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Psychological Bulletin 2017, Vol. 143, No. 7, 775–782
© 2017 American Ps 0033-2909/17/$12.00 http://dx.doi.org
REPLY
Violent Video Game Effects Remain a Societal Concern: Reply to Engelhardt, and Rouder (2017)
. y l d . a s r e o r h s b i l d e b t u a p i n d m e i l e s l s a i d s t i e f b o o e t n t o o r n s o i n d o n i t a a i c r e s o s u s l A a u l d a c i i v g i o d l n o i h e c h t y s f P o n e a c s u i r e l a m n s A o r e e h p t e y t b h r d o e f t h l y g i e r l y o p s o c d e s d i n t t e n n e i m i s u c e o l d c
Sven Kepes
Brad J. Bushman
Virginia Commonwealth University
Ohio State University
Craig A. Anderson Iowa State University A large meta-analysis by Anderson by Anderson et al. (2010) found that violent video games increased aggressive thoughts, angry feelings, physiological arousal, and aggressive behavior and decreased empathic feelingss and helpin feeling helping g behavi behavior. or. Hilgard, Hilgard, Engelh Engelhardt, ardt, and Roud Rouder er (2017 (2017)) reanalyzed reanalyzed the data of Anderson Anders on et al. (2010) using newer publication bias methods (i.e., precision-effect test, precisioneffect estimate with stand standard ard error, p -uniform, p -curve). Based on their reanalysis, Hilgard, Engelhardt, and Rouder concluded that experimental studies examining the effect of violent video games on aggressive affect and aggressive behavior may be contaminated by publication bias, and these effects are very small when corrected for publication bias. However, the newer methods Hilgard, Engelhardt, and Rouder used may not be the most appropriate. Because publication bias is a potential a problem in any scientific domain, we used a comprehensive sensitivity analysis battery to examine the influence of publication bias and outliers on the experimental effects reported by Anderson et al. We used best meta-analytic practices and the triangulation approach to locate the likely position of the true mean effect size estimates. Using this methodological approach, we found that the combined adversee effects of outli advers outliers ers and publ publication ication bias was less severe than what Hilgard, Engelhardt, Engelhardt, and Rouder found for publication bias alone. Moreover, the obtained mean effects using recommended methods and practices were not very small in size. The results of the methods used by Hilgard, Engelhardt, and Rouder tended to not converge well with the results of the methods we used, indicating indic ating potentially potentially poor perform performance. ance. We therefo therefore re conclu conclude de that violent video game effects should shoul d remain a socie societal tal concern. Keywords: violent video games, aggression, meta-analysis, publication bias, outliers Supplemental materials: http://dx.doi.org/1 http://dx.doi.org/10.1037/bul00 0.1037/bul0000112.supp 00112.supp
Anderson et al. (2010) published Anderson (2010) published a large meta-analysis of 381 effects from violent video game studies involving more than 130,000 130, 000 part particip icipants ants.. The They y foun found d tha thatt viol violent ent vide video o gam games es increased increa sed aggres aggressive sive thoughts, angry feelings, physiologi physiological cal
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as well as correlations between violent game play useful in cross-s Useful sive affect, behavior, andNot cognitions cross-secti ecti Hilgard et al. (2017) (2017) examined a total of 13 meta-an butions (see their Table 3). For the most part, there
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KEPES, BUSHMAN, AND ANDERSON
Hilgard et al.’s (2017) Methodological and Statistical Approach
. y l d . a s r e o r h s b i l d e b t u a p i n d m e i l e s l s a i d s t i e f b o o e t n t o o r n s o i n d o n i t a a i c r e s o s u s l A a u l d a c i i v g i o d l n o i h e c h t y s f P o n e a c s u i r e l a m n s A o r e e h p t e y t b h r d o e f t h l y g i e r l y o p s o c d e s d i n t t e n n e i m i s u c e o l d c
Effect_of_Playing_Violent_Video_Games ira60020_335_342
Anderson et al.’s original analysis, their assertion is no correct. We believe the most sophisticated ana meta-analytic practices (e.g., Kepes & McDaniel, 20 al., 2013; Viechtbauer & Cheung, 2010) and the approach (Jick, 1979) to locate the likely position of t effect size estimate using a comprehensive sensi battery (Kepes et al., 2012). We use this more approach to determine whether the results reported b al. (2017) or by Anderson et al. (2010) are more acc ever, before we proceed to reanalyzing the data, we b the publication bias methods used by Hilgard, Eng Rouder.
Hilgard et al. (2017) suggest that trim and fill, the publication bias assessment method Anderson et al. (2010) used, “is best viewed as a sensitivity analysis rather than a serious estimate of the unbiased [meta-analytic] effect size” (p. 760). In turn, they imply that their publication bias assessment methods are not sensitivity analyses and should be viewed as more serious because they provide an accurate for-bias-adjusted mean estimate. Such an implication is misleading because all methods that assess the robustness of a naïve meta-analytic mean estimate should be viewed as sensitivity analyses (Kepes, McDaniel, Brannick, & Banks, 2013). By naïve we mean the meta-analytic mean effect PET-PEESE without any adjustment for potential biases (Copas & Shi, 2000). Sensitivity analyses examine the degree to which the results of The PET-PEESE (Stanley & Doucouliagos, 2014 a naïve meta-analysis remain stable when conditions of the data or publication bias is a combination of two weight the analysis change (Greenhouse & Iyengar, 2009). We know of models. As Hilgard et al. (2017) stated, PET “extrapol no valid method that can provide a for-bias-adjusted mean estimate available data to estimate what the effect would be in of the true underlying population effect size. Instead, sensitivity ical study with perfect precision” (p. 760). PEE analyses tend to estimate the degree to which a naïve meta-analytic similar manner, except that precision is modeled as mean may be adversely affected by publication and/or other biases. function instead of a linear function. Both PET and Furthermore, it is important to note that all methods become less incorporate multiple moderator variables, although stable with small distributions. In fact, most publication bias gelhardt, and Rouder did not use them in that way. F assessment methods should not be applied to meta-analytic distriboth PET and PEESE are modified versions of Egger butions with fewer than 10 samples, including funnel plot- and intercept and, as such, some of the shortcomings ass regression-based methods (Kepes, Banks, McDaniel, & Whetzel, Egger test (Moreno et al., 2009; Stanley & You're Readingthe a Preview 2012; Sterne et al., 2011). 2014; Sterne & Egger, 2005) may also apply In addition, Hilgard et al. (2017) focused on oneUnlock type offull sensiPEESE. access with a free trial. tivity analysis—publication bias. Yet as Hilgard et al. (2017) PET is known to underestimate the size of noted, heterogeneity can adversely affect the results of publication (Stanley & Doucouliagos, 2007), and PEESE can yie bias analyses (as well as the results of a naïve meta-analysis). Download Withresults Freethe Trial closer the true mean effect size is to zero Because outliers can be a major source of between-study heteroDoucouliagos, 2012), which is why Stanley and geneity, they should be considered when examining the potential (2014) outlined conditional decision rules to determ effects of publication bias (Kepes & McDaniel, 2015). Like pubthe two models should be used to assess the potentia lication bias (Kepes et al., 2012; Rothstein, Sutton, & Borenstein, publication bias (see also Kepes & McDaniel, 2015 2005), the effects of outliers tend to lead to upwardly biased mean 2015). In a reanalysis of data regarding the predictiv estimates to the extent that they are on one side of the distribution conscientiousness, Kepes and McDaniel (2015) fou (Viechtbauer & Cheung, 2010). Furthermore, because betweenPET-PEESE results converged relatively well with th study heterogeneity due to outliers can be mistakenly attributed to uppublication to vote on bias this assessment title battery ofSign other method publication bias, a comprehensive assessment of the influence of that the method tended to perform quite well with rea Useful Not useful publication bias should also include a thorough assessment of recently, Stanley and Doucouliagos (2017) conducted outliers or otherwise influential data points (Kepes & McDaniel, and concluded that PET-PEESE properly accounts f 2015) In other words, to obtain precise and robust estimates neity and performs quite well, although another simu
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REPLY TO HILGARD, ENGELHARDT, AND ROUDER (2017)
simulation study highlighted p-uniform’s poor performance in “realistic settings,” which have been defined as settings with “flexible publication rules and heterogeneous effect” as opposed to “restrictive settings, which involve “rigid publication rules and homogeneous effect sizes” (McShane et al., 2016, p. 731). More traditional selection models that use the complete data when estimating the adjusted mean effect (e.g., Hedges & Vevea, 2005) should be used instead because they tend to perform better (McShane et al., 2016).
. y l d . a s r e o r h s b i l d e b t u a p i n d m e i l e s l s a i d s t i e f b o o e t n t o o r n s o i n d o n i t a a i c r e s o s u s l A a u l d a c i i v g i o d l n o i h e c h t y s f P o n e a c s u i r e l a m n s A o r e e h p t e y t b h r d o e f t h l y g i e r l y o p s o c d e s d i n t t e n n e i m i s u c e o l d c
we included them as well (Stanley & Doucouliagos thermore, there is value in assessing the level between PET-PEESE and other, more established m trim-and-fill, selection models), especially because of of the method. However, following the recommendati ley and Doucouliagos (2014), we use the conditional model and report only the appropriate estimate of th mean effect. With regard to trim and fill, we use the recomme effects (FE) model with the L 0 estimator (Kepes et a address some of the legitimate criticisms of the P-Curve method, we also use the random-effects (RE) model w Like p-uniform, the p-curve method uses only significant studies estimator to assess the robustness of the results from t to estimate an overall mean effect. Therefore, as with p -uniform, (Moreno et al., 2009). In addition to the general cumu for the p -curve method to work, the nonsignificant studies have to analysis by precision, which typically gets plotted in be estimating the same overall mean effect as the significant (see Kepes et al., 2012), we also present the cumu studies, and typically that is not the case when there is betweenanalytic mean of the five most precise effect sizes (i. study heterogeneity (as there is in virtually all real data in the sizes from the five largest primary studies; for a simil social sciences). Indeed, when the developers of the p-curve see Stanley, Jarrell, & Doucouliagos, 2010). This m method tested it against a gold standard of replications of 13 shed some light on the issue of low statistical pow effects across 36 laboratories, they focused on the effects that plagues social science studies. For the selection mode proved homogeneous across the laboratories, for exactly this reapriori models (e.g., Hedges & Vevea, 2005) with rec son (Simonsohn, Nelson, & Simmons, 2014). Not surprisingly, as value cut points to model moderate and severe insta with p-uniform, McShane et al.’s (2016) simulation study found lication bias (Vevea & Woods, 2005). that p-curve did not perform well in realistic settings and conOur comprehensive approach involved five cluded that traditional selection models (e.g., Hedges & Vevea, performed a naïve meta-analysis for each relevant s 2005) are more appropriate for assessing the potential presence of studies on violent video games. Second, we applied You're Reading a Preview publication bias in meta-analytic studies. hensive battery of publication bias analyses. Third, we potential of outliers using a battery of multi Unlock full access with a free presence trial. multivariate influence diagnostics (Viechtbauer, Summary bauer & Cheung, 2010). Fourth, we deleted any ide Download Free Trial Although Hilgard et al. (2017) used more recently developed Wither(s) from the meta-analytic distribution and reran publication bias methods than Anderson et al. (2010) did, past Hence, all meta-analytic and publication bias analy research has shown that several of their methods tend to perform plied to data with and without identified outliers. Fi poorly when applied to real data. It is therefore questionable ducted all analyses with and without the two studies whether the methods Hilgard, Engelhardt, and Rouder used to Hilgard et al. (2017; p. 763) as being problematic (i assess publication bias perform better than the trim-and-fill Kirsch, & Esselman, 1985; Panee & Ballard, 2002 method used by Anderson et al. (2010). Thus, Hilgard, Engelhardt, prehensive approach allows us to present the possi and Rouder’s obtained results and conclusions could be erroneous, mean effect size estimates instead of relying on a s upwith to vote on this titleof the triangulati as could Anderson et al.’s results, especially because neither set of which is Sign aligned the advantages authors used a comprehensive approach to account for outlierand customer-centric science useful et al., 2010 Useful Not (Aguinis induced between-study heterogeneity, which can adversely affect Kepes et al., 2012). In fact, our comprehensive app naïve meta-analytic estimates and publication bias results (Kepes quired or recommended in some areas in the medica & McDaniel, 2015; Viechtbauer & Cheung, 2010). sciences (American Psychological Association, 2008
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REPLY TO HILGARD, ENGELHARDT, AND ROUDER (2017)
. y l d . a s r e o r h s b i l d e b t u a p i n d m e i l e s l s a i d s t i e f b o o e t n t o o r n s o i n d o n i t a a i c r e s o s u s l A a u l d a c i i v g i o d l n o i h e c h t y s f P o n e a c s u i r e l a m n s A o r e e h p t e y t b h r d o e f t h l y g i e r l y o p s o c d e s d i n t t e n n e i m i s u c e o l d c
its number of samples ( k ) and individual observations ( N ). Colexceptions, particularly for PET-PEESE (e.g., aggres umns 4–10 display the naïve meta-analytic results, including the all experiments and aggressive affect—best experime RE meta-analytic mean (the naïve mean; ¯ r o), the 95% confidence interval, the 90% prediction interval (PI), Cochran’s Q statistic, I 2, Discussion tau (), and the one-sample removed analysis (minimum, maximum, and median mean estimates). Columns 11–18 show the Recent research indicates that publication bias and results from the trim-and-fill analyses; for the recommended FE as distort meta-analytic results and associated concl well as the RE model, respectively. For each model, the table Banks, Kepes, & McDaniel, 2015; Kepes, Banks, & includes the side of the funnel plot on which the imputed samples Kepes & McDaniel, 2015; Viechtbauer & Cheung, 20 are located (FPS), the number of imputed samples ( ik ), the trimet al. (2017) concluded that some of the Anderson and-fill adjusted mean effect size (t&f FE ¯ r o or t&f RE ¯ r o), and the overestimated the impact of violent video game playin respective 95% confidence interval. Column 19 contains the cusive tendencies. Below, we will address some of the m mulative mean for the five most precise samples ( pr 5 ¯ r o). Columns sions of Hilgard, Engelhardt, and Rouder. 20 and 21 illustrate the results from the moderate (sm m ¯ r o) and severe selection (sm s ¯ r o) models. Column 22 contains the result of the PET-PEESE (pp ¯ r o) analysis). Finally, although not discussed Bias in Naïve Meta-Analytic Mean Estimates in the Results section because of space considerations, we have Experimental Data included the forest plots that display the cumulative meta-analyses Hilgard et al. (2017), noted that they by precision in the supplemental materials (for interpretation guidelines, see Kepes et al., 2012). Because of space limitations, detect[ed] substantial publication bias in experimental r we also focused on experimental effects, which are the effects effects of violent games on ‘aggressive affect’ and ‘agg Hilgard et al. (2017) claimed were most biased. Obviously experior’ and that ‘after adjustment for bias,’ the effects of v imental effects also allow the strongest causal inferences. on aggressive behavior in experimental research Upon first glance, our results for experimental studies seem to being very small, and estimates of effects on aggress be aligned with the results reported by Hilgard et al. (2017). Like much reduced. (p. 757) Hilgard, Engelhardt, and Rouder, we found that many of the naïve Although we agree that some the naïve meta-ana meta- analytic mean estimates were adversely affected by publiYou're Readinginvolving a Preview experimental studies reported by Anderson cation bias. However, contrary to Hilgard, Engelhardt, and Rouder, appear to have been adversely affected by publication we did not obtain results that would come close to Unlock nullifying full the access with a free trial. not agree with the notion that the effects are ‘very original naïve meta-analytic mean reported by Anderson et al. publication bias was considered. As our results in (2010). For example, for the aggressive affect—best experiments, for the potential influence of public Free Trial all but the PET-PEESE publication bias assessmentDownload methods in- Withaccounting outliers, most mean correlations between exposure to v r o dicate that the originally obtained naïve meta-analytic mean ( ¯ games and aggressive behavior in experimental .32) may be overestimated by potentially .05–.09 (15–33%) after between .15 and .25. Effect sizes of this magnitude a the deletion of identified outliers (e.g., t&f FE ¯ r o .24, t&f RE ¯ r o in size. Indeed, most effects observed in social scienc .24, pr 5 ¯ r o .22, smm ¯ r o .27, sms ¯ r o .26). Only the magnitude. For example, one meta-analysis examine PET-PEESE estimate suggests a vastly different mean estimate (pp tude of effects obtained in social psychology studie r ¯ o .0), indicating that the results of this method did not converge past century. The average effect size obtained from well with the results of the other, more established methods. By analyses Sign of more than 25,000 social psychology stud up to vote on this title contrast, for the aggressive behavior—best experiments distribumore than 8 million participants was ¯ r .20 (Richa tion, the most important distribution for drawing causal inferences Useful Not useful Stokes-Zoota, 2003). about the effects of violent video games on aggression, it appears Also, although the reduction in the mean estimates s as if neither outliers nor publication bias adversely affected the magnitude for the distributions involving aggressive naïve meta-analytic mean. After the deletion of one outlier,
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KEPES, BUSHMAN, AND ANDERSON
cially after outlier removal, the results of the various pub assessment methods converged, increasing our conf Hilgard et al. (2017) recommended the exclusion of two studies. obtained results and associated conclusions. Although their exclusion may be justifiable based on conceptual or We do not dispute that publication bias is a seriou methodological grounds, we did not find support for the notion that general or that it may have affected some of the esti the four samples in these two studies had a real meaningful effect Anderson et al. (2010) meta-analysis. In fact, we fou on the obtained meta-analytic results, regardless of whether or not liers, in addition to publication bias, affected we took the potential effects of publication bias and outliers into reported by Anderson et al. We also echo prior calls consideration. Furthermore, we found that more than one identihensive reanalyses of previous published meta-ana fied outlier was detected in several meta-analytic distributions. The (e.g., Kepes et al., 2012). However, such reanalyses s leave-one-out method used by Hilgard, Engelhardt, and Rouder is best-practice recommendations and, therefore be not capable of handling such situations. Relatedly, our results ducted with appropriate and endorsed methods inste indicated that outliers, in addition to publication bias, did have a on relatively new and potentially unproven method noticeable effect on the originally reported mean estimates (An p-uniform and p -curve. derson et al., 2010). Thus, outliers and publication bias had a We also agree with the suggestion of Hilgard, Eng combined adverse effect on the meta-analytic mean estimates, Rouder (Hilgard et al. 2017) to combat publication although neither outliers nor publication bias dramatically changed the prospective registration of meta-analyses (see the main conclusions of the Anderson et al. meta-analytic study. In Daniel, 2013), as the International Committee of Med other words, the Anderson et al. (2010) conclusions remain valid. Editors requires for clinical trials (De Angelis et al We also found that the PET-PEESE results did not always nally, we agree with numerous other recommendati converge well with the other methods under conditions of noticefrom alternative editorial review processes to more s able heterogeneity, as is often the case with real data in the social sharing requirements and a closer attention to the stat sciences (see Moreno et al., 2009). As an example, PET-PEESE of our primary studies, that have been made tended to function relatively poorly for the aggressive affect—all accuracy and trustworthiness of our cumulative scien experiments distributions when compared with the other methods, edge (e.g., Banks et al., 2015; Kepes, Bennett, & McD even after the deletion of the one identified outlier, potentially Kepes & McDaniel, 2013; Maxwell, 2004; O’Boy because of the relatively large heterogeneity in the data (i.e., before ReadingGonzalez-Mulé, a Preview 2017). the removal of the outlier: Q 111.22, I 2 67.63, You're .16; 90% As indicated by the results of our cumulative met PI .05, .47; after the removal of the identified outlier: Q precision, both the cumulative mean of the five most pre Unlock full access with a free trial. 75.53, I 2 53.66, .12; 90% PI .0, .38). (see Table 1) and the forest plots of the complete cumu analyses (see our supplemental materials), it seems evide Limitations and Strengths Download Withsample Freestudies Trial with small magnitude effects (most likel Although our findings regarding the influence of publication that failed to reach the magical p value threshold of .05 and other biases on meta-analytic mean estimates echo the results suppressed from the publicly available literature (see of prior research (e.g., Banks et al., 2015; Kepes & McDaniel, 2012). By contrast, from the forest plots in our supplem 2015; Viechtbauer & Cheung, 2010), our meta-analytic study, like als, one may infer that small sample studies (i.e all meta-analyses, has limitations. For example, all methods used studies) that, maybe by chance, reached an acceptable le to assess the potential presence of publication bias have their tical significance (i.e., p .05) were getting published. T shortcomings, especially with heterogeneous data (Kepes et al., publishing seems to have adversely affectedour cumu Sign up vote on this title 2012; Kepes & McDaniel, 2015). That is why we looked for edge regarding thetoeffects of violent video games. convergence across methods when triangulating the true underlyFinally, we acknowledge that our Useful Not useful conclusions ma ing mean effect. Furthermore, by forming theoretically derived more evidence regarding the superiority of an exi subgroup distributions and deleting the outliers that were identified publication bias assessment method becomes availabl by a comprehensive battery of multivariate influence diagnostics given that we used multiple recommended methods
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. y l d . a s r e o r h s b i l d e b t u a p i n d m e i l e s l s a i d s t i e f b o o e t n t o o r n s o i n d o n i t a a i c r e s o s u s l A a u l d a c i i v g i o d l n o i h e c h t y s f P o n e a c s u i r e l a m n s A o r e e h p t e y t b h r d o e f t h l y g i e r l y o p s o c d e s d i n t t e n n e i m i s u c e o l d c
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REPLY TO HILGARD, ENGELHARDT, AND ROUDER (2017)
. y l d . a s r e o r h s b i l d e b t u a p i n d m e i l e s l s a i d s t i e f b o o e t n t o o r n s o i n d o n i t a a i c r e s o s u s l A a u l d a c i i v g i o d l n o i h e c h t y s f P o n e a c s u i r e l a m n s A o r e e h p t e y t b h r d o e f t h l y g i e r l y o p s o c d e s d i n t t e n n e i m i s u c e o l d c
Anderson et al. considered numerous moderators (e.g., participant gender; participant age; Eastern vs. Western country; type of design— experimental, cross-sectional, or longitudinal; type of outcome—aggressive cognition, aggressive affect, physiological arousal, aggressive behavior, empathy, helping; game characteristics such as human vs. nonhuman targets, first- vs. third-person perspectives), these moderators did not fully account for the between-study heterogeneity observed in the effects. Thus, future research should examine other possible moderator variables, such as publication year (to see whether the effects have changed over time), amount of blood and gore in the game, whether the violence is justified or unjustified, whether players use a gun-shaped controller or a standard controller, whether the video game is played cooperatively or competitively, and whether the video game is played alone or with other players, to name a few. There were not enough studies to test these latter potential moderators in 2010, but there may be now.
Conclusion
Copas, J., & Shi, J. Q. (2000). Meta-analysis, funnel plots a analysis. Biostatistics, 1, 247–262. http://dx.doi. biostatistics/1.3.247 De Angelis, C., Drazen, J. M., Frizelle, F. A. P., Haug, Horton, R., . . . the International Committee of Medica (2004). Clinical trial registration: A statement from the Committee of Medical Journal Eds. New England Journa 351, 1250–1251. http://dx.doi.org/10.1056/NEJMe04 Duval, S. J. (2005). The “trim and fill” method. In H. R. Sutton, & M. Borenstein (Eds.), Publication bias in Prevention, assessment, and adjustments (pp. 127–144). UK: Wiley. Graybill, D., Kirsch, J. R., & Esselman, E. D. (1985). Effe violent versus nonviolent video games on the aggressi aggressive and nonaggressive children. Child Study Jour 205. Greenhouse, J. B., & Iyengar, S. (2009). Sensitivity analysis tics. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), of research synthesis and meta-analysis (2nd ed., pp. 41 York, NY: Russell Sage Foundation. Hedges, L. V., & Vevea, J. L. (2005). Selection methods a H. R. Rothstein, A. Sutton, & M. Borenstein (Eds.), Publ
In conclusion, the trustworthiness of our cumulative knowledge meta analysis: Prevention, assessment, and adjustments regarding the effects of violent video games is of clear concern to West Sussex, UK: Wiley. society, which is why we applaud Hilgard et al.’s (2017) attempt Higgins, J. P., & Green, S. (Eds.). (2011). Cochrane handbo to assess the trustworthiness of this literature. However, our conatic reviews of interventions; version 5.1.0 [updated Sep clusions about violent video game effects differ from those of The Cochrane Collaboration. Available at www.cochr Hilgard, Engelhardt, and Rouder. Contrary to the conclusions of .org Hilgard, Engelhardt, and Rouder, ours are based on results from a Hilgard, J., Engelhardt, C. R., & Rouder, J. N. (2017). Overs You're Reading a Preview comprehensive battery of sensitivity analyses and are thus likely to for short-term effects of violent games on affect be more robust to potential adverse effects. of Anderson et al. (2010). Psychological Bulle Unlock full access withreanalysis a free trial. There was convergence in our results across various different 774. http://dx.doi.org/10.1037/bul0000074 Jick, T. D. (1979). Mixing qualitative and quantitative meth methods when we triangulated the true underlying mean effect for lation in action. Administrative Science Quarterly, 24, Download Trial the relations between violent video games and aggression. Con- With Free dx.doi.org/10.2307/2392366 trary to what Hilgard et al. (2017) suggested, that effect was not Kepes, S., Banks, G. C., McDaniel, M. A., & Whetzel, very small in size. As stated in our title, although the magnitude of Publication bias in the organizational sciences. Organizati the mean effects were reduced by publication bias and outliers, Methods, 15, 624–662. http://dx.doi.org/10.1177/1094 “violent video game effects remain a societal concern.”
References Aguinis, H., Werner, S., Abbott, J. L., Angert, C., Park, J. H., & Kohlhausen, D. (2010). Customer-centric science: Reporting significant research results with rigor, relevance, and practical impact in mind. Organizational Research Methods, 13, 515–539. http://dx.doi.org/10.1177/ 1094428109333339
Kepes, S., Banks, G. C., & Oh, I.-S. (2014). Avoiding bias bias research: The value of “null” findings. Journal of Psychology, 29, 183–203. http://dx.doi.org/10.1007/s1086 (2014). Ev Kepes, S., Bennett, A. A., & McDaniel, M. A. Sign and up to on this title management thevote trustworthiness of our cumulative sci edge: Implications teaching, research, and pract Useful for Not useful Management Learning & Education, 13, 446– 466. http: .5465/amle.2013.0193 Kepes, S., & McDaniel, M. A. (2013). How trustworthy is
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and some cautionary notes. Perspectives on Psychological Science, 11, 730–749. http://dx.doi.org/10.1177/1745691616662243 Moreno, S. G., Sutton, A. J., Ades, A. E., Stanley, T. D., Abrams, K. R., Peters, J. L., & Cooper, N. J. (2009). Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study. BMC Medical Research Methodology, 9, 2. http://dx.doi .org/10.1186/1471-2288-9-2 O’Boyle, E. H., Jr., Banks, G. C., & Gonzalez-Mulé, E. (2017). The chrysalis effect: How ugly initial results metamorphosize into beautiful articles. Journal of Management, 43, 376–399. http://dx.doi.org/10 .1177/0149206314527133 Panee, C. D., & Ballard, M. E. (2002). High versus low aggressive priming . y l d . a s r e o r h s b i l d e b t u a p i n d m e i l e s l s a i d s t i e f b o o e t n t o o r n s o i n d o n i t a a i c r e s o s u s l A a u l d a c i i v g i o d l n o i h e c h t y s f P o n e a c s u i r e l a m n s A o r e e h p t e y t b h r d o e f t h l y g i e r l y o p s o c d e s d i n t t e n n e i m i s u c e o l d c
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