Fluent Essays

Note from Instructor:   I would suggest putting thoughts in your own words by paraphrasing, summarizing, and putting ideas into your own words. Changing a few words in a sentence wont get it. Free of plagiarism  Article  Video Game Violence Use Among ‘‘Vulnerable’’ Populations: The Impact of Violent Games on Delinquency and Bullying Among Children with Clinically Elevated Depression or Attention Deficit Symptoms Here I have compiled a list of common problems that see · Match numbers with Turnitin are going up, please be very careful to put your annotated bibliography summaries in your own words (summarize, paraphrase, and change sentence structure. Please be sure to put in your own words and cite). No more than 1 short direct quote (<10\% total paper). · The reference goes at the top of the page in APA format, before your annotated bibliography, not after it. · The journal indicates, the article speaks is not correct. The authors (or spell out the authors last names in APA format with date). Journal and articles cannot speak, indicate, discuss, etc. Also, using “it” discusses should not be used in formal writing. · You cannot have an “amount of people or participants,” you can have a number or percentage of people. “In regards to” is incorrect, it’s in regard to. · APA format is NOT MLA format, please be sure that you are using the correct format. You will continue to lose points for not using APA format. · Do not use first or second person when writing an annotated bibliography (I, you, we). An annotated bibliography is a short summary of the article, there is no reason to use I, you are not part of it. You and the “collective we” are generally not used in formal writing. Use third person. · Limitations are related to the methodology in the study and generally potential threats to internal (rigor) or external validity (generalization of results). Look to the discussion section of article for cues, authors often discuss the limitations of the study, such as unexpected issues that come. · Single subject designs and cases studies, pilot studies, small sample size all have a potential impact on external validity. A number of factors can be a potential threat to internal validity, see link below: https://cyfar.org/ilm_3_threats · Some students have difficulty with being specific and using it or this, in lieu the specific source, such as… The investigators, The researchers, the participants, etc. PSY 499 Annotated Bibliography Rubric Instructor comments: First and foremost, please follow the organization of material outlined in the grading rubric. Always start with the purpose of the study. This is the normal flow of a research paper. End with limitations/critique and implications for future research. Criterion #1 Please be sure to address the purpose of the study you are reviewing. If there is a hypothesis or research question, please be sure to include it in your own words (summarize). For articles that are case studies or are qualitative research, there may goals and objectives rather than a purpose and hypothesis. Criterion #2 Please address the methods adequately. Discuss the participants and include the demographics, specific material used, and procedures for the study. There should be enough detail in the methods so that the reader could design a replication. For meta-analyses, you may list the number of studies used, the inclusion or exclusion criteria, and the total number of participants. For articles with qualitative analyses there are unique issues that need to be addressed. If there were subjects, they need to be discussed. If the authors used interviews or documents, how where they coded and analyzed? Describe the methods used as clearly as you can. Criterion #3 Describe the results. Were there significant differences between groups or treatments, correlations between variables etc. Be sure to address in a statement what the results mean with regard to past research (does it support or refute it?). Also, please address the significance of the findings. What are the implications for the research (e.g., policy change, educational reform, future research, etc.). NOTE: There are a few areas where students tend to have questions regarding the grading rubric. First, criterion #3, “Results and Significance,” is in regard to the study you are reviewing, not your thesis paper. The rubric does not cover how you are going to use the research, so it does not need to be covered in the annotated bibliography 11 Criterion #4 This criterion addresses strengths and limitations or critique of the article. Limitations are generally discussed in the discussion section of the article. APA guidelines for both qualitative and quantitative articles calls for a discussion of limitations (Cooper, 2020; Levitt, 2019). Look in the discussion section of the article for this information. Remember, no study is perfect. Critique can also be based on theoretical differences or inconsistencies, etc. Please be sure to critically evaluate the article objectively (based on methodology, etc.). Think of the critique as limitations of the study in terms of rigor (threats to internal validity) and generalizability (threats to external validity). Limitations are neither good or bad, do not be judgmental, rather factual, dispassionate, and objective. Some Common Limitations • The article is supported by a for profit organization, such as the pharmaceutical industry. The industry controls the data and what gets published and what doesn’t (potentially biased data). • The subject pool or participant pool was drawn from a convenience sample, is too small, or not representative of the population (potential threat to external validity). • Treatment not having sufficient follow up, such as a 12-week treatment, but no follow-up after 3 or 6 months (does not reflect data on long term effects of treatment). • Insufficient length of the treatment (treatment may have not been sufficient to show effect). • Lack of a control or comparison group in a within group pre-test post-test design. • Using a questionnaire or survey without established reliability and validity. Construct and content validity information can be found in the methods section of the article. • A pilot study that is not based on previous research (insufficient comparison data with other studies available). • Theoretical critique of the study based on the fact that another theory may explain the results. • Subject or participant bias can occur with self-report questionnaires and interviews. Halo effect. 12 • Single subjects experimental designs only have one participant. Without comparing results to other studies, it’s difficult to determine if the results are due to the experimental manipulation or chance. You should draw from your knowledge of Research Methods I & II to address these issues. NOTE: In the case of qualitative studies, you may see the term “transferability,” or do these results transfer to similar situations. Also, rather than rigor, you may see terms like transparency, fidelity, and/or utility. In other words, is the study presented in such a way that you can follow it from start to finish? Are all descriptions adequate? Do they follow from one section to another? Do the results have utility in similar “real world” situations? Potential Threats to Internal Validity (experimental rigor) History (local history) common in cross-sectional designs by age. Some event in one group’s history caused the results, not the treatment. Maturation can occur in longitudinal studies where something in the group’s development causes the results, not the treatment. Testing (listed above) does the test have adequate established reliability and validity. Faulty testing causes the results, not the treatment. Instrumentation, similar to testing has to do with the accuracy of the instruments used to collect the data. If the instruments are not accurate, neither will the results. Statistical regression can occur with test retest studies, scores will tend to cluster around the mean. Selection (covered above) criteria and whether the sample is a convenience sample, stratified sample, etc. Lack of random assignment or matched subjects. Potential Threats to External Validity (generalization) Sample size and demographic composition can impact the results of a study’s ability to generalize to the population. For more on threats to internal and external validity see link below https://web.pdx.edu/~stipakb/download/PA555/ResearchDesign.html https://www.scribbr.com/methodology/internal-vs-external-validity/ NOTE: Your annotated bibliographies are NOT part of your thesis paper. Use the information from the article, do not copy and paste annotated bibliographies together to make up the body of your paper. Consider the annotated bibliographies article summaries only. Criterion #5 Make sure that your annotated bibliographies are 200 - 300 words. Less than 200 words may not cover everything on the grading rubric. Helpful Links The links below will be helpful with setting up your annotated bibliographies https://owl.purdue.edu/owl/general_writing/common_writing_assignments/annotated_bibliograp hies/annotated_bibliography_samples.html This link gives an example for formatting of a book. Please noted that it does not share the same grading criteria for your assignments, so the content is different (see Grading Rubric). http://libguides.enc.edu/writing_basics/annotatedbib/apa This link also provides information on formatting, but does not include a complete criterion for grading your assignment (see Grading Rubric) http://advice.writing.utoronto.ca/types-ofwriting/annotated-bibliography/ This link gives an example of the correct format and some of the criteria that you will be required to include, for a full list of criteria, see the Grading Rubric. https://sites.umuc.edu/library/libhow/bibliography_apa.cfm This website has two examples of an annotated bib in APA format. Note it does not follow the same criteria as our assignment. https://www.youtube.com/watch?reload=9&v=lPhWhRlEWtI Putting an Annotated Bibliography into APA format. This is good example using MS Word. There are a few things to note. First, acceptable formats are Times New Roman, Arial, and Courier 12 font. The other issue presenter forgot the issue number. An issue number should be in parentheses (2) between the volume number and the page numbers. The Assignment Needs During this module, you will submit your third annotated bibliography. This is a clear and concise summary (200 to 300 words) of a journal article, book, or other primary academic source that will be used in your thesis paper. Each submission must also include a brief critique of the source (e.g., how could the study be improved, criticism of the author(s) assertions, ideas for future studies, etc.). E M P I R I C A L R E S E A R C H Video Game Violence Use Among ‘‘Vulnerable’’ Populations: The Impact of Violent Games on Delinquency and Bullying Among Children with Clinically Elevated Depression or Attention Deficit Symptoms Christopher J. Ferguson • Cheryl K. Olson Received: 18 April 2013 / Accepted: 17 July 2013 / Published online: 24 August 2013 � Springer Science+Business Media New York 2013 Abstract The issue of children’s exposure to violent video games has been a source of considerable debate for several decades. Questions persist whether children with pre-existing mental health problems may be influenced adversely by exposure to violent games, even if other children are not. We explored this issue with 377 children (62 \% female, mixed ethnicity, mean age = 12.93) dis- playing clinically elevated attention deficit or depressive symptoms on the Pediatric Symptom Checklist. Results from our study found no evidence for increased bullying or delinquent behaviors among youth with clinically elevated mental health symptoms who also played violent video games. Our results did not support the hypothesis that children with elevated mental health symptoms constitute a ‘‘vulnerable’’ population for video game violence effects. Implications and suggestions for further research are provided. Keywords Video games � Aggression � Violence � Mental health Introduction Whether violent video games do or do not contribute to behavioral aggression and societal violence among youth has been debated, at the time of this writing, for three decades. By societal violence, we refer to a range of behaviors, from bullying and physical fighting to criminal assault and even homicide, which are of concern to law- makers and parents. We contrast societal violence with the measures of relatively mild aggression (or perhaps com- petition) often used in laboratory studies of college stu- dents, which arguably do not tap well into the issue of societal violence (Kutner and Olson 2008). Caution is required in generalization of laboratory aggression mea- sures to societal violence as the potential for misinformation is considerable (Ferguson et al. 2011). To date, no con- sensus has been reached on the matter of whether violent games and societal violence are linked: some scholars argue that violent games contribute to behavioral aggression (Fraser et al. 2012) or even societal violence (Strasburger 2007), while others suggest that video games have a neg- ligible influence on aggression (Puri and Pugliese 2012) or may even reduce aggression (Colwell and Kato 2003). Existing societal concerns about video games have intensified after the 1999 Columbine High School massacre (Ferguson 2013) and other well-publicized school shootings. The tragic 2012 Sandy Hook Elementary School murders in Newtown, Connecticut resurrected these debates amid reports that the 20-year-old shooter was an avid gamer (e.g., Henderson 2012). The Newtown shooting also brought renewed attention to wide discrepancies in opinion regarding whether violent video games influence criminal behavior. The Brown v EMA (2011) Supreme Court decision, in which the Court ruled that a California law restricting the sale or rental of violent games to minors was an unconstitutional violation of the First Amendment, highlighted the limitations of existing studies of violent video games and the difficulty of applying this pool of research to policy-relevant questions. A series of appellate court rulings made similar points (see Brown v EMA 2011, p. 12). Given these court rulings, and C. J. Ferguson (&) Department of Psychology, Stetson University, DeLand, FL 32729, USA e-mail: [email protected] C. K. Olson Reston, VA, USA 123 J Youth Adolescence (2014) 43:127–136 DOI 10.1007/s10964-013-9986-5 the recurring media focus on video games, researchers need to do more to answer the questions of greatest public concern regarding video games and any potential harm to youth. The recurrence of these concerns with each school shooting or court ruling points to the need for studies that can mean- ingfully inform policy and legal debates. Video Game Violence Research: What is the Evidence? Much speculation focuses on the issue of whether violence in video games or other entertainment media, such as television, can contribute to real-life violence. Evidence to date is scant. For instance, in a recent meta-analysis that focused on criminal aggression, Savage and Yancey (2008) found that exposure to media violence shared only trivial amounts of variance with criminal aggression. Similarly, in a large sample of youth aged 10–15, Ybarra et al. (2008) found that violent media exposure did not predict violence once other confounding variables were controlled. It is also noteworthy that the explosion in popularity and availability of video games has coincided with a precipitous decline in youth violence, not a rise (see Ferguson 2013 for discussion). There exists a large pool of studies examining video game violence effects in college students using laboratory methods and measures of relatively mild aggression. The validity of these measures has been debated within the research community (e.g. Giancola and Zeichner 1995; Ritter and Eslea 2005). One point of contention is the lack of clear correspondence between these measures and the types of aggressive behaviors of interest to policy makers and parents. For instance, such studies have examined outcomes such as filling in the missing letters of words, where ‘‘kill’’ rather than ‘‘kiss’’ is considered more aggressive (Farrar et al. 2013); self-ratings of hostile feelings (Williams 2011); or administering non-painful bursts of annoying noise to consenting opponents in a reaction-time test (Anderson and Dill 2000). Taken at face value, such studies may be generalizable to competitive- ness rather than aggression, or perhaps to mild aggressive acts (the equivalent of children sticking tongues out at each other), but cannot be generalized to societal violence. Even these studies produce mixed results, however, and have been criticized for methodological issues such as failing to match violent and non-violent video game play conditions carefully (Adachi and Willoughby 2011), using unstan- dardized outcome measures that may allow researchers to pick and choose outcomes fitting their hypotheses (Fergu- son 2013), and high potential for demand characteristics. By contrast, studies of video game effects on violent behaviors among children, conducted outside laboratory settings, remain relatively few in number. Such studies differ in quality and standardized approach to measure- ment. One study (Anderson et al. 2008) found weak links between video game violence and aggression in US and Japanese children, although interpretation of results is complicated by the use of non-standard measures of aggression and inadequate control for other variables. A later German study tying media violence, including video game play, to aggression in children (Krahé et al. 2012) also did not use standardized assessments. That study may have been compromised by the introduction of a media education program into the schools mid-way through the longitudinal period (e.g., Möller et al. 2012) introducing demand characteristics (i.e., advertising the study hypoth- eses to prime respondents to answer surveys in a particular way, not representative of how they actually behave). Another recent study that links violent games with aggression, by Willoughby et al. (2012), carefully con- trolled for important ‘‘third’’ variables. With other vari- ables controlled, exposure to violent video games correlated with later aggression with an effect size equiv- alent to r = .07, indicating that violent game use was associated with approximately half a percent increase in aggressive behavior. The authors noted, however, that it may be competitive qualities of the games, not violent content, which led to this increase (see Adachi and Wil- loughby 2011). In a follow-up longitudinal study (Adachi and Willoughby 2013), the authors confirmed that com- petition predicts later aggression, irrespective of violent game exposure history. Few other studies of children and video games have made a solid case for a connection to aggression or violent outcomes. Several have suggested that use of violent video games might reduce aggression (Colwell and Kato 2003; Shibuya et al. 2008 1 ). Others indicate that, with other factors controlled, effects are null (Ferguson 2011; von Salisch et al. 2011; Wallenius and Punamäki 2008; Ybarra et al. 2008) or that effects may be idiosyncratic among children (Unsworth et al. 2007). Meta-analyses (e.g., Sherry 2007) have found weaker effects in studies of children than for college students, the opposite of what might be expected developmentally. Thus, overall, it is 1 We note the issue that some research reports insinuate links between violent games and aggression, where their data fail to support such insinuations. We note that in Shibuya et al. 2008, in their Table 2, the video game exposure by violence presence variable is associated with a reduction in aggression in boys, but not girls. For Ybarra et al. (2008) the null effect for violent video games is noted in their Figure 2, although they largely ignore their own results to imply links between violent games and youth aggression. These papers highlight the need to closely examine research results when under- standing the true implications of a research study. The rhetoric employed by scholars in their abstracts and discussion sections does not always match their data. 128 J Youth Adolescence (2014) 43:127–136 123 difficult to make clear conclusions about links between video game violence and childhood aggression or violence. Post-Sandy Hook, a view emerged, typified by the report of the US House of Representatives Gun Violence Pre- vention Task Force (2013), that current research probably did not support concerns that the average child was harmed by video game violence. Rather, attention should be focused on prevention and early intervention with ‘‘at-risk youth,’’ with particular emphasis on mental health. This is a reasonable hypothesis, but one that has not been studied extensively. Several studies of college students by Patrick Markey found that violent video games may interact with preexisting anger symptoms in some young adults to increase hostility, although he has been cautious about extending these findings to violence in children (Giumetti and Markey 2007; Markey and Markey 2010; Markey and Scherer 2009). These warnings are consistent with those of criminologists who warn against generalizing laboratory aggression measures to criminal violence (Savage 2008). One recent analysis with children (Ferguson 2011) was unable to confirm the hypothesis that children with pre- existing antisocial traits were adversely influenced by violent video games. However, more research would cer- tainly be welcome. The Current Study The current study is intended to address gaps in the existing literature by considering the impact of exposure to violence in video games on criminal delinquency and bullying behaviors in a sample of children with clinically elevated mental health symptoms. It is important to note at the outset that the vast majority of children with mental health symptoms do not engage in violent behavior. Although some symptoms of mental health problems such as depression (Ferguson 2011) and attention deficit disorder (Wymbs et al. 2012) have been identified as risk factors for aggressive or violent behavior, this occurs only in combi- nation with other significant risk factors, not as a direct result of the mental health symptoms. Thus, scholars must exercise caution not to further stigmatize mental illness by insinuating links with violence. Rather, our analyses are intended to address the hypothesis that children with clinically elevated mental health symptoms consistitute a ‘‘vulnerable’’ population of individuals who may be susceptible to video game violence effects even if clinically ‘‘normal’’ children are not. We thus test two main hypotheses. First, it was hypothesized that children with clinically elevated symptoms of depression will demonstrate a correlation between violent video game exposure and criminal delinquency and bul- lying behavior-related outcomes. Second, it was hypothesized that children with clinically elevated atten- tion deficit symptoms will demonstrate a correlation between violent video game exposure and criminal delin- quency and bullying behavior related outcomes. Methods Participants The current study includes a subset of participants from a large federally funded project examining video game vio- lence effects on youth. Details related to the initial devel- opment and recruitment for this project can be found at Kutner and Olson (2008). Only children who scored in the clinically significant range on clinically validated scales related to depressive or attention deficit symptoms (scales discussed below) were included in the current analyses. These included 377 children: 182 with clinically elevated attention deficit symptoms, and 284 with clinically ele- vated depressive symptoms. Clinically elevated symptoms were comorbid for 89 (23.6 \%) children. There were 234 females in the sample and 140 males (3 chose not to report their gender). The mean age of the children was 12.93 (SD = .76). Children were recruited from both an urban and suburban school. The ethnic makeup of students in the urban school was 50 \% white, 43 \% black, 2 \% Asian, 5 \% Hispanic and\1 \% other. The ethnic makeup of students in the suburban school was 90 \% white, 4 \% black, 4 \% Asian, 1 \% Hispanic and 1 \% other (individual students were not asked to report their ethnic background). Measures Depression/Attention Symptoms Symptoms of depression and attention-deficit/hyperactivity problems were assessed using the relevant subscales of the youth self-report version of the Pediatric Symptom Check- list—17 (PSC; Gardner et al. 1999). This instrument is a validated, brief screening device for mental health problems in children, and provides clinical cut-offs to identify children whose symptoms merit further evaluation. Participants were asked to rate whether they experienced particular mental health symptoms ‘‘never,’’ ‘‘sometimes’’ or ‘‘often.’’ With the current sample, coefficient alpha for the ADHD subscale was .75 and for the depression subscale .80. The sample reported mean was 5.41 and standard deviation was 2.28. Trait Aggression The Attitudes Toward Conflict scale (ATC; Dahlberg et al. 1998) consists of eight Likert items related to potential J Youth Adolescence (2014) 43:127–136 129 123 aggressive responses to various hypothetical situations. Sample items include, ‘‘It’s OK for me to hit someone to get them to do what I want’’ and ‘‘I try to talk out a problem instead of fighting.’’ Due to the stability in trait aggression it is commonly regarded as an important control variable and we include it here for this reason. Trait aggression correlated with video game exposure at r = .24 for youth with elevated attention deficit symptoms and .23 for youth with elevated depressive symptoms. However, predictive relationships between exposure to video game violence and trait aggression became non-significant in regression equations with gender, parental involvement, stress and family/peer support controlled. Thus, we are confident that our use of trait aggression as a control var- iable does not miss relationships between video game violence and trait aggression with other factors controlled. Coefficient alpha for the current sample for the ATC was .76. The sample reported mean was 16.48 and standard deviation was 4.60. Parental Involvement To measure parents’ involvement with their children’s media use, sharing media consumption with children and making media consumption decisions for them, a nine-item Likert-scale was created for this study. Examples of questions included in this scale are ‘‘My parents play electronic games with me,’’ and ‘‘My parents tell me I can’t play a particular electronic game.’’ Coefficient alpha for the current sample was .68. The sample reported mean was 18.48 and standard deviation was 4.12. Support from Others We compiled a sixteen item Likert-scale measure of per- ceived support from peers and family. This measure was based on two existing measures (Lerner et al. 2005; Phillips and Springer 1992) of peer support and family support. Overall coefficient alpha for the resultant scale was .87. The sample reported mean was 44.35 and standard devia- tion was 10.22. Stress The Stressful Urban Life Events scale (SULE; Attar et al. 1994), a 19 item yes/no scale, was used to measure total stress that children in the current sample had experienced during the past year. The SULE addressed stressors such as getting suspended from school, getting poor grades on one’s report card, or experiencing the death of a family member. Coefficient alpha for the total stress scale was .67 for the current sample. The sample reported mean was 4.82 and standard deviation was 2.96. Exposure to Video Game Violence In the current study, we used Entertainment Software Ratings Board (ESRB) video game ratings as an estimate of exposure to violence in video games. Respondents were asked to write the names of five video games that they had ‘‘played a lot’’ in the past 6 months. ESRB ratings were then obtained for each game, and ordinally coded (a maximal score of 5 for ‘‘Mature,’’ 4 for ‘‘Teen,’’ etc.). The sample reported mean was 29.97 and standard deviation was 30.09. Many factors go into an ESRB rating, including lan- guage, sexual content, and use of (or reference to) drugs or gambling. However, among those factors that determine the age-based rating, violence appears to take priority. Descriptors of listed games were reviewed to ensure that high ratings had not been obtained primarily for sexual content; this was not the case for any of the games. Common violence-containing games named by participants included those in the Halo, Grand Theft Auto, and Mortal Kombat series. The ratings were summed across the 5 games listed, then multiplied by the number of hours per week that the child reported playing video games. As with all attempts to assess game content exposure, this is only an estimate; however, it removes some of the subjectivity inherent in previous methods. This approach has been found to be reliable and valid in previous research (Fer- guson 2011; Lenhart et al. 2008). Delinquency A six-item Likert scale of general delinquency was com- piled from several existing delinquency scales (Brener et al. 2002; Elliot et al. 1985; Leffert et al. 1998). Ques- tions addressed physical aggression (been in a physical fight; hit or beat up someone) as well as more general delinquency (stole something from a store; got into trouble with the police; damaged property just for fun, such as breaking windows, scratching a car, or putting paint on walls; skipped classes or school without an excuse). Par- ticipants were asked to report how often these behaviors occurred within the previous twelve months. Coefficient alpha for the resultant scale was .75 for the current sample. The sample reported mean was 3.00 and standard deviation was 3.95. Bullying The Revised Olweus Bully/Victim Questionnaire (Olweus 1996) was used to assess bullying behaviors. The bullying perpetration scale consisted of 9 items in which partici- pants were asked to rate how often they had engaged in bullying behaviors over the past couple of months. Items 130 J Youth Adolescence (2014) 43:127–136 123 inquire about physical aggression, verbal aggression, threats and social exclusion. A coefficient alpha of .86 was obtained for the current sample. The sample reported mean was 2.68 and standard deviation was 4.27. Procedure All procedures described within this study were approved by local IRB and designed to comport with APA standards for ethical human research. An ‘‘opt out’’ procedure was used for student involvement, with parents notified of the study through school newsletters and notices sent home to students. Youth assent for participation was obtained for all participants. Teachers were not present during data col- lection, which occurred during the school day. Primary data analysis used for the testing of the study hypotheses were OLS multiple regressions. Gender, parental involvement, trait aggression, stress, family/peer support and exposure to video game violence, as well as the interaction between exposure to violent video game and trait aggression, were entered simultaneously in the regression equation. In keeping with the recommendations of Simmons et al. (2011), we certify that this analysis approach was selected in advance and was not altered to produce particular results. An interaction between trait aggression and exposure to video game violence was tested by first centering the variables to avoid multicollinearity. Collinearity diagnostics for all regressions revealed absence of any concerns with all VIFs below 2.0. Youth with depressive or attention deficit symptoms will be considered separately. Results Video Game Exposure Children in our sample were generally very familiar with electronic games. Of our sample, 84.4 \% reported playing video games on a computer, 81.2 \% on a console and 50.4 \% on a handheld device in the previous 6 months. Only 6.1 \% reported playing no games at all during that time. Similarly, only 11.4 \% of our sample had no expo- sure to violent video games. Boys had considerably more exposure to violent video games than did girls [t(189.24) = 9.07, p \ .001, r = .46, 95 \% CI = .38, .54]. Kurtosis and skew were acceptable, suggesting a normal distribution of scores. Video Game Influences With the sample of children with clinically elevated depressive symptoms and regarding delinquent criminality as an outcome only stress (b = .30) and trait aggression (b = .42) were predictive of delinquent criminality. Nei- ther exposure to video game violence nor the interaction between trait aggression and exposure to video game vio- lence were predictive of delinquent outcomes. The adjusted R 2 for this regression equation was .36. These results are presented in Table 1. With the same sample of children with clinically ele- vated depressive symptoms but considering bullying behaviors as an outcome, once again only stress (b = .23) and trait aggression (b = .28) were predictive of bullying behaviors. Neither exposure to video game violence nor the interaction between exposure to video game violence and trait aggression were predictive of bullying related out- comes. The adjusted R 2 for this regression equation was .22. These results are presented in Table 2. With the sample of children with clinically elevated attention deficit symptoms and regarding delinquent crim- inality, as with the sample of children with clinically Table 1 Delinquency regression: beta weights and significance of entered variables for adolescents with clinical elevated depressive symptoms Variable b 95 \% confidence interval t test Significance Gender .06 0.92 .36 Parental involvement -.01 -0.05 .96 Stress .30 (.19, .40) 4.73 .001* Family/peer support -.07 -0.96 .34 Trait aggression .42 (.32, .51) 6.08 .001* VGV .04 0.55 .59 VGV 9 trait aggression .04 0.64 .53 VGV exposure to video game violence Table 2 Bullying regression: beta weights and significance of entered variables for adolescents with clinical elevated depressive symptoms Variable b 95 \% confidence interval t test Significance Gender -.11 -1.74 .14 Parental involvement -.01 -0.09 .92 Stress .23 (.12, .34) 3.24 .001* Family/peer support -.05 -0.67 .50 Trait aggression .28 (.17, .38) 3.74 .001* VGV -.07 -0.95 .34 VGV 9 trait aggression -.02 -0.23 .82 VGV exposure to video game violence J Youth Adolescence (2014) 43:127–136 131 123 elevated depressive symptoms only stress (b = .32) and trait aggression (b = .38) were predictive of delinquent criminality. Neither exposure to video game violence nor the interaction between trait aggression and exposure to video game violence were predictive of delinquent out- comes. The adjusted R 2 for this regression equation was .37. These results are presented in Table 3. Finally, with the sample once again of children with clinically elevated attention deficit symptoms and with regards to bullying behavior only trait aggression (b = .41) was predictive of bullying behaviors along with the inter- action between trait aggression and exposure to violent games (b = -.22) suggesting that highly trait aggressive children who also played violent video games were less likely to engage in bullying behaviors. Exposure to Video game violence was not a significant predictor of bullying behaviors. The adjusted R 2 for this regression equation was .19. These results are presented in Table 4. Discussion The 2011 Supreme Court (Brown v EMA 2011) case seemed to have briefly cooled speculation about video game violence effects on children. The tragic 2012 shoot- ing of young children in Newtown, Connecticut by a 20-year-old male reportedly fond of playing violent video games put the issue back on the front burner (Gun Violence Prevention Task Force 2013). The consensus from the government (e.g., Gun Violence Prevention Task Force 2013) seems to have been that current research does not consistently link exposure to video game violence with aggression or societal violence, but more research is nec- essary to assess effects on potentially vulnerable subgroups of children. The current study is an attempt to fill that gap by considering correlational violent video game effects in a sample of youth with clinically elevated mental health symptoms. Our results did not provide support for the hypotheses that exposure to violent video games would be associated with increased delinquency or bullying behav- iors in children with elevated mental health symptoms. Our results indicated that violent video games were associated with neither delinquent criminality nor bullying behaviors in children with either clinically elevated depressive or attention deficit symptoms. Nor did we find support for the belief that trait aggression would interact with video game violence within this sample of youth. That is a particularly interesting finding given that a combina- tion of mental health symptoms and long-term aggressive traits are common elements to attackers who carried out school shootings (US Secret Service and US Department of Education 2002). Our results cannot, of course, be gen- eralized to mass homicides. We do note that our findings with more general forms of youth violence are similar to those of the Secret Service report, in that trait aggressive- ness and stress were risk factors for negative outcomes where exposure to video game violence was not. The only exception was our finding that, for children with elevated attention deficit symptoms, trait aggression and video game violence interacted in such a way as to predict reduced bullying. This could be considered some small correla- tional evidence for a cathartic type effect, although we note it was for only one of four outcomes and small in effect size. Thus we caution against overinterpretation of this result. None of the hypotheses related to video game violence effects on vulnerable youth were supported. Although this is only one piece of evidence, this early result does not support the belief that certain at-risk populations of youth, at least related to clinically elevated depression and attention deficit symptoms and trait aggression, demon- strate negative associations between violent video games and aggression related outcomes. It may be that the Table 3 Delinquency regression: beta weights and significance of entered variables for adolescents with clinical elevated attention deficit symptoms Variable b 95 \% Confidence interval t test Significance Gender .06 0.71 .48 Parental involvement .06 0.70 .49 Stress .32 (.18, .44) 4.21 .001* Family/peer support -.15 -1.69 .10 Trait aggression .38 (.25, .50) 4.23 .001* VGV .04 0.45 .65 VGV 9 trait aggression .03 0.39 .70 VGV = exposure to video game violence Table 4 Bullying regression: beta weights and significance of entered variables for adolescents with clinical elevated attention deficit symptoms Variable b 95 \% confidence interval t test Significance Gender -.06 -0.61 .54 Parental involvement .06 0.65 .52 Stress .12 1.38 .17 Family/peer support .01 0.02 .99 Trait aggression .41 (.28, .52) 4.17 .001* VGV .06 0.60 .55 VGV 9 trait aggression -.22 (-.08, -.35) -2.27 .03* VGV exposure to …