson et al., 2003; American Academy of Pedi­ atrics, 2009). Two Surgeon Generals of the United States (in 1972 and 2001) have warned the public that media violence is a risk factor for aggression. For example, in March 1972, then Surgeon General Jesse Steinfeld told Congress:

…it is clear to me that the causal relationship between [exposure to] televised vio­ lence and antisocial behavior is sufficient to warrant appropriate and immediate remedial action…there comes a time when the data are sufficient to justify action. That time has come. (Steinfeld, 1972)

Yet, despite this conclusion supported by prestigious individuals and most scientific orga­ nizations, and despite the sizable body of empirical evidence accumulated over several decades confirming the negative effects of the consumption of media violence, a relative­ ly small number of critics continue to challenge this conclusion, disregarding the weight of empirical data, and further disregarding the theoretical explanations underlying the ef­ fects. Many dissenters obviously believe passionately in what they have concluded and write prolifically and compellingly about it. The quantity of such writing, in which the flaws are often difficult for nonexperts to discern, eventually may influence informed pub­ lic opinion and give even those policy-making organizations that have opposed media vio­ lence second thoughts about their positions. For example, we note that recently, the American Psychological Association, which had previously issued statements opposing media violence, decided against submitting an amicus brief on the research evidence to the Supreme Court, which was hearing a challenge to a California law requiring parental approval for sales of violent video games to minors (Azar, 2010). They explained their de­ cision as follows:

APA was invited to submit a brief, but after a review of the literature, the associa­ tion concluded it was premature to advise the court on research-based links be­ tween violent video games and problematic behavior in the context of a First Amendment challenge. Breckler (APA's Executive Director for Science) explained that although most of the research in this area supports a connection between vio­

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lent games and aggression, there is also some credible research to the contrary, and APA concluded that there was not a basis to weigh in with the Supreme Court given the nature of the relevant research and the legal issues at question. (Azar, 2010, p. 38)

To the authors of this chapter this explanation represents an admission that APA, despite believing that media violence is harmful, is caving in to pressure not to take a position be­ cause of potential legal consequences from either first amendment advocates or those with economic interests in violent media.

The goal of this chapter is to present an explanation of why, when the evidence that me­ dia violence and violent video games causes aggression is as compelling as the evidence supporting many other public health threats, many people who are intelligent and well in­ formed don't accept that there are significant effects on aggression for media violence. It is not our goal to review extensively the empirical literature in this area. A plethora of ex­ tensive reviews have appeared in print in the past decade (e.g., Anderson et al., 2003, 2010; Huesmann & Kirwil, 2007; Bushman & Huesmann, 2011). Nevertheless, we must begin with a very brief summary of the empirical evidence to set the stage for our major argument.

Meta-analyses Demonstrating That Media Vio­ lence Stimulates Aggression When the body of research in an area becomes very large, single studies in the body may be expected to show contradictory results; so a meta-analysis becomes the best way to get an overall grasp of what the empirical evidence shows. Meta-analyses combine effect sizes from large numbers of studies to reach a “best” estimate of the true population ef­ fect size. In 1994, Paik and Comstock conducted the first large comprehensive meta- analysis of the relation between observing media violence and aggressive or antisocial be­ havior. They analyzed 217 studies conducted from 1957 to 1990. The studies included lab­ oratory and field experiments, surveys, and time series designs. The authors found that the average effect size for experiments testing causal effects was r =.40 and for field studies (cross-sectional or longitudinal) was r =.19. These effects sizes, although moder­ ate to small in absolute terms, were highly significant. The effect sizes were significant for college-age students (p. 161) (r =.39), preschoolers (r = .49), 6- to 11-year-olds (r = . 32), and 12- to 17-year-olds (r = .23). The overall effect sizes were also somewhat stronger for males (r = .37) than females (r = .26).

The Paik and Comstock review did not include many studies of video games, but 7 years later Anderson and Bushman (2001, 2002) published meta-analyses that include the ef­ fects of violent video games. Using 280 studies conducted before 2001 across multiple media types (television, movies, video games, comic books, and music), the authors found effect sizes somewhat smaller than those reported earlier by Paik and Comstock (1994), but highly significant. Effect sizes across study designs (laboratory and field experiments,

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cross-sectional and longitudinal studies) ranged from r = .17 to r = .23. Most recently, An­ derson et al. (2010) conducted a meta-analysis of the results from 136 high-quality stud­ ies (yielding 381 effect size estimates) published through 2008 on the relation between

playing violent video games and aggressive behavior, aggressive cognitions, aggressive affect, physiological arousal, empathy/desensitization, and prosocial behavior. This meta- analysis yielded a pattern of effect sizes consistent with their prior meta-analysis showing that playing violent video games causes increases in aggression in the short run and play­ ing violent video games is a risk factor for increased aggression in the long run.

To be fair, not all experiments have shown causal effects of exposure to media violence on aggression and not all field studies have shown positive longitudinal effects of exposure to media violence on aggression. Although many such studies have glaring flaws, some seem to be very well done; for example, a longitudinal study by von Salisch, Vogelgesang, Kristen, and Oppl (2001) that indicated that the relation between aggression and violent video game play in third and fourth graders is more caused by aggressive children liking violent games than children who play violent games becoming more aggressive. From a theoretical standpoint, it is quite plausible that there would be effects in both directions (Huesmann et al., 2003; Slater, Henry, Swaim, & Anderson, 2003); however, it is unusual in this body of research not to have a longitudinal effect from prior exposure to media vio­ lence to subsequent aggression. How threatening should be the effect of any one such study to the overall conclusion that media violence stimulates aggressive behavior in the short run and in the long run? This is why the meta-analyses are so important. Meta- analyses serve the important function of statistically aggregating the results of many di­ verse studies—some with positive effects, some with negative effects, some with no ef­ fects—to reach an overall conclusion. And the conclusion shown by the vast majority of such meta-analyses is that media violence causes increases in aggression. The few dis­ senting meta-analyses (e.g., Ferguson, 2007a,b), Ferguson & Kilburn, 2009) have been conducted by the dissenters we discuss in the following pages, and have flaws described in detail elsewhere (Anderson et al., 2010).

Theoretical Explanations for Media Violence Effects Empirical evidence by itself, however consistent and powerful, should not be enough to convince scientists or the public that a particular environmental substance is dangerous. One needs a process-model explanation of how it exerts its dangerous effect. For more than four decades, scientists have been building a consistent psychological process model of why exposure to violence stimulates aggression both in the short run and in the long run (Eron et al., 1972; Bandura, 1977; Huesmann, 1988, 1998; Bushman & Huesmann, 2001; Huesmann et al., 2003; Huesmann & Kirwil, 2007). We now know why and how it happens. In the short term, priming, mimicry, and excitation transfer account for the ef­ fects. In the long term, observational learning of aggressive scripts, schemas, and beliefs and emotional desensitization account for the effects.

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Short-Term Processes

Priming explains relatively short-term underlying processes by which exposure to media violence can incite aggression. The logic of priming is based on cognitive and neurologi­ cal perspectives that consider human memory as an associative network of scripts or ideas representing semantically related thoughts, feelings, and behavioral tendencies (Fiske & Taylor, 1984; Berkowitz, 1989, 1993). Priming from observing violence refers to the neurological fact that related violent thoughts, emotions, and concepts residing in memory are automatically activated when violence is observed. These primed or activat­ ed thoughts and emotions bias the processing and interpretation of subsequently encoun­ tered situations, even without one's perception of this influence (Bargh & Pietromonaco, 1982). Empirical studies reveal that violent media content activates aggressive scripts in one's memory, and these aggressive scripts in turn increase the likelihood of subsequent hostile responses to certain situations, (p. 162) especially those involving interpersonal conflicts or frustration (Bargh & Pietromonaco, 1982). In addition, the mere presence of objects associated with violence, such as weapons, primes aggressive responses (Berkowitz & LePage, 1967; Anderson, Benjamin, & Bartholow, 1998; Payne, 2001).

Activation and processing of aggressive scripts occurs even without one's conscious awareness, and, when repeated, eventually makes aggressive scripts chronically accessi­ ble (Huesmann & Kirwil, 2007, p. 549). This increase in chronic accessibility is owing to a “lowered threshold of activation,” which makes the construct more easily activated by other stimuli for at least a short period (Bushman, 1995, p. 538). Thus, although the prim­ ing effect is considered relatively fleeting compared with enduring social learning effects (described in the following pages), because aggression-activated thoughts become chroni­ cally accessible through repeated priming, violent media consumption can have a consid­ erable cumulative impact on increasing the likelihood of aggressive behavior through the priming process.

Another short-term process is mimicry, which explains why exposure to violent media im­ mediately precipitates aggressive behavior, especially among young children. Neurophys­ iological research on automatic imitation has revealed that humans have an innate ten­ dency to mimic any behavior they observe (Meltzoff & Moore, 2000; Hurley & Chatter, 2004; Rizzolatti, 2005). Applied to the violent media, aggressive actions performed by me­ dia heroes can be immediately mimicked by young children, especially if children per­ ceive the observed model to be similar to themselves and if the model's behavior is rein­ forced (Bandura, 1977). Short-term imitation can occur after a single observation of an action without elaborate cognitive processing (Huesmann, 1998; Bushman & Huesmann, 2006). The observation and imitation of a specific aggressive behavior can lead to acquisi­ tion of more coordinated aggressive scripts for future behavior (Huesmann, 1988, 1998).

The third short-term psychological process accounting for why exposure to media vio­ lence can temporarily increase aggression relates to arousal and excitation transfer. Exci­ tation transfer is the idea that physiological arousal dissipates slowly and if two arousing events are separated by a short amount of time, some of the arousal caused by the first

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event may pass on to the second event (Zillmann, 1983, 1988). In general, observing vio­ lent media creates a sense of excitement in most people. As Bandura (1983) explained, this emotional arousal can increase the likelihood of aggressive action, particularly if the person conceives his or her aroused experience as negative, such as frustration or anger. Other types of arousal such as sexual or physiological arousal (by exercise) are thought to facilitate aggressive reaction during the subsequent event. A number of experimental studies have reported that emotionally or physiologically aroused individuals are especial­ ly prone to be aggressively stimulated by violent scenes (e.g., Bryant & Zillmann, 1979; Zillmann, Bryant, Comisky, & Medoff, 1981).

Long-Term Processes

Unlike priming, mimicry, and arousal, whose effects are relatively short lived, observa­ tional learning of aggressive scripts and schemas for behavior includes specific mecha­ nisms through which viewing violent media increases aggression in the long run. Accord­ ing to Huesmann, “a script serves as a guide for behavior by laying out the sequence of events that one believes are likely to happen and the behaviors that one believes are pos­ sible or appropriate for a particular situation” (1998, p. 80). Huesmann (1988, 1998) de­ veloped a cognitive processing model to account for one's own and others’ actions during social situations and how exposure to violence might influence behavior. Huesmann's model provides a detailed explanation of how an individual develops aggressive problem- solving behavior through a four-step sequential process. The four steps involve percep­ tion and interpretation of environmental cues, activation of retrieved scripts, evaluation of scripts against normative beliefs, and interpretation of environmental responses (Hues­ mann, 1998).

To begin with aggressive children have a larger repertoire of aggressive scripts than nonaggressive children and thus are more likely to call on these scripts in social conflict situations. Aggressive scripts are acquired initially mainly though observational learning of others behaving aggressively in the child's environment including the mass media. They are then cemented in place through reinforcement of the use of aggressive scripts that achieve desired outcomes. Aggressive children will seek out environments that are consistent with their aggressive scripts (e.g., violent media, aggressive peers, opportuni­ ties to use aggression), and aggressive children can create their own aggressive environ­ ments. Thus, a downward spiral of increasing aggression can occur (Slater et al., 2003). The maintenance of an aggressive script also depends on how frequently and competently the child rehearses it (Huesmann, 1988); rehearsal of observed information enhances

(p. 163) its connectedness in memory, thereby making it more accessible (Klatzky, 1980). Thus, frequent enactment of aggressive scripts (even through fantasizing) should make their retrieval more likely (Huesmann, 1998). Huesmann also contends that once in place, aggressive scripts are relatively resistant to change, and therefore chronically influence aggressive behavior throughout development. However, an activated script may remain unused if it is evaluated as negative or inappropriate given the situation. Huesmann posits that whether children act out an aggressive script depends on the self-perception

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that the script is doable, that enacting the script will lead to desired consequences and that the script is socially acceptable.

Two cognitive schemas that affect whether aggressive scripts will be selected and em­ ployed include the child's “normative belief about aggression” and “world views” (Huesmann & Guerra, 1997; Anderson & Huesmann, 2003; Huesmann & Kirwil, 2007). Through inferences drawn from observational learning, children develop norma­ tive beliefs about what aggressive behaviors are socially appropriate and develop schemas about how violent they perceive the world to be in general (world schemas). Like aggressive scripts, normative beliefs and world schemas are learned through observation of parents, peers, and media characters (Huesmann, Lagerspetz, & Eron, 1984; Miller, 1991; Henry et al., 2000). A child who is repeatedly exposed to violence in real life or the mass media (including video games) will perceive the world to be a more violent place (i.e., “have hostile attributional biases” or “perceive a mean world”) (Signorielli, 1990; Gerbner, Gross, Morgan, & Signorielli, 1994), and also may think that it is socially accept­ able to resolve any encountered conflict with violence. Consequently, the child is more likely to enact aggressive scripts in response to perceived provocation.

Thus, through such observational learning and enactment of aggressive schemas, scripts, and beliefs, children not only learn specific aggressive behaviors, but also internalize the values, beliefs, and attitudes that are associated with the process and context of their learning (Huesmann, 1998). Accordingly, as Huesmann and Kirwil (2007) describe, this process can result in “habitual modes of [aggressive] behavior,” which last a long time (p. 552). Consistent with this theoretical model, Huesmann et al. (Eron, Huesmann, Lefkowitz, & Walder, 1972; Huesmann, Moise-Titus, Podolski, & Eron, 2003) found that higher levels of childhood exposure to television violence significantly predicted higher levels of aggressive behavior in adulthood (e.g., crime records, traffic tickets, spouse abuse, child hitting), even when other relevant individual and social factors (e.g., educa­ tion, early parenting, parent aggression, socioeconomic status) were statistically con­ trolled.

Emotional desensitization is another psychological process with long-term implications for aggressive behavior. Desensitization to media violence refers to “emotional habitua­ tion” or the gradual increase in emotional tolerance for violence and the reduction of the unpleasant physiological responses to violence that occur with repeated exposures to vio­ lence (Carnagey, Anderson & Bushman, 2007; Krahe et al., 2011). Thus, by repeatedly viewing violence over an extended period of time, a person becomes less affected by the unpleasantness associated with violence, both emotionally and physiologically, and as a result, he or she may be less inhibited about behaving aggressively. According to re­ searchers, then, the risk of consuming extensive amounts of violent media is that the like­ lihood of having aggressive thoughts and acting aggressively increases when depictions of violence no longer cause emotional distress (Huesmann et al., 2003). It should be not­ ed that, like precipitating effects of priming and excitation transfer, this desensitization

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also is experienced as a “natural” and “unconscious” process, and the enduring effects on aggressive attitudes and behaviors also develop outside of one's conscious awareness.

Disputing Media Violence Effects Given the compelling amount of empirical data summarized in the preceding and the con­ sistent theoretical explanations for the effects reviewed there, one must wonder how an informed layman, much less an informed social scientist, can dispute the conclusion that media violence causes aggression. Why do “disbelievers” continue to adamantly hold their views in the light of the evidence? Of course, it is amazing to most of us what many people do and don't believe. Large portions of the American population still believe that President Obama was not born in the United States (although a significant portion of peo­ ple who say “not in the USA,” when asked where he was born, then say “in Hawaii”). Many people also believe that all sorts of dietary substances improve or worsen their health when there is absolutely no evidence that the effects exist. But there should be a difference between what the general public may accept about influences on behavior and health and what well-read “public intellectuals” and especially those trained in science and health (p. 164) should accept. Highly educated people, especially those with training in social science research, should be able to evaluate the quality of media violence re­ search and understand what conclusions follow from the preponderance of the evidence. If they are not doing so in a way that represents the conclusions that can be drawn from this body of evidence, it may be that other psychological factors within them, perhaps un­ consciously, are influencing their beliefs.

The remainder of this chapter describes some underlying psychological processes that may motivate (consciously or unconsciously) the disbelievers to reject the evidence drawn from the vast research relating media violence and aggression. We offer theory-driven ex­ planations of the critics’ denials. Then we offer conclusions and ideas for future direc­ tions of how research may contribute to public opinion and public policy.

Some Flawed Arguments Used to Discredit Me­ dia Violence Research First, however, before we describe the psychological processes that lead to disbelief, we need to briefly address the surface arguments presented by disbelievers as reasons for their disbelief. One common argument is that the evidence accumulated to date has pro­ vided little or no indication of a causal effect of media violence on viewers’ aggressive be­ havior (e.g., Ferguson, 2009b). Typically, when making this argument, disbelievers ignore experiments and focus on one-shot field studies that, indeed, do not provide evidence of causation. However, other critics (or the same critics at other times) focus only on experi­ ments and argue that the results of such studies are unimportant because they are done in an artificial laboratory setting using measures that do not represent real-world aggres­ sion. (Typically, when making this argument, disbelievers ignore one-shot and longitudi­

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nal field studies.) Perhaps the most frequent methodological criticism is that experimen­ tal studies are contaminated by the artificiality of the viewing situations and laboratory settings (Howitt & Cumberbatch, 1975), and thus the findings are not generalizable to the “real world.” This lack of generalizability, however, is not critical if the primary goal of the experiment is to test a causal hypothesis, which requires demonstrating only that ma­ nipulating the independent variable can cause the changes in dependent variable. Crano and Brewer (2002) referred to this goal as “psychological realism,” which represents the extent to which “the psychological processes that occur in an experiment are the same as psychological processes that occur in everyday life” (p. 110). As the authors argue, al­ though an experimental setting may bear little resemblance to real-life experiences, or what they call “mundane realism,” the experimental operations still may capture impor­ tant underlying processes that are highly representative of those that reflect events in the real world. For this reason, establishing experimental realism would be more imperative for validity of true experimental results. To achieve this, the researcher must ensure the internal validity of his or her research by controlling for the effects of confounding vari­ ables that might contaminate the findings.

Of course, the good scientist should combine evidence from well-controlled internally valid experiments with more externally valid field studies in which the criterion measures assess severe forms of actual physical aggression and violence such as fighting, hitting, and bullying at school (McLeod, Atkin, & Chaffee, 1972; Buchanan, Gentile, Nelson, Walsh, & Hansel, 2002; Huesmann et al., 2003; Lee & Kim, 2004). The best way to do this is not to “cherry-pick” selected studies that fit your preconceived ideas, but to conduct a meta-analysis that combines the effects of all studies. When this is done, as described, the comprehensive meta-analyses show positive and very significant effect sizes.

A second approach used by those who want to argue against positive effects has been to combine small truths and minor accurate criticisms with the “big lie” that there are no ef­ fects. For example, Freedman (2002) found a number of studies on exposure to media vio­ lence in which he could correctly point out specific flaws (e.g., experimenter demands, confounding factors, poor aggression measures). As with any other large body of re­ search in the social sciences, there are studies with methodological flaws—some minor, some major. After reviewing only this subset of studies, Freedman concluded that studies finding positive effects are flawed, and therefore the conclusion that media violence pro­ motes aggressive behavior is not justified. However, as Cantor (2003) stated in her re­ view of Freedman's book, although Freedman raised valid questions on certain issues (e.g., the exaggerated number of published articles on violent media and aggression), he failed to differentiate between “the lack of a statistically significant difference” and “a finding of no effect” (p. 468). Moreover, Huesmann and Taylor (2003) pointed out that Freedman's study-by-study analysis was based on a theoretical vacuum, giving no refer­ ence to “a psychological theory that has been advanced to explain why observation of vio­ lence engenders aggressive behavior” (p. 119). The authors (p. 165) also argued that Freedman's criteria for evaluation were not consistent across studies, leading to biased

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and inaccurate readings of the findings of extant literature (see reviews by Cantor, 2003; Crooks, 2003; Huesmann & Taylor, 2003).

A third approach to arguing against the conclusion that viewing media violence causes in­ creased aggression has been to argue that viewing media violence reduces aggression. For example, Fowles (1999) and Jones (2002) have contended that watching violent televi­ sion serves as an outlet for natural violent impulses and therefore decreases aggression in the viewer. This “catharsis” view, perhaps because it draws on Freudian thinking, is of­ ten accepted by mass media and pop psychologists who then encourage angry people to vent their feelings through various aggressive and violent activities (e.g., hitting a pillow or punching bag). Despite its popularity, the catharsis hypothesis has no significant em­ pirical support (Geen & Quanty, 1977; Bushman, Baumeister, & Phillips, 2001). For exam­ ple, one study showed that people who hit the punching bag after reading a procathartic message subsequently became more aggressive than people who read an anticathartic message (Bushman, Baumeister, & Stack, 1999).

A fourth argument frequently offered by disbelievers is that media violence researchers have failed to consider alterative causes of aggression such as personality traits, evolu­ tion, or domestic violence (Freedman, 2002; Olson, 2004; Savage, 2004). However, no reputable media violence researcher has ever argued that aggressive behavior is caused only by media violence. In fact, it has been shown in several longitudinal studies that the effects of frequent viewing of violent television shows on later aggression are significant even after statistically controlling for other significant causes of aggression such as the child's prior level of aggression, the child's intelligence or academic achievement, and family of origin socioeconomic status (e.g., Huesmann & Eron, 1986; Huesmann et al., 2003).

Still another badly flawed argument by the disbelievers has been that one should not be­ lieve that media violence has an effect because as violent video game sales and violent movie sales have increased in the country in recent years, homicides have decreased. This argument would only make sense if one believed that the only cause of homicides was media violence. It makes as little sense as the counterargument that because homi­ cides increased in the 1960s and 1970s, exactly 15 years after most people got televi­ sions, homicides must be caused by television violence.

A remaining common criticism offered by disbelievers, however, has been that the agreed-on effect sizes (around .15 to .20 for field studies and .30 to .35 for experiments) are too small to be “socially significant” even if they are statistically significant. However, a number of scholars (e.g., Abelson, 1985; Rosenthal, 1986; Anderson et al., 2003) have argued that these effect sizes really are socially significant. These researchers pointed out that although correlations around .20 may seem to explain only small proportions of variance, the squared correlation is the wrong metric with which to evaluate the social significance of a public health threat. Effect sizes of r = .20 are very socially meaningful because a very large population is exposed to the risk factor, the effects are likely to accu­ mulate with repeated exposure, and no other explanatory factors have much larger effect

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sizes. Furthermore, a correlation of .20 amounts to a shift in odds from 50/50 to 60/40 for a dichotomous outcome that is binomially distributed. Such a shift in odds for violence is certainly socially significant. Additionally, Bushman and Huesmann (2001) have shown that such effect sizes are as large as or larger than public health threats such as the link between passive exposure to cigarette smoke and lung cancer, condom use and HIV risk, and calcium intake and bone mass. Recently, Ferguson (2009a) has tried to argue that these kinds of comparisons are inappropriate because medical effect sizes are really usu­ ally underestimates of true effect sizes. He argues that most effect sizes for therapeutic drugs are computed by examining samples of both well and sick people and therefore un­ derestimate the true effect sizes because, of course, the drug won't do anything for peo­ ple who are not sick. However, he misses the fact that his argument is irrelevant to the computation of effect sizes for public health threats like smoking and asbestos, which do not just affect ill people.

Psychological Processes Driving the Denial of Media Violence Effects Given the existing empirical data, existing theoretical explanations for it, and weakness of the counterarguments reviewed in the preceding that the disbelievers have offered, one has to remain puzzled about why the disbelievers disbelieve. In 2003, Huesmann and Tay­ lor first addressed this issue and offered several psychological explanations for why the disbelievers are so adamant in their disbelief. Here, we present and expand on those ex­ planations.

(p. 166) The Need for Cognitive Consistency

First, we argue that a strong drive toward cognitive consistency is behind several reasons for denying the effects of media violence. Cognitive consistency is a remarkably powerful psychological force that affects behaviors and beliefs (Abelson et al., 1968); it requires that new discrepant beliefs be denied or existing beliefs or behaviors be changed when beliefs or beliefs and behaviors are perceived as dissonant. For example, individuals in­ volved in the production or marketing of media violence will find it difficult to believe that viewing violence could be damaging to audiences because that belief would be cognitively inconsistent with their existing behavior of producing or marketing violence. The econom­ ic fact is that violence in entertainment attracts audiences and makes large amounts of money for its purveyors (Hamilton, 1998). Many of the most vociferous disbelievers repre­ sent companies or organizations with vested interests in the money being made by media violence, and a few have been paid by such organizations for what they write (e.g., Fried­ man, 2002). Recognizing that media violence has negative effects would be discrepant with accepting financial benefits that producing violence engenders or arguing against it produces.

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Perhaps equally important, accepting that media violence has negative effects would be discrepant with the purveyors’ images of themselves as doing something valuable for so­ ciety by producing artistic entertainment. This same type of reasoning would apply to those social science consultants to media industries who are paid for their work. Is it pos­ sible to be unaffected when one's identity becomes connected to a group with a view? Furthermore, if the purveyors of violence accepted that violence has serious effects on children, they would have to categorize themselves with other purveyors of products that threaten health (e.g., tobacco), which would produce even more dissonance. Years ago the senior author of this chapter was verbally berated in front of a meeting of the director's guild in Hollywood by director Rob Reiner who was incensed that producers of violent films could be compared with purveyors of tobacco. Perhaps his need for cognitive consistency as a director of violent films required him to deny negative effects of media violence, which made it seem cognitively inconsistent and outrageous to him that he could be compared to the tobacco sellers who clearly distribute harmful substances.

This need to maintain a positive self-image also affects the beliefs and behaviors of younger social scientists who have no financial interest in media violence but have grown up using media violence. The generation that graduated from college in the 1960s and be­ gan to influence social thinking in the 1970s was the first generation for which television was a major socializer. The generation that graduated from college in the 1990s and be­ gan to influence social thinking in the 2000s, however, was the first generation for which video games was a major socializer. We argue that if one grows up developing a self-im­ age that includes “violent television viewer” or “violent video game player” as a major part of one's self-image, the cognitive consistency process will make it very difficult for one to accept that violent television programs or violent video games could cause prob­ lems.

In fact, some of the most regular and vocal dissenters in the social science literature have reported having intense and long-lasting involvement with violent media of one kind or another. They do not hide this information; in fact, the information about their spending years of playing multiplayer violent games, or being “glued” to video game consoles, or playing strategy and war games, or playing violent games like “The Borgias” or “Grand Theft Auto,” or watching lots of violent television and movies, or even information about their writing violent prose is readily available on their web sites. We are not suggesting that these experiences make the dissenters consciously biased. In fact, a number are sci­ entists who strive to be unbiased and value unbiased evaluations. The problem is that cognitive consistency exerts its effect at an unconscious level. Anyone, academic or not, who has an identity associated both with behaving nonviolently and playing violent video games or viewing media violence faces a cognitive inconsistency if he or she accepts the view that those activities make people more violent. This cognitive dissonance creates an unconscious bias against believing that media violence could cause violence that is hard for even them to see.

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In addition to affecting those who have grown up using violent media, the cognitive con­ sistency process can lead to a denial of effects for those who believe strongly in the free expression in the mass media. Many individuals with strong liberal beliefs about free ex­ pression in the mass media also have strong beliefs about society having a duty to protect children. If they accepted the fact that media violence harms children, they might have to rethink their beliefs about balancing freedom of expression with protecting children. It is easier for them to reduce this cognitive dissonance by denying that media violence has ef­ fects than it would be for them (p. 167) to resolve the dissonance by having to alter their beliefs about free expression.

Reactance to Control

Another psychological concept that can account for denial of media violence effects, reac­ tance, is most relevant to the artistic community, including authors, movie makes, and game producers. Most humans at a young age develop an aversion to being controlled and respond to such attempts with reactance, or attempts to regain or increase their own control (Brehm & Brehm, 1981). We suggest that artists, writers, and producers are par­ ticularly susceptible to displaying such reactance when attempts are made to control their creative products, in which their egos are heavily involved. Artists often view state­ ments that their programs or films harm viewers as threats over control. Suppose a re­ searcher tells an artist that a program of his or hers, which is a financial and critical suc­ cess, is bad because it stimulates violence in the children watching it. The artist, rightly or wrongly, consciously or unconsciously, may well interpret this statement as a threat over control. Therefore, a plausible response by the artist who detests control according to reactance theory would be to attack the researcher's thesis that the program has nega­ tive effects on the viewer.

The Third Person Effect

A third psychological phenomenon that is relevant to denial of media violence effects is the third person effect in communication research (Davison, 1983). This is the tendency of people to believe that the mass media may be affecting other people, but it is not affect­ ing them or their children—specifically, in this case, the opinion is that “media violence may affect some ‘susceptible’ people, but it will not affect ‘me’ or ‘my children’ because we are impervious to such influences.” The third person effect is really not a separate psychological process, but probably a consequence of the two processes described in the preceding (cognitive consistency and reactance). First, with regard to reactance, what viewer wants to admit that he or she is being influenced by messages in the media? To admit this, the viewer would have to admit to being “controlled” to some extent by the media. Reactance would demand some action, then. But if one denies that one is being controlled by the media, one does not need to act, according to reactance theory. Second, with regard to cognitive consistency, if one believes that violence is bad and media vio­ lence is causing his or her own aggression or his or her child's aggression, one is in a state of dissonance. The inconsistency could be resolved, for example, by turning the child away from media violence, but it could also be resolved by simply denying that me­

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dia violence plays any role in causing one's child's aggressive behavior. Similarly, if one's own self-identity is heavily invested in video games and behaving nonaggressively, it is in­ consistent with the individual's self-image to play violent video games if they cause you to be aggressive. However, it would be fine if you believe that you are impervious to the in­ fluence, even if others are not.

Desensitization

A fourth psychological process that might account for denial of media violence effects is

desensitization. As reviewed, researchers have shown that repeated exposure to violence both reduces the negative emotional impact that violence has on an observer and gener­ ates a cognitive desensitization in the sense that violence is perceived as more normative (Krahe et al., 2011). We are now at a point in history when the emerging generation of policy makers and researchers has mostly grown up playing violent video games as well as observing violent television and movies. The public today, on average, has been ex­ posed to more scenes of violence than any recent generation. Beatings, mutilations, rapes, and murders are all common in violent video games, movies, and television. Al­ though the total level of violence may not have increased substantially on television and in movies in recent years, the explicitness of the violence and blood and gore has in­ creased. Inevitably, this must desensitize the public more to violence. Consequently, the public is less likely to “see” violent video games or violent movies as violent. Thus, it be­ comes harder for the public to be concerned about the general issue of media violence.

Desensitization to media violence might also account for the argument that a focus on media violence effects takes attention away from what some researchers and policy mak­ ers believe to be “more important” causes of aggression (e.g., observing or being victim­ ized by violence in the family, school, and peer group). Indeed, researchers and policy makers themselves are not immune to desensitization. Dedicated individuals opposed to violence may not “see” the violence in video games, movies, or television as particularly upsetting because they have been raised on a diet of that violence. Consequently, they may focus more on exposure to violence in the real world because they have not been ex­ posed to it themselves. If one is not aware of the theory about why observation of vio­ lence stimulates violence, one (p. 168) can easily imagine that observation of real-world vi­ olence might have a much more potent effect.

Exposure to “Unbalanced” News Coverage of the Research Findings

Finally, in addition to these four psychological processes that might account for denial of media violence effects, it also is important to note the potentially important influence of news media reports on the topic. Bushman and Anderson (2001) showed that from 1975 to 2000, effect sizes based on scientific studies of the effects of media violence on aggres­ sion have actually increased. However, the strongest statements in news reports (the au­ thors found 636 newspaper and magazine articles) about negative effects of media vio­ lence peaked in the 1970s and early 1980s, and then weakened through 2000. Specifical­ ly, the authors had judges rate news reports on a 21-point scale with –10 assigned to re­

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ports stating that violence viewing causes a decrease in aggression, –5 assigned to re­ ports saying or implying that parents should encourage their children to watch violence, 0 assigned to reports stating there was no association between media violence and aggres­ sion, +5 assigned to reports saying or implying that parents should discourage their chil­ dren from watching violence, and +10 assigned to reports stating that media violence

causes aggression. Between 1975 and 1985, the average article was judged at 5.09, but from 1990 to 2000 it was judged as 4.06. This led the authors to speculate on why, as sci­ entific evidence for media violence effects became stronger, news articles reported weak­ er effects. The authors speculated that: (1) the news industry might have a vested eco­ nomic interest in denying the effect; (2) scientists have failed to explain the effect; and (3) the news media may operate according to a “fairness doctrine,” that is, in an attempt to present both sides of any argument, they give equal weight to the opinions of both sides. Thus, news reports give “balanced” coverage to both sides of the debate, despite the fact that the weight of the scientific evidence supports the link between media violence and aggression. Thus, we argue that the news media may represent a powerful source of in­ fluence that shapes public attitudes about media violence effects.

Our view is that the psychological processes we have described along with unbalanced news coverage about any topic, not just media violence, would be likely to lead to intellec­ tually flawed critical thinking by many people about information that is new and in con­ flict with those individuals’ already established beliefs. Scriven and Paul (1987) defined critical thinking thus:

intellectually disciplined process of actively and skillfully conceptualizing, apply­ ing, analyzing, synthesizing, and/or evaluating information gathered from, or gen­ erated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action. In its exemplary form, it is based on universal intellec­ tual values that transcend subject matter divisions: clarity, accuracy, precision, consistency, relevance, sound evidence, good reasons, depth, breadth, and fair­ ness.

They go on to say:

Critical thinking varies according to the motivation underlying it. When grounded in selfish motives, it is often manifested in the skillful manipulation of ideas in ser­ vice of one's own, or one's groups,’ vested interest. As such it is typically intellec­ tually flawed, however pragmatically successful it might be. When grounded in fairmindedness and intellectual integrity, it is typically of a higher order intellectu­ ally, though subject to the charge of ‘idealism’ by those habituated to its selfish use.

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Future Directions Bushman and Anderson (2001) described some steps that social scientists themselves could take to present their findings and educate the public about the link between expo­ sure to media violence and aggressive behavior. One step is simply to recognize that dif­ ferent types of communication styles are required for the “conservative scientist role” (e.g., presenting research with appropriate caution at scientific conferences) versus the “public educator role” (e.g., offering opinions, without technical language, based on one's general knowledge of the empirical findings). Second, Bushman and Anderson sug­ gested that regarding the public educator role, researchers may communicate to the pub­ lic how the findings have led to their own personal choices regarding use of violent me­ dia. For example, have the research findings affected whether they restrict their own children's violent media use?

Third, the authors argued that researchers need to “realize that the role of disseminating insights gained from their research is a part of their job…” (p. 487). In this vein, it is in­ structive to note that media violence researchers did indeed contribute to the recently highly publicized court case involving a California law requiring parent approval for sales of violent video games to minors. Thirteen scientific (p. 169) experts (all of whom had pub­ lished original empirical research on media violence effects in peer-reviewed journals) au­ thored a “Statement on Video Game Violence” to accompany a court brief (the Gruel Brief) in support of the media violence–aggression relation; that report was cosigned by 115 additional experts. An opposing brief (the Millett Brief), filed in support of the video game merchants, was signed by 82 individuals, including researchers, medical scientists, and video game industry executives. Two lower courts, followed by the Supreme Court in June 2011, struck down the law based on an infringement of the First Amendment's free­ dom of speech guarantee. Gentile and Anderson (2011) noted, “Yet, we can imagine that many parents may misunderstand this ruling as suggesting that there is no evidence that video games can have effects on children. It is important to recognize that this ruling is based on constitutional grounds and is only peripherally related to scientific evidence.” Interestingly, Sacks et al. (2011) analyzed the scientific credibility of the “experts” who filed the opposing briefs. Sacks et al. found that Gruel Brief authors and signatories were much more likely to have published peer-reviewed journal articles in the field of aggres­ sion/violence, and in particular, media violence effects (including in top-tier journals), compared with Millett Brief signatories. For example, 100% of the 13 Gruel Brief authors and 37% of the 115 Gruel signatories had published at least one peer-reviewed article on media violence, compared with 13% of the 82 Millett Brief signatories. Sacks et al. sug­ gested that the courts need to have at their disposal a way to judge the degree to which briefs’ “experts” are truly qualified to make judgments to support their arguments.

Gentile and Anderson (2011) anticipated the Supreme Court's decision to overturn the California law. They wrote, “…we understood that many other factors are relevant to this case beyond research, such as legal precedent, constitutional issues, and political fac­ tors.” Those authors concluded that perhaps the most effective roles for media violence researchers are to continue to collaborate with industry representatives to improve me­

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dia ratings systems and educate parents to understand and use these systems. Although we agree with this approach, we also think that, as with other socially relevant effects in the past that the public and courts had trouble accepting (e.g., smoking causes cancer, segregating schools causes poor education for minorities), eventually truth will triumph and dissonance between beliefs and behaviors will be reduced more easily by changing behaviors rather than by denying that effects exist.

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L. Rowell Huesmann

L. Rowell Huesmann, Research Center for Group Dynamics, Institute for Social Re­ search, University of Michigan, Ann Arbor, MI

Eric F. Dubow

Eric F. Dubow, Bowling Green State University, OH; University of Michigan, Ann Ar­ bor, MI

Grace Yang

Grace Yang, Department of Communication Studies, University of Michigan, Ann Ar­ bor, MI

Violent Media Use and Violent Outcomes

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Print Publication Date: Oct 2020 Subject: Psychology, Clinical Psychology Online Publication Date: Oct 2020 DOI: 10.1093/oxfordhb/9780190218058.013.18

Violent Media Use and Violent Outcomes Christopher L. Groves, Sara Prot, and Craig A. Anderson The Oxford Handbook of Digital Technologies and Mental Health Edited by Marc N. Potenza, Kyle A. Faust, and David Faust

Abstract and Keywords

Electronic media is an omnipresent form of entertainment in contemporary society. A large body of empirical evidence provides support for the notion that violent media use (e.g., television, films, video games) increases the likelihood of low-level everyday forms of physical, verbal, and relational aggression. Less work has been conducted on the ef­ fects of violent media on more extreme forms of aggression that can be considered vio­ lent. This chapter provides a review of the theoretical frameworks for understanding the potential effects of violent media use on violent outcomes. It follows this discussion with a selective review of the relevant literature regarding the effects of violent media use on vi­ olent outcomes, with a focus on the effects of violent video games. Conclusions are drawn regarding the state of the literature, current debate, and future directions needed for re­ search.

Keywords: violent media, video games, television, aggression, risk factors, violent behavior, criminal behavior

(p. 202) There is no doubt that the past few decades have brought about dramatic changes in the ways in which individuals entertain themselves. The public now has unprecedented access to media with the introduction of tablets, smartphones, and smart televisions. This shift in entertainment is marked most clearly by the rise in video game play, which contin­ ues to grow. In 2013, consumers spent nearly US$21 billion on video games and video game equipment (Entertainment Software Association, 2014). Nearly 90% of American children and teenagers report playing video games (Gentile, 2009) and do so for an aver­ age of 2 hours a day (Rideout, Foehr, & Roberts, 2010). This level of popularity has fos­ tered concern among policymakers, parents, and researchers alike regarding one of the most prevalent themes of video game play: its violent content.

Research regarding violent media effects has a rich history. Analyses of violent television and film effects have consistently found modest but significant effects on a wide range of aggression-related outcomes (for reviews, see Anderson et al., 2003; Strasburger & Wil­ son, 2014). Recently, the field has experienced renewed interest in these effects, fostered by the increasing popularity of video games. Scholarly interest in video game effects has

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produced hundreds of studies that are perhaps best summarized in meta-analyses on the subject. In the largest meta-analysis on the subject to date, Anderson et al. (2010) con­ cluded that violent video game play serves as a causal risk factor for aggressive behavior. While disagreements over specific details in the field exists, meta-analyses conducted by both proponents for and against violent media effects demonstrate a consistent relation­ ship between violent media use and aggressive behavior even when employing overly conservative statistical controls (Boxer, Groves, & Docherty, 2015).

Much of the research on violent video game use has focused on subcriminal levels of ag­ gression, such as the administration of hot sauce to someone participant who is known to dislike spicy foods (Barlett, Branch, Rodeheffer, & Harris, 2009), administering unpleas­ ant noise blasts to a competitor (Anderson et al., 2004), and rating another person as less deserving of financial support (Cicchirillo & Chory-Assad, 2005). We will label these ex­ amples of aggression low-level aggression. Such laboratory measures of aggression are frequently used in experimental studies designed to test the causal role of media vio­ lence. The use of these techniques has produced a thorough and methodologically sound literature that can speak to the causal nature of these relationships and to the underlying psychological processes. However, the administration of hot sauce and aversive noise blasts are far cries from the real-world extreme forms of violence that the general public and news media sometimes implicate as resulting from media violence.

This difference between the types of aggression that can ethically be used in laboratory experiments versus extreme real-world violence does not exonerate violent media use as a potential source of criminal-level aggressive outcomes. It does, however, indicate that before making strong claims about violent media effects on violent behavior, one must look beyond laboratory experiments of media violence effects on aggressive behavior. Specifically, we must consider basic theoretical processes that link media violence to ag­ gressive and violent behavior, both directly and through media violence effects on known risk factors for violence. We also must look to nonexperimental studies of media violence effects, as these are studies that can ethically examine more extreme forms of aggression as outcome variables. Next, we briefly discuss the theoretical processes responsible for violent media effects and conclude this section by describing a useful theoretical frame­ work for understanding how criminal-level or violent outcomes may transpire. Finally, we summarize the literature specific to such outcomes and provide directions for future re­ search.

Theory and Definitions Aggression is a behavior enacted with the intent to cause harm to a person who does not want to be harmed (Bushman & Anderson, 2001; Bushman & Huesmann, 2010). It is an observable behavior, not an emotion, a thought, or a fantasy that occurs inside the per­ son. This definition excludes accidental hurtful actions (e.g., unintentionally tripping someone) as well as intentional actions that are not done with the intent to cause harm (e.g., a dentist administering a painful medical procedure). Aggression can take several

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forms, including physical aggression (e.g., punching someone), verbal aggression (e.g., cursing at someone), and relational aggression (e.g., harming someone’s relationships by spreading a rumor behind his or her back).

Violence refers to more extreme forms of physical aggression that pose a significant risk of injury to the victim (such as assault and murder; Huesmann & Taylor, 2006). All violent behaviors are aggressive behaviors, but many aggressive behaviors are not violent (e.g., a kindergartner poking an annoyed classmate). Violent and aggressive behaviors can be viewed as a part of a continuum of severity, ranging from mild aggressive acts (e.g., pok­ ing) to severe aggressive acts which constitute violence (e.g., shooting; Anderson et al., 2003; Anderson & Huesmann, 2003). In support of the idea that aggression and violence exist on a continuum, research demonstrates strong associations between mildly aggres­ sive behavior earlier in life and risk of violent behavior later (Huesmann, Lagerspetz, & Eron, 1984). In addition, aggressive cognitions and affect are significant predictors of ag­ gression and violence (Anderson & Bushman, 2002; Bushman & Huesmann, 2010). There­ fore, research studies measuring mild forms of aggressive behavior, thoughts, or emo­ tions can provide valuable insight into understanding violent behavior. Of importance for the current discussion, violence is defined by the World Health Organization as “the in­ tentional use of physical force or power, threatened or actual, against another person or against a group or community that results in or has a high likelihood of resulting in injury, death, physiological harm, maldevelopment, or deprivation” (Zwi, Krug, Mercy, & Dahlberg, 2002). This definition is important to consider as it does not require that out­ comes of violent acts include injury or harm, merely that the act itself is highly capable of producing injury. This is perhaps one of the clearest differences between violent acts and criminal violence. By definition, criminal violence possesses a lower frequency compared to violent acts as violent actions do not always lead to injury and are (perhaps conse­ quently) reported to authorities less frequently. For this reason, research benefits from the use of violent behavior as an outcome rather than criminal behavior because such ac­ tions are more common, possess more variability to explain, and can be researched with somewhat smaller samples.

The General Aggression Model The General Aggression Model (GAM; Anderson & Bushman, 2002; DeWall, Anderson, & Bushman, 2011) provides a comprehensive framework for understanding aggression and violence. GAM (p. 204) describes the personal and situational factors and their resultant processes that influence an individual’s aggressive behavior in the current situation as well as forces that influence the long-term development of aggressive tendencies. The GAM has guided a wealth of research in the media violence domain (Anderson et al., 2010; Anderson & Dill, 2000; Möller & Krahe, 2009) but also has been applied to numer­ ous other aggression domains. Although GAM includes various biological processes (e.g., genetic, hormone), our present focus is on the social-cognitive aspects. It provides a basis

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Figure 18.1 The General Aggression Model: overall view.

From Anderson & Carnagey (2004).

for understanding how violent media influence aggression and violence both in short- and long-term contexts.

An overview of the key processes proposed by GAM is shown in Figure 18.1. The likeli­ hood that a person will act aggressively in a current social encounter is influenced both by person factors (e.g., trait aggression, hostility, and psychoticism) and situation factors (e.g., provocation, heat, violent media). Media violence increases the likelihood of aggres­ sive behavior in a current social encounter through its influence on a person’s present in­ ternal state (their current thoughts, affect, and arousal level). For example, watching a vi­ olent television show primes aggressive cognition, increases hostile affect, and leads to physiological arousal. These changes in internal state variables affect appraisal and deci­ sion making. They guide a person’s perception, interpretation of events, decision making, and behavior. For example, a person whose aggressive thoughts and affect have been primed by watching a violent television show is more likely to decide to act aggressively in response to provocation.

Once a behavioral response has been chosen, it influences the ongoing social encounter, creating a feedback loop. Over time, repeated aggressive encounters (such as bullying, rejection, or habitual violent video game use) strengthen a person’s habitual patterns of responding and can lead to the development of an aggressive personality. Repeated expo­ sure to violence (in real life or in the media) involves recurring rehearsal of aggressive knowledge structures. Over time, habitual violent media consumers can become more ag­ gressive in outlook by developing hostile perceptual biases, attitudes, beliefs, and behav­ iors (Bartholow, Sestir, & Davis, 2005; Gentile, Li, Khoo, Prot, & Anderson, 2014).

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Risk Factor Approach Research shows that violent actions are rarely the result of a single cause (Huesmann & Taylor, 2006). Instead, aggression and violence are caused and moderated by a large number of interacting factors, such as genetic predispositions (Hudziak et al., 2003), par­ enting practices (Eron, Huesmann, & Zelli, 1991), personality (Schmeck & Poustka, 2001), accessibility of guns (O’Donnell, 1995), cultural norms (Nisbett & Cohen, 1996), even climate change (Anderson & DeLisi, 2011). No reputable researcher would claim that media violence is “the cause” of violent behavior: it is one of many contributing fac­ tors (Huesmann & Taylor, 2006).

A useful approach for understanding how multiple causes contribute to violent behavior is the risk and resilience approach (Anderson, Gentile, & Buckley, 2007; Gentile & Bush­ man, 2012; Gentile & Sesma, 2003; Masten, 2001). This approach focuses on life experi­ ences that put people at risk for future maladaptation (risk factors) and factors that pro­ tect from this risk exposure (protective factors). Effects of different risk and protective factors accumulate; the cumulative risk model posits that the likelihood of problematic functioning is increased by every risk factor present and is decreased by each protective factor present (Masten, 2001; Wright, Masten, & Narayan, 2013). Cumulative risk is ex­ pected to have a bigger role in disrupting functioning than any single risk factor (Belsky & Fearon, 2002; Gentile & Sesma, 2003). For example, Gentile and Bushman (2012) ex­ plored effects of six risk and protective factors for aggression (media violence use, physi­ cal victimization, sex, hostile attribution bias, parental monitoring, and prior (p. 205) ag­ gression). Each risk factor increased the likelihood of aggression over a 6-month period, while each protective factor decreased it. Furthermore, the overall number of risk factors present was a better predictor of aggression than any single variable, showing evidence of cumulative risk.

Taking multiple risk factors into account is especially important when attempting to pre­ dict extreme and rare behaviors, such as violent behavior. After hearing about media vio­ lence effects, non-scientists sometimes give comments such as “I play violent video games, but I’ve never shot anyone” (Anderson et al., 2007). In fact, “shooting someone” is a severe form of physical aggression which most people will never engage in. Media vio­ lence, by itself, is unlikely to bring about extremely violent behavior (as is any single risk factor). However, it may increase the likelihood of more common or subtle forms of ag­ gression (e.g., verbally aggressive responses like swearing at someone). It can also in­ crease the risk of violent behavior for those individuals who have multiple risk factors (Anderson et al., 2007). In populations that have multiple risk factors and few protective factors, media violence effects on violent and criminal behaviors can be clearly observed (DeLisi, Vaughn, Gentile, Anderson, & Shook, 2013).

These processes can be illustrated using the metaphorical “aggression thermometer” (shown in Figure 18.2). People at the lowest end of the thermometer al­ ways act respectfully and nonaggressively. People at the highest end frequently engage in extremely aggressive and violent acts. Each risk factor may heat up the thermometer and

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Figure 18.2 The aggression thermometer.

From Gentile & Bushman (2012).

increase a person’s aggression level by several notches but is not powerful enough to change someone’s behavior from being routinely respectful to others to being violent. If, however, the person already has other risk factors (e.g., poverty, being bullied, living in a violent neighborhood, etc.), regular media violence use may contribute enough additional risk to push them to the top of the aggression thermometer, resulting in physical aggres­ sion and violence.

Selective Review of Empirical Evidence A central tenet of the risk-factor approach to understanding violent outcomes is that high- level aggression (such as assaults and murders) requires multiple risk factors to be present if it is to occur. The details of such interactions require more research in order to be fully understood. For example, future research could focus on the relative contribution of specific risk factors or determine whether specific risk factors must be present in or­ der for certain outcomes to occur, and work to better understand the potentially curvilin­ ear relationship between risk factor possession and such outcomes (Gentile & Bushman, 2012).

A resultant prediction from this approach is that the effect of any single risk factor should provide a progressively smaller impact on increasingly severe forms of aggression. As noted previously, the relationship between violent video game use and low-level aggres­ sive outcomes is small but robust (Anderson et al., 2010). The predicted effect of violent video game use alone on high-level aggressive outcomes is therefore likely to be much smaller and, as measured by many research designs, statistically undetectable.

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A good example of this is the oft-repeated claim that there are no studies demonstrating that media violence causes an increase in homicides. Statements such as these are rarely accompanied with the appropriate caveat that there are numerous risk factors at multiple levels (such as prenatal malnutrition, physical and psychological abuse, exposure to vio­ lence, provocation, access to guns, culture of honor) that conjunctively influence violent crimes. Furthermore, it is both practically and ethically impossible to conduct experimen­ tal studies of media violence effects on homicide rates. For this reason, we focus on stud­ ies using methods suited to (p. 206) answering research questions about media violence effects on violent behavior that actually are answerable. For the most part, that means fo­ cusing on correlational and longitudinal studies that include some type of violent behav­ ior as an outcome variable. Furthermore, we avoid studies that employed inappropriate statistical controls for investigations of this sort. For example, variables that are consid­ ered outcomes of violent media use (e.g., trait aggression; Anderson & Bushman, 2002) should not serve as statistical controls in such research as their inclusion effectively con­ trols for a study’s own dependent variable (Prot & Anderson, 2013).

Correlational Research

Among the most common methods for studying the effect of violent video game use on vi­ olent outcomes is the correlational design. This approach allows researchers to examine relationships between variables that are typically unmeasurable in a laboratory and de­ rive the large sample that is likely needed to observe the effects of violent video game use on violent outcomes.

In one large study by Denniston, Swahn, Hertz, and Romero (2011) of a nationally repre­ sentative sample of students in grades 9–12 (n = 14,041), media use (television, video game, and computer) was dichotomized by clustering individuals who reported spending more than versus less than 3 hours per day consuming either television or video games/ computer games/general computer use. Similarly, violent outcome variables were also di­ chotomous and included whether students had carried a weapon, a gun, or a weapon on school property within the 30 days prior to the survey. They also recorded whether stu­ dents had been in a physical fight or been in a physical fight on school property within the last 12 months. They found that frequent television and video game/computer use was associated with all of these outcomes. Furthermore, the relationships including outcomes such as physical fighting and carrying a weapon on school property remained significant even when controlling for race, gender, and grade. Of important note is that media use did not include the more specific measure of violent content and therefore the effects of violent content may be underestimated when considering these findings.

In another large-scale study by Boxer, Huesmann, Bushman, O’Brien, and Moceri (2008), 820 youth (390 of which were juvenile delinquents), as well as their parents and teachers, completed measures of violent media use (reported by youth) and a number of aggression and violence-related outcomes including frequency of punching or beating another indi­ vidual, throwing rocks or bottles at others, fights/bullying (reported by teachers and guardians), and several other related outcomes. Several additional risk factors for aggres­

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sion were also measured including general (low-level) aggressiveness, psychopathology, callous-unemotional traits, exposure to neighborhood violence, academic skills, and expo­ sure to low-level aggression. A composite of the violence-related measures was signifi­ cantly related to violent media use even after controlling for sex, age, and these other risk factors. Violent media use was also related to delinquency status, though this is a bi­ variate relationship in which gender and age were not controlled.

In a recent study by Ybarra, Huesmann, Korchmaros, and Reisner (2014), longitudinal da­ ta were collected from 9-to 18-year-olds (n = 1,489). This sample was analyzed as a cross- sectional set due to the rarity of youth reporting carrying a weapon in the past month across all three time points. They asked youth to report whether they had carried a weapon to school in the past month (such as a gun, knife, or club). They found that indi­ viduals who reported playing at least some games with violent content were five times more likely to report having carried a weapon to school compared to those who reported playing games with little to no violent content.

In an additional correlational study, DeLisi et al. (2013) collected data from 227 juvenile offenders. Predictors included measures of violent video game play, prosocial video game play, sex, age, race, and antisocial personality. The primary dependent variable was self- reported serious violence as measured by gang fighting, hitting a teacher/parent/peers, and attacking another person. Despite the use of statistical controls that are considered overly conservative (antisocial personality), violent video game use was significantly asso­ ciated with more violent acts.

Experimental Research

As noted previously, experimental research regarding the effects of violent media use on violence-related outcomes is rare due to the ethical considerations of eliciting seriously aggressive behavior in the laboratory. Experimental studies provide the strongest method for testing causality and ruling out potential confounding variables due to the use of ran­ dom assignment. One experimental study by Konijn, Bijvank, and Bushman (2007) was able to examine violent behavior (as defined by the World Health Organization) using a modified version (p. 207) of Taylor’s Competitive Reaction Time Task. This task involves asking participants to compete in reaction time trials against another, ostensible partici­ pant. Prior to each trial, participants select a noise volume and duration to administer to their opponent if the trial is won. Crucial to the violent outcome hypothesis, participants were told that high noise volume levels could cause permanent hearing damage to their opponent, which therefore meets the definition of violent behavior. As expected, they ob­ served a significant main effect of violent content on the selection of noise volume levels capable of producing damage. Furthermore, this tendency was exacerbated among those who expressed desires to be like the main character of the violent games.

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Longitudinal Research

Longitudinal studies are among the strongest in providing evidence for the effect of vio­ lent media use on violent behavior. They allow examination of violent behavioral change over time by measuring all key variables at two or more points in time. They also allow the testing of causal mechanisms by controlling for violent behavior at the onset of the study, thereby ruling out attraction effects that may occur if violent individuals gravitate toward violent media use. Importantly, effect sizes obtained when controlling for initial levels of violent behavior should not be considered measures of the “true” effect size as such a procedure effectively only captures the influence of violent media use on change in violent behavior between the two time periods measured and therefore ignores any prior influence of media violence that occurred before the study began.

In a short-term, longitudinal study by Gentile, Coyne, and Walsh (2011), children in the third, fourth, and fifth grades (total n = 430), their parents, and teachers completed sur­ veys at two time points within the school year (average lag between measurements was 5 months). Violent media use was measured by asking participants to report what their three favorite video games, television shows, and movies were; how violent each was; and how often they used each. The researchers then took the product of the frequency of use and the violent content for each show/video game/movie and summed these products to­ gether to produce a total violent content use measure. Of particular interest for the cur­ rent review, the measure was significantly related to a composite measure of child physi­ cal fighting as reported by the child, their teachers, and other forms of physical aggres­ sion reported by peers. Several studies have produced similar findings in which violent media use is related to physical fights among youth (Anderson et al., 2007; DuRant, Champion, & Wolfson, 2006; Gentile & Bushman, 2012). Considering this particular out­ come as violent behavior, however, may be debatable given that physical fighting among youth is arguably less likely to lead to serious outcomes such as physical injury requiring medical attention. However, longitudinal work by Huesmann, Eron, Lefkowitz, & Walder (1984) indicates that aggression levels early in life (age 8) are related to serious aggres­ sive behaviors later in life (age 30) including spousal abuse and antisocial and criminal behavior.

In other work by Huesmann et al. (Huesmann, Moise-Titus, Podolski, & Eron, 2003), indi­ viduals were followed for 15 years between the ages of 6–8 and 21–23. Data collection be­ gan in 1977–1978 and completed in 1992–1995. It is worth noting that only violent televi­ sion use was measured in the current sample. When considering this limitation, it is im­ portant to note that the psychological processes involved in both violent television and vi­ olent video game effects are thought of as highly similar (Anderson et al., 2003), and therefore results from work on violent television use should be considered relevant to questions regarding the influence of violent video game play on violent outcomes. The au­ thors found that high violent television use as a child was significantly related to adult­ hood pushing, grabbing, or shoving one’s spouse for males; throwing items at one’s spouse for females; shoving others for males and females; punching, beating, or choking

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another adult for females; and state-reported convictions for non-traffic violations among males.

In a study by Johnson, Cohen, Smailes, Kasen, and Brook (2002), children (n = 707) were followed over a 17-year interval. They found that television viewing among individuals (mean age 14) was associated with assault or physical fights resulting in injury, robbery, threats to injure someone, or using a weapon to commit a crime and other aggressive acts years later (mean ages 16 or 22). Furthermore, these associations continued when examined at the time these individuals were an average of 30 years old. Most critically, these associations persisted even when controlling for a variety of factors including previ­ ous aggressive behavior, childhood neglect, family income, neighborhood violence, parental education, and psychiatric disorders.

(p. 208) Contrasting Findings

Despite the seemingly strong findings of these studies, other investigations have found less support. One study by Gunter and Daly (2012) used a propensity score matching technique in which individuals who reported playing mature-rated (M-rated) games were individually matched with others who did not play such games but held similar scores on a large battery of control variables. They found that without using the matched sample, M-rated (mature) game players were significantly more likely to engage in all violent out­ comes including hitting another person, participating in a group fight, carrying a weapon, or carrying a gun and produced a higher overall violent score. When examining the matched sample, however, these relationships were reduced to nonsignificance for all measures except for participating in a group fight and carrying a weapon. Female matched participants scores were not reduced to nonsignificance but still showed fewer and weaker overall effects.

One issue worth noting in this study is that the use of playing M-rated games as an indi­ cator is not the best method for measuring violent video game use because such ratings take into account the presence of profanity and nudity in games—not solely the violent content. Previous research indicates that this difference in measuring violent video game exposure can lead to meaningful decrements in observed effect sizes (Busching et al., 2013). More importantly, however, the use of propensity score matching dramatically re­ duces the sample size used and thus the power to detect true but small effects. Given that the theoretical effect size of violent video game use on violent outcomes should be quite small, the statistical power required to detect such effects demands very large sample sizes. In fact, examination of the group differences between M-rated game players and nonviolent game players in the matched samples indicates that M-rated players scored higher on every metric of violent outcomes except for gun-carrying among males, sug­ gesting a larger sample is needed.

A cross-sectional study by Ybarra et al. (2008), of youth aged 10–15 years (n = 1,588) in­ cluded measures of violent video game, violent music, violent television use, and expo­ sure to depictions of real violence online using the Youth Internet Safety Survey. They

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measured seriously violent behaviors such as stabbing or shooting someone, aggravated assault, robbery, and sexual assault. Their primary dependent variable was whether indi­ viduals had engaged in one or more of these behaviors in the previous year. They found that violent television, video game play, and even music use were each associated with in­ creased likelihoods of engaging in at least one seriously violent act within the past year. Individuals consuming such violent media were between 2.6 and 7.2 times more likely to engage in violent acts depending on the type of violent media used. These relationships, however, dropped to nonsignificance for all forms of media except for the depiction of re­ al-life violence on Internet websites when a large battery of covariates including race, so­ cioeconomic status, substance use, violence in the home and community, a number of in­ dices of poor parental care, academic performance, and delinquent peers were added to the statistical model. Those who viewed instances of real violence on online websites, however, remained five times more likely to engage in seriously violent behavior within the past year compared to those who did not view such websites. The authors note that the explanations for the disparity between these effect sizes are unknown and require fur­ ther research.

In another contrasting study, Robertson, McAnally, and Hancox (2013) assessed a group of individuals (n = 1,037) from birth to 26 years of age beginning in 1972–1973. Impor­ tantly, they assessed television viewing throughout a large portion of the participants’ childhood (ages 5–15 years) and also measured several covariates during this time. They found that television viewing throughout childhood was related to the likelihood of being diagnosed with antisocial personality disorder, aggressive personality traits, and the like­ lihood of criminal conviction. These effects occurred even when statistically controlling for sex, IQ, socioeconomic status, previous antisocial behavior, and indices of parental control. The contrasting finding, however, was that the likelihood of violent convictions was not a significant outcome after including each of these statistical controls. Of course, the study suffers from failing to measure violent content of television viewing. Also, it may have been necessary to include more participants in the study as well, given that vio­ lent convictions are relatively rare.

Conclusion The effects of violent media have been heavily researched over the past several decades. There is now a wealth of evidence supporting the conclusion that violent video game use serves as a causal risk factor for aggressive affect, thoughts, and behavior (Anderson et al., 2010, Greitemeyer & Mügge, 2014). (p. 209) The evidence for violent television and film effects is similarly overwhelmingly strong.

As our review indicates, aggressive acts differ across a wide range of severity. The bulk of research, especially experimental research, has focused on milder forms of aggression. There is much less work regarding the influence of violent media use on the more ex­ treme forms of aggression covered in this chapter.

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A central theme arising from our review is that the studies conducted on this topic do not arrive to precisely the same conclusions. Studies that tend not to detect any effects of vio­ lent media use also tend to measure the most extreme forms of aggressive behavior (e.g., group fighting, stabbings, shootings). Sample sizes appear to be a potential factor con­ tributing to null effects in at least some of these cases. Some studies have failed to assess exposure to violent screen media, assessing total time spent rather than time on violent media.

Studies that demonstrate no relationship between violent media exposure and violent be­ havior also tend to include large numbers of statistical control variables, some of which are actually potential outcomes of high media violence exposure. Other control measures often tap into factors related to violent outcomes such as poor parenting, violence seen in the community, delinquent peer presence, and poor academic performance. Such factors are crucially important to parse out of the relationship between violent media use and vi­ olent outcomes. The results of these studies suggest that any bivariate relationship may be at least partially fueled by these and similar third variables. However, it is important to note that such techniques often are not pre-tested by ensuring that both violent media use and violent behavior are significantly related to each covariate. The inclusion of fac­ tors that do not meet this statistical criteria may serve to artificially deflate the relation­ ship between violent media use and violent outcomes. In cases such as this, the variance associated between the covariate, the predictor, and the outcome, is statistically re­ moved, even when there is no “true” relationship to be controlled. In other words, the variance in the dependent variable (violent behavior) is inappropriately attributed to the covariate, and, consequently, violent media use has less variance to “explain” the out­ come (Prot & Anderson, 2013). Future research needs to do a better job of testing and re­ porting details of the statistical control variable.

Investigations that do report significant effects on violent behavior tend to assess more common forms of violent behavior, use more specific measures of exposure to violent me­ dia, and tend to be large-scale longitudinal studies that examine behavior over very long periods of time. This may suggest that the effects of violent media use on severe violent behavior require sustained, long-term exposure in order for such extreme forms of ag­ gression to emerge. If this is the case, the “snapshot” method used in correlational stud­ ies may not be sufficient to detect effects of violent media use on truly violent behavior.

The subject matter discussed here has been the focus of intense debate with extreme points having been made on either side of the argument, especially within lay circles. In commenting on a murder in 2004, the lawyer Jack Thompson, stated that “We have dozens of killings in the US by children who had played these types of [violent] games…. These types of games are basically murder simulators. There are people being killed here almost on a daily basis” (CNN International, 2004). In contrast, the president of the Elec­ tronics Software Association, Doug Lowenstein, was cited in a 2000 interview as stating that “There is absolutely no evidence, none, that playing a violent video game leads to ag­ gressive behavior” (Cable News Network, 2000; as cited in Anderson & Bushman, 2001).

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Due to the work of numerous scholars on the subject cited here, we can safely assert that the truth lies somewhere in between these extreme points of view.

A lot is already known about both the short- and long-term processes by which television, film, and video game violence exposure increases aggressive and violent behavior. The basic bio-social-cognitive models have been well-tested and validated. Nonetheless, a great deal more work is required on this topic before we fully understand all of the conse­ quences of violent game play. For example, much more work is needed in examining such effects over long periods of time. Such work needs to include much larger samples; more precise measures of key predictor, outcome, and control variables; and a host of addition­ al variables that have only recently begun to receive research attention. The latter cate­ gory includes variables related to media violent effects on stereotypes of various out­ groups, on attention deficits and impulse control problems, and on moral reasoning, be­ liefs, and attitudes. Longitudinal studies are often limited to a span of a few years, but this review suggests that effects on extreme forms of behavior may be best studied under much longer time periods consisting of a decade or more of observation. More work is al­ so needed in determining (p. 210) the relationship between violent game play and the severity of outcomes. Findings supporting risk factor approaches to understanding ag­ gression frequently consider the impact of an increasing number of risk factors on the likelihood of engaging in a single violent outcome (e.g., Gentile & Bushman, 2012). Much less work has been conducted on the relationship between risk factor possession and out­ comes based on severity within an individual study (e.g., the number of risk factors re­ quired for highly violent outcomes to occur compared to relatively less extreme forms of violence). Without doubt, this area of study is likely to receive heavy focus by the scientif­ ic community in the next several decades.

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Christopher L. Groves

Christopher L. Groves, PhD, MS, Department of Psychology, University of Wisconsin, Oshkosh

Sara Prot

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Sara Prot, PhD, School of Psychological, Social and Behavioural Sciences, Coventry University

Craig A. Anderson

Craig A. Anderson, PhD, MS Department of Psychology, Iowa State University

Self-Enhancement and Self-Protection Motives

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Print Publication Date: Feb 2012 Subject: Psychology, Social Psychology Online Publication Date: Sep 2012 DOI: 10.1093/oxfordhb/9780195399820.013.0017

Self-Enhancement and Self-Protection Motives  and

The Oxford Handbook of Human Motivation (1 ed.) Edited by Richard M. Ryan

Abstract and Keywords

People desire to maximize the positivity, and minimize the negativity, of their self-views. The tendency to exalt one's virtues and soften one's weaknesses, relative to objective cri­ teria, manifests itself in many domains of human striving. We focus illustratively on three strivings: the self-serving bias (crediting the self for successes but blaming others or situ­ ations for failures), the better-than-average effect (considering the self superior to the av­ erage peer), and selective self-memory (disproportionately poor recall for negative self- relevant information). Nonmotivational factors (e.g., expectations, egocentrism, focalism, individuated-entity versus aggregate comparisons) are not necessary for the emergence of these strivings. Instead, the strivings are (at least partially) driven by the self-enhance­ ment and self-protection motives, as research on self-threat and self-affirmation has es­ tablished. The two motives serve vital functions: They confer benefits to psychological health and psychological interests (e.g., goal pursuit).

Keywords: self-enhancement, self-protection, self-serving bias, better-than-average effect, self-memory, psycholog­ ical health

Introduction Individuals routinely appraise their qualities, performance, behavior, and feedback they receive from others. They also choose activities in which to engage, allocate credit or blame for dyadic and group task outcomes, recollect events from their lives, use self- knowledge to understand other people, and judge the value of their relationships or the groups to which they belong. We suggest, in the current chapter, that these and similar domains of human functioning can be motivated, and we proceed to discuss the role of two pivotal motives: self-enhancement and self-protection.

Self-enhancement and self-protection are instances of self-evaluation motives (Sedikides & Strube, 1995), which themselves are a class of the hedonic or pleasure/pain drive (Al­ icke & Sedikides, 2011a). Self-evaluation motives guide processing and appraisal of self- relevant information, broadly defined (Sedikides, 1993; Sedikides & Strube, 1997). Self-

Constantine Sedikides Mark D. Alicke

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enhancement in particular refers to the desire and preference for maximizing the positivi­ ty of self-views, whereas self-protection refers to the desire and preference for minimiz­ ing the negativity of self-views. Self-enhancement and self-protection are reflected in indi­ viduals’ tendency to exaggerate their strengths and to underrate their weaknesses more so than objective standards would warrant. The two motives are also reflected in individu­ als’ tendency to construe or remember events in a manner that places their self-attributes in the most favorable light that is credible to themselves and to others (Sedikides & Gregg, 2003). Finally, the motives energize and guide attributions, task involvement, and behavior. In the long run, self-enhancement and self-protection foster psychological health (Sedikides, Gregg, & Hart, 2007) and assist in the advancement (p. 304) and pro­ tection of psychological interests (e.g., goals; Alicke & Sedikides, 2009).

We begin our excursion into self-enhancement and self-protection with a brief historical overview. We then provide key examples of motive instantiation, what we call self-en­ hancement and self-protection strivings (Alicke & Sedikides, 2011b; Sedikides & Gregg, 2008). These striving are the self-serving bias, the better-than-average effect, and selec­ tive self-memory. In discussing each of these strivings, we consider the perennial “cogni­ tion-motivation” debate. We acknowledge, of course, that cognition and motivation are closely intertwined (Kruglanski, 1989; Kunda, 1990; Pyszczynski & Greenberg, 1987). Yet we aim to provide evidence that the strivings are motivated and, in particular, that they cannot be exclusively accounted for by the vagaries of information processing (Sedikides, 2012). Next, we discuss the functional benefits of the two motives: promotion of psycho­ logical health and psychological interest. We conclude with a consideration of issues wor­ thy of further empirical attention.

A Historical Overview The seeds for modern theorizing on self-enhancement and self-protection motivation were sown in classical times. The Cyrenaics (founder: Aristippus; Tatarkiewicz, 1976) and Epi­ cureans (founder: Epicurus; De Witt, 1973) thought that hedonism drives human action. They observed that people want to feel good, or avoid feeling bad, about themselves, and they further proposed that humans want and pursue pleasurable experiences, while de­ testing and eschewing unpleasant ones. Notably, Demosthenes, the orator of antiquity, re­ marked insightfully on self-deception: “Nothing is so easy as to deceive oneself; for what we wish, we readily believe.”

The role of hedonism as the master motive receded while rationalism was in ascendance. This philosophical school, building on Plato's ideas (Bloom, 1991), depicted an objective reality that all individuals with correct understanding (“orthodoxy”) could readily discern (Kenny, 1986; Loeb, 1981). Continental rationalists (Descartes, Leibniz, Spinoza), for ex­ ample, opined that selfish, irresponsible, or malicious behavior was due to flawed knowl­ edge. Erudition would cure personal and social ills such as immorality or the prioritiza­ tion of personal over societal goals.

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The pendulum swung back with Renaissance philosophers (Macfarlane, 1978) and the British empiricists. Mandeville (1705) argued that humans overvalue themselves and ex­ pect others to do the same. Hobbes (1651/1991) believed that behavior was driven by the unbridled pursuit of pleasure rather than by a failure to grasp a priori truths. “Men [are] vehemently in love with their own opinions” (p. 48), he proclaimed. The position that hu­ mans have an excessively positive view of themselves and of the objects (e.g., persons, possessions) associated with them was reflected in the utilitarianism of Bentham (1789/1982) and John Stuart Mill (1863/2004), the forewarning of Nietzsche (1886/1972) for the power of pride to rewrite memory (Maxim 68, p. 72), and the contemplations of La Rochefoucauld (1678/1827), Schopenhauer (1844/1996), and Freud (1905/1961a) on the curious human capacity for self-deception.

William James (1890) was the first psychologist to systematize various philosophical ac­ counts and propose a unifying principle. He observed that thinking about one's self gives rise to the emotions of “self-complacency and self-dissatisfaction” (p. 305). He also re­ marked on “social self-seeking,” people's persistent concern with the achievement of tan­ gible successes and public acclaim. “Each of us,” James stated, “is animated by a direct feeling of regard for his [self]” (p. 308). He proceeded to define the self (empirical “me”) as a repository of ego-relevant matters. James’ key animating principle, self-enhance­ ment, found fertile ground in Gordon Allport's (1937) theorizing. He advocated that hu­ mans have a need for self-positivity, and he also regarded self-protection as “nature's el­ dest law.” Heider (1958) similarly argued that subjective needs, desires, and preferences partially serve to maintain an individual's positive outlook. Rogers (1961) proposed the construct of positive self-regard, a form of self-appreciation achieved by satisfying one's own, rather than others’, standards and expectations. In the meantime, Sigmund Freud (1915/1961b, 1923/1961c, 1926/1961d) and Anna Freud (1936/1946) were pioneering the analysis of defense mechanisms. The scientific study of self-enhancement and self-protec­ tion was born.

Instantiations of Self-Enhancement and Self- Protection How have scientists approached self-enhancement and self-protection? They have done so through experimental and correlational investigations of over 60 instantiations (or imple­ mentations) of the motives. These marks of self-enhancement and self-protection have re­ cently been summarized through factor-analytic techniques, with both Western (Hepper, Gramzow, & Sedikides, 2010) and East-Asian (Hepper, Sedikides, & Cai, in press) sam­ ples, into four factors: positivity embracement, defensiveness, favorable construals, and self-affirming reflections.

(p. 305) Positivity embracement reflects the acquisition of positive feedback (e.g., self- serving attributions for success), whereas defensiveness reflects the protection of self from threat (e.g., self-serving attributions for failure). A striving that exemplifies both fac­ tors is the self-serving bias, the tendency to credit the self for successes but to blame oth­

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ers (e.g., dyadic partners, ingroup, situations) for failures. Favorable construals reflects flattering portrayals of the self in the social world. An exemplary striving here is the bet­ ter-than-average effect, the tendency to regard the self as superior to others in many do­ mains of functioning. Finally, self-affirming reflections refers to securing favorable, or by­ passing unfavorable, self-views and outcomes. A key mechanism through which this process is attained is selective self-memory, or disadvantageous recall for negative as op­ posed to positive feedback.

Next we review literature on the self-serving bias, the better-than-average effect, and se­ lective self-memory. Although we fully endorse the close interweaving of cognition and motivation (Kruglanski, 1989; Kunda, 1990; Pyszczynski & Greenberg, 1987), we venture to make the case for motivation. That is, we attempt to document that this class of purpo­ sive goal strivings cannot be accounted for purely and exclusively by nonmotivational an­ tecedents. Instead, each striving is, at least in part, an outcome of the self-enhancement and self-protection motives in action.

The Self-Serving Bias

“If more than one person is responsible for a miscalculation, none will be at fault,” Murphy's law advocates. Weiner's (1972) attributional analysis of achievement motivation documented this pattern. Actors attribute their successful outcomes to internal factors (e.g., ability, effort, discipline) and their unsuccessful outcomes to external factors (e.g., bad luck, task difficulty, harsh course instructor). More generally, assuming the lion's share of responsibility for desirable events and denying responsibility or displacing it to external causes for undesirable events has come to be known as the self-serving bias (SSB; Miller & Ross, 1975).

The SSB is a robust and pervasive phenomenon. It is evident among university students (Zuckerman, 1979), athletes (De Michele, Gansneder, & Solomon, 1998), and drivers (Ste­ wart, 2005). It occurs in the arena of interpersonal influence (Arkin, Cooper, & Kolditz, 1980), naturalistic sports (Mullen & Riordan, 1988), and organizations (Corr & Gray, 1996). It is manifested by children, adolescents, and adults (Mezulis, Abramson, Hyde, & Hankin, 2004). And it is found both in Western and non-Western cultures (Brown & Kobayashi, 2002; Mezulis et al., 2004).

Next, we will consider reasons why the self-serving bias is motivated or why it cannot be accounted for solely by nonmotivational factors. Specifically, we will discuss the role of self-threat, self-affirmation, expectancies, and impression management. We will offer rep­ resentative examples in each case.

Self-Threat From a self-protection perspective, when people feel threatened, they become defensive (Roese & Olson, 2007). Given an outlet, such as the opportunity to deflect attributions re­ garding task outcomes, they will grab it to footprint their defensiveness. Assuming that the self-protection motive underlies the SSB, the more threatened people feel, the

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stronger the magnitude of the SSB will be. A meta-analysis by Campbell and Sedikides (1999) tested whether the SSB waxes and wanes as function of self-threat, operational­ ized as negative feedback. This meta-analysis examined several moderators of the SSB, such as role, self-focused attention, and interpersonal orientation.

In particular, each moderator was classified as high or low in self-threat potential. For ex­ ample, the moderator role was classified in terms of actor or observer. Actors presumably experience more self-threat than observers, given that actors’ self-views are directly chal­ lenged by negative feedback. The moderator self-focused attention was classified as self- focused or other-focused attention. Self-focused attention presumably involves more threat, given that participants in this experiential state are more likely to become aware of the discrepancy between their actual and ideal/ought self. Hence, their focus on perfor­ mance standards would intensify the psychological impact of negative feedback. Finally, the moderator interpersonal orientation was classified as competitive or cooperative. Some participants competed (actually or ostensibly) with another person, whereas others cooperated (actually or ostensibly) with another person, on a task. Failed competitive par­ ticipants would presumably experience the highest level of self-threat because they would have the most at stake on the task outcome.

The meta-analysis proceeded to test the effectiveness of the SSB moderators. The propo­ sition that self-threat magnifies the SSB was supported. For example, actors, self-focused, and competing participants displayed the SSB, but their respective counterparts (ob­ servers, other-focused, and cooperative (p. 306) participants) did not do so. In all, this meta-analysis illustrated that, the more threatened individuals feel, the more likely they are to resort to the SSB.

This conclusion is bolstered in research by Kernis, Cornell, Sun, Berry, and Harlow (1993) and by Crocker, Voelkl, Testa, and Major (1991). Undergraduate students are quick to find flaws in a test when they fail it but quick to stress its validity when they pass it (Wyer & Frey, 1983). This pattern is especially pronounced among individuals with unstable self- esteem, suggesting that these individuals use the SSB when threatened to shore up a fragile sense of personal worth (Kernis et al., 1993). Black American students experience a drop in self-esteem when the negative feedback is administered by a White evaluator believed to be unaware of their race; however, their self-esteem is unaffected when the evaluator is believed to be aware of their race. In the latter case, participants attribute their failure to racial prejudice, thus denying the validity of the test (Crocker et al., 1991). Here, the SSB is not only a mode to respond to self-threat but also a means to alleviate the consequences of threat (i.e., drop in self-esteem).

Self-Affirmation As discussed earlier, self-threat intensifies the SSB. It follows that the SSB will be attenu­ ated or cancelled when the self-threat is assuaged. One way of reducing self-threat is via self-affirmation (Sherman & Hartson, 2011). Here, individuals affirm a domain (e.g., val­ ues) irrelevant to self-threat. For example, they explain in writing, before or after they re­ ceive negative feedback, why some values are important to them. This self-affirmation

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procedure reduces defensiveness (and even buffers neuroendoctrine and psychological responses to stress; Creswell et al., 2005) by making individuals feel more secure in their self-worth. Self-affirmation, then, would reduce, if not eliminate, the SSB.

Sherman and Kim (2005) tested these ideas in field experiments with volleyball and bas­ ketball athletes. The experiments were conducted at the conclusion of a game, with posi­ tive feedback operationalized as a win and negative feedback as a loss. Immediately after the game, athletes were escorted into a conference room and undertook a self-affirmation manipulation. They rated and ranked five values (aesthetics, religion, social, political, the­ oretical) in terms of personal importance. Then, participants in the control condition re­ ceived a 10-item scale corresponding to their least important value, whereas participants in the self-affirmation condition received a 10-item scale corresponding to their most im­ portant value. Each item consisted of two statements, one describing a facet of the rele­ vant value, the other being neutral (i.e., filler). Participants proceeded to rate their agree­ ment with each statement. Participants in the control condition displayed the SSB. How­ ever, participants in the self-affirmation condition refrained from it. In all, self-affirmation eclipsed the proclivity to respond defensively to self-threat, a pattern tracked by the van­ ishing of the SSB.

Nonmotivational Explanations We will now turn to the nonmotivational explanations of expectancies and impression management.

Expectancies It has been argued that differential expectancies for success and failure account for the SSB (Miller & Ross, 1975). Based on prior experience (Kelley & Michela, 1980; Tetlock & Levin, 1982), individuals expect success more frequently than failure. As such, they make internal attributions for expected outcomes and external attributions for unexpected out­ comes (i.e., SSB).

There is evidence that expectations can influence the SSB. For example, individuals with chronic expectations of superior task performance (e.g., high self-esteemers, normals) manifest strongly the SBB relative to individuals with chronic expectations of inferior task performance (low self-esteemers, depressed; Blaine & Crocker, 1993; Tennen & Herzberger, 1987). Similarly, participants who regard a task as important (and hence like­ ly have chronic expectations of superior performance) demonstrate the SSB to a greater degree than participants who regard a task as unimportant (Miller, 1976).

Nevertheless, expectations are not a necessary component of the SSB (Weary, 1979; Weary Bradley, 1978; Zuckerman, 1979). Of the various moderators in the Campbell and Sedikides (1999) meta-analysis discussed earlier, expectations did not play a substantial role. Actors and observers approach the experimental situation with the same expecta­ tions, yet only actors display the SSB. Furthermore, it is not clear why a momentary state of self-focused versus other-focused attention, or a state of competitive versus coopera­ tive interpersonal orientation, would influence task expectancies. Yet the SSB was mani­

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fested by some of these participants (i.e., actors, state-self-focused persons, competitive persons) but not others. Finally, the SSB is observed even when controlling for task im­ portance (Sedikides, Campbell, Reeder, & Elliot, 1998).

(p. 307) Impression Management Participants may display the SBB in a strategic maneuver to present themselves favorably to others (Miller, 1978; Weary, 1979). Impression management, of course, aims at the en­ hancement or protection of one's public image (Forsyth & Schlenker, 1985), although such aims are not always felicitous (Miller & Schlenker, 1985; Sedikides, Gregg, et al., 2007). Nevertheless, strategic enhancement/protection of one's public image does not ne­ cessitate the concurrent enhancement/protection of one's private self. Impression man­ agement may be superficial and short lived (i.e., driven by the moment or situation) rather than authentic. It may merely reflect putting on a persona or playing a role rather than expressing a cherished self-belief.

Impression management concerns can influence the SSB (Arkin, Appelmen, & Burger, 1980; House, 1980). Such concerns, however, are not necessary for its occurrence. Sedikides et al. (1998) tested undergraduate students at a large university. The partici­ pants worked together, as members of a dyad, on an interdependent-outcomes task. They were unacquainted and thus unlikely to anticipate future interactions. In addition, care was taken to ensure that participants expected not to meet each other after the experi­ ment and not to discuss this experiment even if they happened to encounter each other on campus. Finally, all procedures were private, anonymous, and confidential, with each participant being unaware of the other's contribution to the interdependent-outcomes task. These procedures were intended to minimize impression management concerns. The experimental task ostensibly assessed creativity. Following bogus success or failure feedback at the dyadic level, participants did manifest the SSB.

Greenberg, Pyszczynski, and Solomon (1982) put the impression management explana­ tion of the SSB directly to test. Participants took an alleged intelligence test (“Culture Fair Test of g”). Half of them learned that the experimenter was interested in their perfor­ mance on the test and therefore would collect their named answer sheets and record their scores (public performance condition: presence of impression management con­ cerns). The other half of participants learned that the experimenter was disinterested in their performance and had no way of knowing how well they had done on the test (private performance condition: absence of impression management concerns). Participants dis­ played the SSB in both conditions. Remarkably, the SSB was stronger in the private than public performance condition. In all, impression management concerns cannot fully ac­ count for the SSB.

Summary Although nonmotivational factors play a role in the SSB, they cannot account singly for it. Expectations or strategic self-management is not necessary for the emergence of the

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SSB. In contrast, research on self-threat and self-affirmation makes a compelling case that the SSB is a valid signature of the self-enhancement and self-protection motives.

The Better-Than-Average Effect

Garrison Keillor's Lake Wobegon is a fictional location, where “all the women are strong, all the men are good looking, and all the children are above average.” This characteriza­ tion describes succinctly the human tendency for overestimation of one's merits and un­ derestimation of one's liabilities, in comparison to other persons. Research has confirmed this tendency. Most people judge themselves as better than their average peer (Alicke & Govorun, 2005; Brown, 1998; Dunning, Heath, & Suls, 2004), and they truly believe they are so (Williams & Gilovich, 2008). The phenomenon of rating oneself above the average peer standing on positive characteristics, or rating oneself below the average peer stand­ ing on negative characteristics, has been labeled the better-than-average effect (BTAE).

The BTAE is robust and pervasive. It is found among undergraduate students rating their leadership skills, athletic prowess, ability to get along with others (Brown, 1986; College Board Exams, 1976), intentions (Kruger & Gilovich, 2004), resistance to socially undesir­ able media messages (Davison, 1983), complexity of personality (Sande, Goethals, & Radloff, 1988), possessions (Nesselroade, Beggan, & Allison, 1999), and, indeed, their very humanness (Haslam, Bain, Douge, Lee, & Bastian, 2005); drivers rating their driving skills, while in a hospital due to a car accident they had caused (Preston & Harris, 1965); college instructors rating their teaching ability (Cross, 1977); social psychologists rating the quality of their research (Van Lange, Taris, & Vonk, 1997); students assessing their dating popularity (Preuss & Alicke, 2009) or couples assessing the quality of their mar­ riage (Rusbult, Van Lange, Wildschut, Yovetich, & Verette, 2000); and adults assessing their happiness (Freedman, 1978). In addition, individuals suffering from rheumatoid arthritis rate their symptoms as less severe than those of the average patient (DeVellis et al., 1990), and elderly persons judge that they are less at risk for age-related problems than their peers (p. 308) (Schulz & Fritz, 1987). The BTAE has also been found among preschoolers (Weiner, 1964), elementary school children (Albery & Messer, 2005), high school students (Kurman, 2002), and representative community samples (Andrews & Whitey, 1976; Heady & Wearing, 1988). Ironically, people believe that they are less prone to the BTAE than the average person (Pronin, Lin, & Ross, 2002).

Next we will discuss five reasons why the BTAE is motivated. These pertain to attribute valence and controllability, attribute importance (in cross-cultural context), attribute veri­ fiability, self-threat, and self-affirmation. We will also consider nonmotivational accounts of the effect.

Attribute Valence and Controllability Self-enhancement and self-protection strivings are tactical (Sedikides & Strube, 1997; see also Sedikides & Gebauer, 2010). People do not self-enhance or self-protect across the board; instead, they are selective on the attributes that they will tout or undervalue. For example, they may be more likely to self-enhance on positive attributes over which they

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have high control (e.g., resourceful) than positive attributes of which they have low con­ trol (e.g., mature). Conversely, they may be more likely to self-protect on negative attrib­ utes over which they have high control (e.g., unappreciative) than negative attributes over which they have low control (e.g., humorless).

The results of a study by Alicke (1985) demonstrated that the BTAE effect indeed varies as a function of attribute valence and controllability. Undergraduates rated themselves more favorably on positive traits, and less favorably on negative traits, compared to their average peer. Thus, the BTAE increased as the valence of the self-attribute increased. In addition, participants rated themselves more favorably on positive controllable traits, and more unfavorably on negative controllable traits, compared to their average peer. Finally, they rated themselves more favorably on positive controllable than positive uncontrol­ lable traits, and rated themselves less favorably on negative uncontrollable than negative controllable traits, compared to their average peer. This latter finding in essence illus­ trates that people self-aggrandize the most when they feel responsible for their positive traits, and self-aggrandize the least when they believe that fate is responsible for their negative traits.

Attribute Importance: On the Panculturality of The Btae Self-enhancement and self-protection strivings are also tactical in another way. People are more likely to assert their self-superiority on their important (e.g., trustworthy) than their unimportant (e.g., punctual) attributes (Sedikides & Strube, 1997). This principle is illus­ trated in recent work by Brown (2011, Studies 1–4), where participants indeed showed a stronger tendency to evaluate themselves more positively on important than unimportant traits (Study 1). This principle is also illustrated when placing the BTA effect in cultural context.

Important self-attributes are those that imply successful role fulfillment or enactment of culturally sanctioned roles. They imply that one is a valued member of a given culture, given that one excels on culturally (and personally) important characteristics, no matter if one falls behind on culturally (and personally) unimportant characteristics. Members of all cultures, then, will appraise themselves positively on important (but not necessarily on unimportant) attributes.

For Western culture important attributes are those conveying agency (e.g., personal ef­ fectiveness, competence), whereas for Eastern culture important attributes are those con­ veying communion (e.g., personal integration, other-orientation). Hence, Westerners will display the BTAE on agentic attributes, whereas Easterners will display the BTAE on com­ munal attributes. Westerners, for example, will rate themselves as better than their aver­ age peer on originality or independence but not on loyalty or respectfulness, but Eastern­ ers will rate themselves as better than their average peer on loyalty or respectfulness but not on originality or independence. This hypothesis has been confirmed both by primary studies (Brown & Kobayashi, 2002; Gaertner, Sedikides, & Chang, 2008; Sedikides, Gaert­ ner, & Toguchi, 2003) and meta-analytic investigations (Sedikides, Gaertner, & Vevea,

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2005, 2007; for more general discussions, see Brown, 2003, 2010). The findings attest to the panculturality of the BTAE.

Attribute Verifiability There is another way in which self-enhancement and self-protection are tactically ex­ pressed. It involves attribute verifiability. Some attributes (e.g., those belonging to the moral or social domain) are more difficult to verify objectively than others (e.g., those be­ longing to the intellectual or physical domain; Reeder & Brewer, 1979; Rothbart & Park, 1986). Therefore, moral attributes leave more latitude for self-enhancement strivings than intellectual ones. The BTAE, then, will be stronger in the case of moral than intellec­ tual attributes.

(p. 309) This pattern has been empirically supported. Participants firmly believe that they have enacted more moral behaviors than their average peer. However, they believe rather tentatively that they have enacted more intellectual behaviors than their peers (Allison, Messick, & Goethals, 1980; Van Lange & Sedikides, 1998). In addition, participants rate themselves as better than average on traits that are either preclassified as ambiguous or are manipulated to be ambiguous (Critcher, Helzer, & Dunning, 2011). These findings il­ lustrate that self-enhancement and self-protection strivings, albeit “dying to come out,” are susceptible to reality constraints (Gramzow, 2011; Sedikides & Gregg, 2008).

Self-Threat A self-protection perspective would predict that, when individuals feel threatened, they will become defensive (Roese & Olson, 2007). We have discussed evidence that self-threat intensifies the SSB. Does self-threat also intensify the BTAE?

Research by Brown (2011, Study 4) showed that it does. All participants took the Re­ motes Associates Test (RAT; Mednick, 1962), ostensibly a test of the cognitive ability of in­ tegrative orientation (defined as creativity). The RAT consists of a series of three words; in each case, participants are asked to generate a fourth word that relates in some way to the other three. All RAT problems were difficult, and participants received either bogus negative feedback or no feedback. Subsequently, participants completed a BTAE task: They rated both themselves and most other people on important and unimportant traits. Participants who received negative feedback manifested a stronger BTAE effect (com­ pared to those who did not receive feedback). In particular, they rated themselves as su­ perior to others on important than unimportant traits, but they rated others as superior on unimportant than important traits. These results underscore the motivational rele­ vance of the BTAE (see also: Brown, Collins, & Schmidt, 1988; Brown & Gallagher, 1992; Dunning, Leuenberger, & Sherman, 1995).

Self-Affirmation Does self-affirmation reduce the BTAE? An experiment by Guenther (2011) addressed this question. Participants were assigned to either a self-affirmation or a control condition. The manipulation was a hybrid of two established procedures introduced by Blanton, Pel­ ham, DeHart, and Carvallo (2001) and by Wiesenfeld, Brockner, Petzall, Wolf, and Bailey

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(2001). Specifically, self-affirmation participants described an accomplishment or achieve­ ment that made them feel good about themselves. Control participants, on the other hand, described the student union building on campus. Subsequently, all participants rat­ ed their standing, relative to that of their average academic peer, on a variety of traits (e.g., cooperative, truthful, athletic, attractive, imaginative, tolerant).

The results were revealing. The BTAE emerged, as expected, among participants in the control condition, but it was attenuated among participants in the self-affirmation condi­ tion. Self-affirmation reduced defensiveness or the need to assert one's superiority over others. These findings attest to the motivational underpinnings of the BTAE.

Nonmotivational Explanations The three most prominent nonmotivational explanations for the BTAE effect are egocen­ trism, focalism, and individuated-entity versus aggregate comparisons. We consider them next along with a fourth possibility, that the BTAE reflects simple contrast of oneself from the average peer.

Egocentrism According to egocentrism, when participants compare their attributes to those of the av­ erage peer, they think selectively about their own strengths or about their peer's weak­ nesses (Champers, Windschitl, & Suls, 2003; Moore, 2007; Moore & Kim, 2003; Wein­ stein, 1980). However, selective recruitment of one's assets or of peers’ liabilities may themselves be expressions of self-enhancement and self-protection (Brunot & Sanitioso, 2004; Sanitioso & Niedenthal, 2006). In addition, egocentrism cannot explain why the BTAE is obtained not only with direct measures (where participants compare the self to the average peer on a single scale) but also with indirect measures (where participants rate the self and average peer on separate and scales that are counterbalanced) (Alicke & Govorun, 2005). Moreover, egocentrism has trouble accounting for why the BTAE is stronger on unverifiable than verifiable traits (Allison et al., 1989; Critcher et al., 2011) and for why self-affirmation reduces the BTAE (Guenther, 2011). Finally and importantly, the BTAE is observed even when behavioral evidence for attributes is equated for self and others. This pattern was demonstrated by Alicke, Vredenburg, Hiatt, and Govorun (2001). Participants first estimated the percentage of times they enacted various trait-relevant behaviors (e.g., percentage of times they were uncooperative or cooperative, when the opportunity arose). A month (p. 310) and a half later, participants received the very same estimates but were led to believe that the estimates were provided by their average peer. Still, participants rated themselves more favorably than “their average peer” on almost all traits. Participants claimed that they were superior to themselves.

Focalism According to focalism, people put greater weight on whatever entity is currently the fo­ cus of their attention. By asking participants to compare their attributes to those of their average peer, research on the BTAE places the self in the focal position and the average peer in the referent position. Self-representations consist of a higher number of unique attributes than other-representations (Karylowski, 1990; Karylowski & Skarzynska, 1992).

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Hence, focusing on the self highlights those unique attributes and leads to perceiving the self as less similar than the average peer (Moore & Kim, 2003; Otten & van der Pligt, 1996; Pahl & Eiser, 2006, 2007; Windschitl, Kruger, & Sims, 2003). However, focalism cannot provide an adequate account of why the BTAE varies as a function of attribute va­ lence, controllability, importance, and verifiability. In addition, focalism cannot explain why the BTAE is obtained with indirect measures (Alicke & Govorun, 2005), when behav­ ioral base rates for relevant traits are the same for self and other (Alicke et al., 2001), and even when the referent is highly concretized (Alicke, Klotz, Breitenbecher, Yurak, & Vre­ denburg, 1995). Finally, focalism cannot explain why participants manifest a stronger BTAE on important than unimportant traits, even when the self constitutes the referent and “most other people” constitute the target (Brown, 2011, Study 3).

Individuated-Entity Versus Aggregate Comparisons This nonmotivational account refers to a single entity (e.g., a person, an object) being compared with an aggregate (e.g., the average peer, the average object). Klar and his col­ leagues (Giladi & Klar, 2002; Klar, 2002; Klar & Giladi, 1997) showed that any member of a liked group (e.g., a randomly selected student at one's university, police officer, soap fragrance) is rated more positively than the group average (e.g., average student at one's university, average police officer, average fragrance), and that any member of a disliked group is rated more negatively than the group average. These findings raise the possibili­ ty that the BTAE is due to the self being an individuated entity and the average peer be­ ing an aggregate. However, the BTAE is still present when the individuated entity is the self; that is, the effect emerges even when the self is compared to any other individuated entity (Alicke et al., 1995). In addition, this nonmotivational alternative cannot explain why the effect ebbs and flows as a function of the motivational significance of the judg­ ment (e.g., attribute valence, controllability, verifiability, importance). Moreover, the al­ ternative cannot easily explain why self-affirmation weakens the effect and, importantly, why the effect emerges even under cognitive load (Alicke et al., 1995, Study 7)—a pattern indicative of automatic self-enhancement (Paulhus, 1993). Finally, the alternative cannot explain why participants manifest a stronger BTAE on important than unimportant traits, even when they compare themselves with a single person (Brown, 2011, Study 2).

Assimilation and Contrast Although some researchers have conjectured that self versus average peer judgments are made by anchoring on the self and contrasting the average peer from that point (e.g., Kruger, 1999), until recently, no studies had been designed specifically to examine this facet of the BTAE. To address this question, Guenther and Alicke (2010) constructed an experimental design that was equipped to test whether self versus average peer judg­ ments represent assimilation or contrast, and in what direction assimilation or contrast might occur. In the first study, participants first made either self or average peer ratings in a pretesting session. Later in the semester, their original ratings were returned and they were now asked to rate the other target (i.e., those who rated the self in the first phase now rated the average peer in relation to their self-ratings, and those who rated the average peer in the first phase now rated the self in relation to their average peer rat­

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ings). Comparisons with the ratings provided by a group that simply made simultaneous ratings of self and the average peer showed that self-ratings were unaltered as a result of whether self and average peer were rated simultaneously, self was rated in relation to the average peer, or the average peer was rated in relation to the self. This shows clearly that the self anchors these judgments. The findings also demonstrated that ratings of the aver­ age peer were higher when made in relation to self-ratings than when self and average peer were rated simultaneously. Contrary to the common assumption that judgments of an average peer are contrasted from the self, average peer ratings were assimilated to­ ward the self.

(p. 311) The fact that people move evaluations of the average peer closer to the self seems to contradict self-enhancement assumptions. However, most modern self-enhancement perspectives (Alicke & Sedikides, 2009; Sedikides & Gregg, 2003, 2008) acknowledge that such tendencies occur in concert with many nonmotivational forces, including rela­ tively automatic anchoring and adjustment processes. Guenther and Alicke (2010) next designed a study to assess whether self-enhancement motives could be discerned in light of these assimilative comparative judgments.

In this study (Guenther & Alicke, 2010, Study 2), participants made self-judgments on various trait dimensions during pretesting. The returned later in the semester and were provided with the self-ratings they had completed during pretesting. This time, they were asked to evaluate the average college student with reference to these self-ratings. Most important, half of the participants were led to think that the ratings they now received were those provided by a randomly selected student instead of by themselves. The critical comparison was between ratings of the average peer made with reference to scale points that participants believed were their own ratings, and those made with reference to iden­ tical points that were believed to belong to another student. Participants assimilated their ratings of average toward the scale points provided to a lesser degree when those scale points were described as self-ratings compared to when the identical points were attrib­ uted to another individual. Thus, although anchoring comparative judgments on the self induces average-peer assimilation because of the fact that self-ratings constitute high scale points, participants’ desire to maintain favorable self-concepts restricts this assim­ ilative process and thereby maximizes the distance between the self and the average peer.

Summary As with the SSB, nonmotivational explanations for the BTAE are rather unsatisfactory. Egocentrism, focalism, individuated-entity versus aggregate comparisons, and assimila­ tion/contrast cannot account for the fluctuation of the BTAE as a function of assessment technique (i.e., indirect measures, equation of behavioral evidence for self and other, cog­ nitive load), motivational relevance (attribute valence, controllability, importance, verifia­ bility), and referent individuation. On the other hand, research on self-threat, self-affirma­ tion, and the motivational relevance of the BTAE makes a compelling case that this effect is a legitimate signature of self-enhancement and self-protection motivation.

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Selective Self-Memory

“It's not only the most difficult thing to know one's self, but the most inconvenient,” quipped Josh Billings. The empirical evidence has treated Billings kindly. People indeed remember poorly their weaknesses compared to their strengths, a memorial pattern that does not occur for other people's weaknesses and strengths (Sedikides & Green, 2009; Skowronski, 2011). We refer to this phenomenon as selective self-memory. Next we dis­ cuss it by reviewing research both from the autobiographical and experimental litera­ tures.

Selective self-memory is robust and pervasive. It has been observed in the domain of feedback (Crary, 1966; Sedikides & Green, 2000), social act frequencies (Gosling, John, Craik, & Robins, 1998), possessions and places (Zauberman, Ratner, & Kim, 2009), rela­ tionship-relevant behaviors (Van Lange, Rusbult, Semin-Goossens, Goerts, & Stalpers, 1999), personality traits (Messick, Bloom, Boldizar, & Samuelson, 1985; Mischel, Ebbe­ sen, & Zeiss, 1976), life events (Ross & Wilson, 2002; Skowronski, Betz, Thompson, & Shannon, 1991), and emotionally charged (i.e., pride-inducing and shame-inducing) events (D'Argembeau & Van der Linden, 2008). It has also been observed not only in Western but also in non-Western or East-Asian cultures (Kwon, Scheibe, Samanez-Larkin, Tsai, & Carstensen, 2009; Schrauf & Hoffman, 2007). Selective self-memory emerges ear­ ly in life. Children, for example, ascribe more serious transgressions to their siblings than to themselves in their recollections of sibling conflict (Wilson, Smith, Ross, & Ross, 2004). Finally, selective self-memory is found both among younger and older adults (Field, 1981, 1997; Wagenaar & Groeneweg, 1990; Yarrow, Campbell, & Burton, 1970).

Selective self-memory may be due to an encoding bias. People avoid attending to unfavor­ able feedback (Baumeister & Cairns, 1992; Sedikides & Green, 2000, Experiment 3), thus impeding its registration. However, selective self-memory may also be due to a retrieval bias. Evidence for this processing mechanism is found in memory for behaviors that ex­ emplify desirable traits (Sanitioso, Kunda, & Fong, 1990), satisfying interpersonal rela­ tionships (Murray & Holmes, 1993), and health-boosting habits (Ross, McFarland, & Fletcher, 1981). Finally, selective self-memory may be due to retention. The negative af­ fect associated with autobiographical memories fades faster across time than the positive

(p. 312) affect associated with such memories (Landau & Gunter, 2009; Ritchie, Skowrons­ ki, Hartnett, Wells, & Walker, 2009; Walker, Skowronski, & Thompson, 2003).

We will examine next why selective self-memory is motivated. In particular, we will zero in on the role of self-threat and self-affirmation in selective self-memory. We will also consid­ er the nonmotivational accounts of differential expectancies and inconsistency between information valence and self-view valence.

Self-Threat

Sedikides and colleagues (Sedikides & Green, 2009; Sedikides, Green, & Pinter, 2004) tested experimentally the role of self-threat in selective self-memory. In the standard par­ adigm, participants first receive behavioral feedback. Some are then asked to imagine, or

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are led to believe, that they are likely to perform the behaviors contained in the feedback. Other participants are asked to imagine, or are led to believe, that another person (Chris) is likely to perform the very same behaviors. These behaviors are either negative or posi­ tive, and they exemplify either central (e.g., unkind vs. kind, untrustworthy vs. trustwor­ thy) or peripheral (e.g., complaining vs. uncomplaining, unpredictable vs. predictable) traits. Next, participants engage in a surprise recall task. The typical finding is that par­ ticipants recall poorly behaviors that are negative, exemplify central traits, and refer to the self (e.g., unkind or untrustworthy behaviors) compared to all other categories of be­ havior (e.g., those that are positive, exemplify central traits, and refer to the self; those that are negative exemplify central traits but refer to Chris). For example, participants re­ call poorly the behaviors “you would borrow other people's belongings without their knowledge” (untrustworthy) and “you would refuse to lend classnotes to a friend who was ill” (unkind). However, participants recall relatively well the behaviors “Chris would bor­ row other people's belongings without their knowledge” and “Chris would refuse to lend classnotes to a friend who was ill” (unkind). Additionally, they recall relatively well the be­ haviors “you would keep secrets when asked to” (trustworthy) and “you would offer to care for a neighbor's child when the babysitter couldn't come” (kind). This recall discrep­ ancy has been labeled mnemic neglect and has been attributed to the self-threat potential of the feedback.

Research has consistently supported the idea that self-threat underlies mnemic neglect. In general, the more threatening the feedback is perceived, the more defensive partici­ pants become (i.e., more likely to exhibit mnemic neglect). For example, the effect is ob­ tained when the behaviors are high on diagnosticity (e.g., “you would be unfaithful when in an intimate relationship”), but it is cancelled when the behaviors are low on diagnostic­ ity (e.g., “would forget for a week to return a borrowed book to a friend”) (Green & Sedikides, 2004). This is because high-diagnosticity behaviors can really reveal whether one is untrustworthy or unkind, and are thus threatening. In addition, the effect is ob­ tained when participants are led to believe that their traits are unmodifiable, but it is can­ celled when they are led to believe their traits are modifiable (Green, Pinter, & Sedikides, 2005). This is because learning that one was born untrustworthy or unkind and will be so for life makes untrustworthiness or unkindness feedback threatening. Relatedly, the ef­ fect is obtained when participants are deprived of the opportunity to improve on feed­ back-relevant dimensions (e.g., to become less untrustworthy or less unkind) and are thus threatened, but it is cancelled when participants are offered the opportunity to improve (Green, Sedikides, Pinter, & Van Tongeren, 2009). In all, this research shows that selec­ tive self-memory is motivated.

Self-Affirmation

Does self-affirmation reduce or negate selective self-memory? Green, Sedikides, and Gregg (2008, Experiment 2) addressed this question. All participants took a test ostensi­ bly assessing their cognitive ability (i.e., creativity). In the self-threat condition, partici­ pants learned that they had performed poorly on the test. In the self-affirmation condi­ tion, however, participants learned that they had performed well on the test. Subsequent­

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ly, all participants proceeded to an “impression” task, which was actually the standard mnemic neglect paradigm (i.e., behavioral feedback).

The results were, once again, telling. Self-threatened participants evinced mnemic ne­ glect, whereas self-affirmed participants did not. Self-affirmation relaxed defensiveness, as tracked by the abolishment of mnemic neglect. These results are consistent with the idea that mnemic neglect is a motivated phenomenon.

Nonmotivational Explanations

We next turn to two nonmotivational explanations of selective self-memory: differential expectancies and inconsistency between information valence and self-view valence.

(p. 313) Differential Expectancies In a review of the literature, Walker et al. (2003) concluded that the base rate of negative versus positive life events is unequal. That is, negative events are half as frequent as posi­ tive events (25% vs. 50%). Differential base rates may also be involved in mnemic ne­ glect. People may process shallowly and recall negative feedback poorly because they do not expect to receive it; based on prior experience, such feedback is implausible.

Can differential expectancies account for selective self-memory? We (Sedikides et al., 2004; Sedikides & Green, 2009) addressed this issue in the context of the mnemic neglect paradigm. As described earlier, this research was concerned with the on-line processing of a concrete and experimentally provided array of feedback as opposed to the recon­ struction of pleasant or unpleasant life events, thus exerting tight control over the to-be- remembered material. The ratio of negative to positive information was equal. In addi­ tion, the relevance of self versus other memories was taken into consideration: The same information was self-referent in one condition and other-referent in another condition. More important, the research addressed the issue of whether mnemic neglect is due to expectancies (Sedikides & Green, 2004, Experiment 1).

All participants received hypothetical behavioral feedback. However, the referent of the feedback varied. A quarter of the participants received feedback about themselves, and another quarter about Chris. The third quarter of participants received feedback about a person described in glowing terms, such as extraordinarily trustworthy and kind (glowing Chris condition). The fourth quarter of participants received feedback about a close friend. Pretest had established that participants held the most positive expectancies for glowing Chris, considering him or her as most likely to enact positive behaviors and least likely to enact negative behaviors. Expectancies for close friend and self were virtually identical, and they were both more positive than expectancies for (mere) Chris. If ex­ pectancies constituted a sufficient explanation for mnemic neglect, then the effect would be more strongly evident in the glowing Chris than the self condition, and it would be equally strong in the close friend and self conditions. This was not the case. Participants evidenced the most neglect in the self condition, followed by the friend condition, and then by the glowing Chris and Chris conditions (which did not differ significantly).

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These findings were conceptually replicated by Newman, Nibert, and Winer (2009). In a separate session after the usual exposure to and recall of behavioral feedback, partici­ pants provided expectancies for each behavior for either the self or Chris. That is, they estimated the extent to which they could imagine either themselves or Chris performing the behavior. Expectancies and recall were uncorrelated for most but a subset of partici­ pants. This subset was defensive pessimists, who as hypothesized, did not show the typi­ cal mnemic neglect pattern. In conclusion, differential expectancies, albeit relevant to re­ call of autobiographical information (Walker et al., 2003), cannot account solely for mne­ mic neglect and more generally selective self-memory.

Inconsistency Between Information Valence and Self-View Valence Another alternative, though, is worth considering, specifically, inconsistency between the valence of one's self-views and the valence of feedback (Abelson et al., 1968). Mnemic ne­ glect, in particular, may reflect processing of information whose valence is inconsistent with the valence of self-conceptions. Most participants have a positive self-concept (Ogilvie, 1987; Schwartz, 1986). Hence, they recall negative feedback poorly because it is inconsistent with their self-views. This alternative explanation leads to an interesting pre­ diction. Inconsistency will also drive mnemic neglect among participants with a negative self-concept. These participants will recall positive feedback poorly, because it is inconsis­ tent with their self-views.

An experiment (Sedikides & Green, 2004, Experiment 2) tested whether feedback incon­ sistency (behaviors that are inconsistent with the self-view) or feedback negativity (be­ haviors that are negative regardless of whether they are consistent or inconsistent with the self-view) drives mnemic neglect. A pretest identified two groups of participants: those with positive self-views (i.e., trustworthy, kind) and those with negative self-views (i.e., untrustworthy, unkind). These participants were then brought in the laboratory and exposed to the usual mnemic neglect paradigm. The inconsistency alternative would pre­ dict that participants with positive self-views would recall poorly untrustworthy and un­ kind behaviors, whereas participants with negative self-views would recall poorly trust­ worthy and kind behaviors. The results ran contrary to this alternative. All participants, regardless of the valence of their self-conception, manifested mnemic neglect. That is, even individuals who regarded themselves as untrustworthy or unkind recalled poorly un­ trustworthy or unkind behaviors. This is additional (p. 314) evidence that feedback nega­ tivity (i.e., self-threat) underlies mnemic neglect. In conclusion, inconsistency between the valence of one's self-views and the valence of feedback, albeit relevant to autobio­ graphical recall (Gramzow & Willard, 2006), cannot account singly for mnemic neglect and more generally selective self-memory.

Summary As with the SSB and the BTAE, nonmotivational explanations for selective self-memory are not particularly persuasive. Differential expectancies and inconsistency between in­ formation valence and self-view valence cannot provide a satisfactory account for poor re­ call of negative, central, self-referent feedback. Instead, the threat potential of such feed­

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back, including research on self-affirmation, can. The extant evidence points to mnemic neglect as a valid signature of the self-protection motive.

But is self-threatening feedback always recalled poorly? Research on trauma would seem to indicate that it is not: Traumatic events are well remembered (Berntsen, 2001; McNal­ ly, 2003). Such events, though, are extreme, and event extremity is associated with supe­ rior recall (Thompson, Skowronski, Larsen, & Betz, 1996). And yet event valence predicts recall independently of event extremity (Thompson et al., 1996, Chapter 4). Finally, in the mnemic neglect paradigm, behavioral feedback was moderate rather than extreme (Sedikides & Green, 2000, pilot studies). Selective self-memory, then, is applicable to the domain of mild, as opposed to extreme, feedback or events.

What Are Self-Enhancement and Self-Protec­ tion Good For? Self-enhancement and self-protection strivings have functional advantages for the individ­ ual. Next we will consider two critical domains of functionality: psychological health and psychological interests.

Psychological Health

The SSB is linked to a variety of psychological health benefits. For example, the SSB is re­ lated to positive mood (McFarland & Ross, 1982) and high subjective well-being (Rizley, 1978), improved problem solving (Isen & Means, 1983), reduced depression (Abramson & Alloy, 1981), better immune functioning (Taylor et al., 2000), and lower mortality and mor­ bidity longitudinally (Peterson & Seligman, 1987). On the other hand, a weak or absent SSB is related to depression (Sweeney, Anderson, & Bailey, 1986), deteriorating physical health (Peterson, Seligman, & Vaillant, 1998), and poorer athletic, academic, and work performance (Peterson & Barrett, 1987; Seligman, Nolen-Hoeksema, Thornton, & Thorn­ ton, 1990). The positive association between the SSB and psychological health has been found not only in Western culture but also in East-Asian culture (China; Anderson, 1999).

The BTAE is also strongly linked to psychological health. For example, the BTAE is posi­ tively related to indices of thriving (e.g., subjective well-being, purpose in life, positive re­ lations, self-acceptance), positively related to resources (optimism, extraversion, self-es­ teem, family support), and negatively related to indices of distress (e.g., loneliness, de­ pression, anxiety) (Brown, 1991, 1998; Marshall & Brown, 2007; Taylor, Lerner, Sherman, Sage, & McDowell, 2003a). Similar patterns have been obtained in several East-Asian cul­ tures such as China (Brown & Cai, 2009; Cai, Wu, & Brown, 2009; O'Mara, Gaertner, Sedikides, Zhou, & Liu, 2010), Japan (Kobayashi & Brown, 2003), Korea (Chang, Sanna, & Yang, 2003), Taiwan (Gaertner et al., 2008), and Singapore (Kurman & Sriram, 1997). In addition, longitudinal studies, in Western and non-Western culture, indicate that the BTAE promotes subsequent psychological health under adverse conditions (Bonanno, Field, Ko­ vacevic, & Kaltman, 2002; Bonanno, Rennicke, & Dekel, 2005; Gupta & Bonanno, 2010;

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Zuckerman & O'Loughlin, 2006). Moreover, the BTAE serves a stress-buffering function: As a response to stress, the BTAE is related to lower cardiovascular response, more rapid cardiovascular recovery, and lower baseline cortisol level (Taylor, Lerner, Sherman, Sage, & McDowell, b).

Finally, selective self-memory in autobiographical recall is also associated with psycholog­ ical health. For example, selective self-memory is related to lack of dysphoria (Walker, Skowronski, Gibbons, Vogl, & Thompson, 2003), reduced depression (Williams et al., 2007), a future orientation (Brunson, Wheeler, & Walker, 2010), social connectedness or better interpersonal relations (Wildschut, Sedikides, Arndt, & Routledge, 2006), felt conti­ nuity between one's past and one's present (Sedikides, Wildschut, Gaertner, Routledge, & Arndt, 2008), perceptions of life as meaningful (Routledge et al., 2011), and reduced exis­ tential anxiety (Juhl, Routledge, Arndt, Sedikides, & Wildschut, 2010). Relatedly, selective self-memory is linked to fewer symptoms of psychopathology and better psychological health over time (Bonanno, Keltner, Holen, & Horowitz, 1995; Bonanno, Znoj, Siddique, & Horowitz, 1999; Newton & Contrada, 1992). In conclusion, self-enhancement (p. 315) and self-protection strivings are associated with, or promote, psychological health.

Psychological Interests

Psychological interests include love/security, social status, and popularity, as well as skills and abilities (e.g., musicality, athleticism, intelligence). Interests are hierarchically orga­ nized from the general (e.g., being a good student, being a good friend) to the specific (e.g., performing well on a task, providing support to a friend in need) ones. Furthermore, interests can entail private matters (e.g., meeting one's personal standards) or public matters (e.g., meeting organizational standards) and can extend to close relations or im­ portant groups. Finally, interests can be negative or positive. Negative interests include matters that individuals wish to circumvent or shun (e.g., relationship breakup, achieve­ ment failure), whereas positive interest include matters that individuals wish to possess or attain (e.g., two-story house, managerial position) (Alicke & Sedikides, 2009).

A vital function of self-enhancement and self-protection is the pursuit of psychological in­ terests (Alicke & Sedikides, 2009). This pursuit is carried out through either primary or secondary means. (These constructs correspond to notions of primary and secondary con­ trol; Rothbaum, Weisz, & Snyder, 1982.) Primary means refer to changing an objective state of affairs by assuming instrumental action. In that capacity, self-enhancement en­ tails effective action that promotes oneself and one's prospects. Secondary means refers to psychological mechanisms that regulate events by altering how one perceives or inter­ prets them. In that capacity, self-protection entails effective intervention that obviates failing below one's standards. Self-enhancement and self-protection, then, contribute ef­ fectively to the successful pursuit of psychological interests of the effective avoidance of harm to those interests.

The three self-enhancement and self-protection strivings serve psychological interests. Let us first consider the SSB. Seligman et al. (1990) examined the role of the SSB in pre­

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dicting athletic performance. They found that varsity swimmers prone to the SSB (as­ sessed at the start of the season) performed better at sporting competitions than swim­ mers not prone to the SSB. Additionally, Peterson and Barrett (1987) reported that under­ graduate students prone to the SSB (assessed at the beginning of their first year at uni­ versity) received higher grades during their freshman year compared to students not prone to the SSB. This pattern held after controlling for initial ability (measured by the Scholastic Aptitude Test) and initial depression. Students prone to SSB were more likely to have specific academic goals and to make use of academic advising.

The BTAE is similarly implicated in the facilitation of psychological interests. Taylor et al. ( a) showed that the BTAE is positively related to active coping, positive reframing, plan­ ning, achievement, mastery, and personal growth. In addition, Wright (2000) demonstrat­ ed that undergraduate students who are more likely to manifest the BTAE (assessed in the beginning of the semester) achieved higher grades during the semester compared to students less likely to manifest the BTAE. Moreover, students who exaggerate reporting of their grade point average perform better than those who do not (Gramzow, 2011). In general, the BTAE is associated with working harder and longer on tasks (Taylor & Brown, 1988) and with performing better on tasks (Armor & Taylor, 2003).

Finally, selective self-memory in autobiographical recall is also involved in the promotion of psychological interests. Such memory has approach rather than avoidance conse­ quences (Stephan et al., 2011; Walker & Skowronski, 2009) and, as such, it can motivate individuals to engage and persist in goal pursuit (Sedikides & Hepper, 2009; Walker & Skowronski, 2009). Indeed, forms of selective self-memory have been found to be associ­ ated with resilience (Coifman, Bonanno, Ray, & Gross, 2007), improved coping following traumatic life events (Janoff-Bulman, 1992), and, in general, the implementation of active coping strategies in times of stress (Langens & Moerth, 2003) and in attempting to mas­ ter life challenges (Walker & Skowronski, 2009).

Summary A psychological health and psychological interests analysis addresses squarely the issue of why people self-enhance and self-protect. They do not do so for a whim, or just to feel good, or for short-lived impression management purposes. Rather, they do so, and they do so persistently, because self-enhancement and self-protection strivings confer both mo­ mentary and long-term benefits (i.e., ways in which psychological health and psychologi­ cal interests are advanced) and deter both momentary and long-term harms (i.e., ways in which psychological health interests are regressed or thwarted).

Conclusions In his An Outline of Intellectual Rubbish (1943), Bertrand Russell was duly impressed by the influence (p. 316) of motives on human judgment. “Man is a rational animal—so at least I have been told. [ … ] I have looked diligently for evidence in favor of this state­ ment, but so far I have not had the good fortune to come across it [ … ],” he exclaimed in

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wonder (p. 73). We have focused in this chapter on two self-evaluation motives that might have confounded Russell, self-enhancement and self-protection.

We defined self-enhancement as the desire and preference for maximizing the positivity of one's self-views, and we defined self-protection as the desire and preference for mini­ mizing the negativity of one's self-view. We argued that the tendency to exalt one's virtues and make light of one's weaknesses, relative to impartial criteria, manifests itself in a va­ riety of strivings. Due to space limitations, we restricted our discussion to three key striv­ ings: the SSB (crediting the self for successes but blaming others for failures), the BTAE (considering the self superior to others), and selective self-memory (disadvantageous re­ call for negative feedback).

Although we acknowledged that cognition and motivation are closely intertwined, we pro­ ceeded to make a case for the motivational underpinnings of these strivings. We aimed to provide evidence that self-enhancement and self-protection strivings cannot be exclusive­ ly accounted for by nonmotivational (i.e., information processing) factors. The nonmotiva­ tional explanations of expectations and impression management were not deemed neces­ sary for the occurrence of the SSB. Likewise, egocentrism, focalism, and individuated-en­ tity versus aggregate comparisons were not deemed necessary for the occurrence of the BTAE. And similarly, differential expectancies and inconsistency between self-view va­ lence and feedback were not deemed necessary for the occurrence of selective self-mem­ ory. In contrast, evidence from research on self-threat and self-affirmation testifies to the motivational underpinnings of the strivings. The SSB, BTAE, and selective self-memory are driven, in part, by the self-enhancement and self-protection motives.

We drew to a conclusion by asking why individuals self-enhance and self-protect. A partial answer lies in the functionality of self-enhancement and self-protection strivings: They ac­ crue benefits pertaining to psychological health and psychological interests. Self-en­ hancement and self-protection strivings are associated with, or confer, a host of psycho­ logical health advantages, and they advance a host of psychological interests. Mild self- enhancement and self-protection continue to be markers of psychological health.

Future Directions There are several issues in need of further empirical attention. We will briefly touch upon four of them. First, what is the interplay between the two motives? Although self-en­ hancement and self-protection are occasionally treated as polar ends of a single dimen­ sion, the empirical evidence suggests that a lot will be gained if they are treated sepa­ rately (Elliot & Mapes, 2005). Yet the relation between the two motives is complex. They can operate independently, one motive may facilitate the other, or one motive may impede the other. Second, and relatedly, what is the interplay between implicit and explicit self- enhancement and self-protection? In particular, what is the relation between implicit and explicit self-enhancement and self-protection strategies (Arndt & Goldenberg, 2011) or between implicit and explicit self-esteem (Gregg & Sedikides, 2010)? Third, what is the interplay between the self-enhancement and self-protection motives on the one hand and

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other self-evaluation motives on the other? These other motives are self-assessment (i.e., pursuit of accurate self-knowledge; Gregg, Sedikides, & Gebauer, 2011), self-improve­ ment (i.e., pursuit of one's betterment; Sedikides & Hepper, 2009), and self-verification (i.e., pursuit of self-confirmation; Swann, Rentfrow, & Guinn, 2003). Finally, what are the boundary conditions—both situational demands and individual differences—that constrain self-enhancement or self-protection (Gramzow, 2011)? And what are the intrapersonal and interpersonal consequences of such constraints upon motive emergence or manifesta­ tion? These and other issues are worth exploring. As La Rouchefoucauld (1678/1827) prophetically noted, “Whatever discoveries have been made in the land of self-love, many territories remain to be discovered.”

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Constantine Sedikides

Constantine Sedikides Department of Psychology University of Southampton Southampton, England, UK

Mark D. Alicke

Ohio University, Athens, OH, USA