Research Assignment ONE Directions
PSY 101 Introductory Psychology
Due by Friday 18 September 2020
This exercise is the first research assignment (draft worth 80 points, two full pages of text minimum) designed to get you familiar with doing research using the psychology journal database. For these writing assignments you will need to use the PsychInfo or PsychArticles database that you access through the ISU library website. (Note that a google search or a google scholar search is not sufficient). The Psychology databases (accessed through the ISU library site) contain hundreds of thousands of articles published in journals that are refereed and reviewed by established scientists. You MUST use these databases to search for articles related to your interest area. You MUST learn to use the psychology databases through the library access for these assignments. I have provided 8 research questions and a reference to a primary research article that will help you answer the question. You should choose a question from the list and then using the PSYCHINFO database through the Library link find the referenced article. Many research questions in psychology are complicated and may depend on many factors. Some research may indicate one answer and other research may refute it. For this research question you need to find the article listed AND you will need to find another primary source that will help you answer this question. You may use other secondary sources as well but you need to use at least 2 primary sources for this paper. (Note a primary source is a detailed account of the experiment or study that is written by the people that did the experiment.) For this first research paper you need 2 primary sources, and you should attempt to answer the research question in your paper. The paper should NOT be simply a review of the two sources that you have found. You should focus your paper on answering the question and be sure to include the DATA (Results) of the studies you have read about to support your statements. For example – If you are attempting to answer a question about memory and diet you should not just say that people that eat processed foods have poorer memories than those that eat healthier. You need to provide support (results and data) from the research you found. Something like this: Subjects in the processed food group scored 68% on the memory test and subjects in the healthy eating group scored 92% on the memory test. Now your reader (me) has some better information on how the two groups differed on the memory task. This draft should be at least 2 pages in length and should include a cover page and a reference page. So it will be at least 4 pages total when submitted to the turn it in link. Please see the sample paper for formatting questions. Here are the steps:
1) Find the articles using the library electronic database
2) Save the pdfs to your flash drive or ISU account hard drive space
3) Read and understand the relevant portions of the article. Identify the evidence (data and results) you are going to use to answer your question.
4) How do these articles help you answer the question? What data is important to YOUR research question? It does not matter what the authors concluded. You should make conclusions and support your conclusions with results from the studies.
5) Create a two page word document that clearly states the research question and then attempt to answer the question using data from the articles to support your conclusions. Include citations to the articles where appropriate and an APA style reference section that lists the sources you used.
6) We are using APA style for this research paper so you will need to make sure you have a cover page at the start and a reference page at the end. Be sure to create a header with a running head on the left and page number on the right. Use the Sample paper as a template to help you get the page numbers, running head, margins and other formatting correct.
Find the research articles by going to our class blackboard site and clicking the button labeled “ISU Library”.
Then select the “Articles & Databases” tab above the search box. Then click on the “Databases by Subject” selection that appears just under the search bar. That will open a new window and allow you to select “Psychology” from the drop down menu. Then click the Search button.
It will find 3 recommended databases
Click on PsycARTICLES. Now you will see the EBSCOhost window. There are three search boxes at the top. This database searches hundreds of thousands of journals and limits the findings to journals that have immediate access to the pdf file of the article. (For a more extensive search you should use PsychINFO).
Start your search for research articles by typing in some keywords from the title of the article or topic you have selected from the list of 9 acceptable research questions.
Then click the SEARCH button.
You can type in different terms in each of the 3 boxes.
The Ebsco site will probably find several hundred sources that are related to your Question Assignment. You can refine your search by typing in the first author’s last name and then indicate over on the right side drop down menu that this is an author name.
This Ebsco search is powerful. You should be able to find 2 good primary sources that will help you answer your research question.
IMPORTANT: Not all the sources you find in the database or published in journals are primary sources. This assignment emphasizes being a critical thinker and evaluating the evidence you have. Therefore you must find sources (articles) that present evidence. For example; above I presented a question about memory and diet. It is not good enough to find an article in which the researcher concludes that consumption of processed foods leads to poorer physical and cognitive health. You MUST have evidence that this is true. You MUST find an article that has evidence. In this case you might find an article that compared problem solving abilities (like maze solving) between two groups of people. One group reported eating a healthy diet (HD) (at least 4 salads a week) and the other group reported having an unhealthy diet (UD) (mostly fast food and very few fresh vegetables). The researchers might report the average maze solving times for each group. They might find that the HD group solved the mazes in an average time of 6.7 minutes and the UD group solved the mazes in an average time of 13.1 minutes. That is the EVIDENCE! Now you can conclude that the HD group were better problem solvers than the UD group. You have evidence to show this is true.
Choose a research question from the list below. Nine are provided. If you have a different question that you would like to use you must first find a primary source that will help you answer that question and then get the instructor’s permission to use a different research question by showing them your question and your source. If you want to do this then bring your article and your research question to your instructor’s office during office hours to discuss the potential of using your own question.
Suggested Research Questions
1. Does sleep deprivation affect memory or other cognitive performance?
Grundgeiger, T., Bayen, U.J., & Horn, S.S. (2014). Effects of sleep deprivation on prospective memory. Memory, 22(6), 679-686.
2. How accurate are the memories that are recovered under hypnosis?
Nash, M.R., Drake, S.D., Wiley, S, Khalsa, S. & Lynn, S.J. (1986). Accuracy of recall by hypnotically age-regressed subjects. Journal of Abnormal Psychology, 95(3), 298-300.
3. Can meditation help stress management?
Yang, E., Schamber, E., Meyer, R.M.L. & Gold, J.I. (2018). Happier healers: Randomized controlled trial of mobile mindfulness for stress management. Journal of Alternative and Complementary Medicine, 24(5), 505-513.
4. Does playing violent video games make a person more aggressive?
Hollingdale, J., & Greitemeyer, T. (2013). The changing face of aggression: The effect of
personalized avatars in a violent video game on levels of aggressive behavior. Journal of Applied Social Psychology, 43(9), 1862-1868.
5. Are there gender differences in the degree to which individuals are likely to conform to expected behaviors? (i.e. Are men or women more likely to wear masks in public?)
Maslach, C., Santee, R.T., & Wade C. (1987). Individuation, gender role, and dissent: Personality mediators of situational forces. Journal of personality and Social Psychology, 53 (6), 1088-1093.
6. Are college athletes at higher risk of developing eating disorders?
Petrie, T. A;, Greenleaf, C. Reel, J. & Carter, J. (2008) Prevalence of eating disorders and disordered eating behaviors among male collegiate athletes, Psychology of Men & Masculinity, 9(4),. 267-277.
7. Does sleep effect children’s grades and behaviors?
Sadeh, A., Raviv, A., & Gruber, R. (2000). Sleep patterns and sleep disruption in school-age children. Developmental Psychology, 36(3), 291-301.
8. Does Gratitude and Thankfulness really make a difference?
Park, Y, Impett, E., MacDonald, G., & Lemay, E., (2019). Saying ‘thank you’: Partners’ expressions of gratitude protect relationship satisfaction and commitment from the harmful effects of attachment insecurity.. Journal of Personality and Social Psychology, 117(4), 773-806.
9. How does getting enough sleep improve daily life?
Hobson, J.A., Pace-Schott, E.F., & Stickgold, R. (2000). Dreaming and the brain: Toward a cognitive neuroscience of conscious states. Behavioral and Brain Sciences, 23, 793-1121.
Psychology
Topic: Violent video games and how there is no proven link between them and increased aggressive behaviors
Paper details:
4. Does playing violent video games make a person more aggressive?
This is the prompt chosen and the paper will be about violent video games having no direct, proven link to violent behavior. I’ve included the sources that were required to be used. It is important that these are the only sources utilized for this paper.
Title:
Violent video games and real-world violence: Rhetoric versus data. By: Markey, Patrick M., Markey, Charlotte N., French, Juliana E., Psychology of Popular Media Culture, 21604134, 20151001, Vol. 4, Issue 4
Database:
APA PsycArticles
Violent Video Games and Real-World Violence: Rhetoric Versus Data
Contents
1. Analysis One: Annual Changes in Video Game Sales and Violent Crime: 1978 to 2011
2. Method
3. Analytic Strategy and Results
4. Analysis Two: Monthly Changes in Video Games and Violent Crime: 2007 to 2011
5. Method
6. Analytic Strategy and Results
7. Analysis Three: Keyword Searchers for Violent Video Games and Violent Crime: 2004 to 2011
8. Method
9. Analytic Strategy and Results
10. Analysis Four: Violent Crime Following the Release of Three Popular Violent Video Games
11. Method
12. Analytic Strategy and Results
13. Discussion
14. Footnotes
15. References
Full Text
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By: Patrick M. Markey
Department of Psychology, Villanova University;
Charlotte N. Markey
Department of Psychology, Rutgers University
Juliana E. French
Department of Psychology, Villanova University
Acknowledgement:
Controlling the use of violent video games is one step we can take to help protect our society from violence.—Brad Bushman (2013)
. . . high exposure to media violence is a major contributing cause of the high rate of violence in modern U.S. society.—Craig Anderson (2000), testimony before the U.S. Senate Commerce Committee on the impact of interactive violence on children.
There is considerable evidence relating violent video games to aggressive behaviors and cognitions. In a comprehensive meta-analysis, Anderson et al. (2010) identified 74 studies that used the “best practices” (i.e., those studies that used valid measurements and sound methodologies), and concluded that exposure to violent video games is a causal risk factor for increased aggressive cognitions, aggressive affect, and aggressive behaviors. Although some researchers have cautioned that the apparent negative effects of violent video games are small and may be a result of publication bias (Ferguson, 2007), the popular media, lawmakers, and researchers have often linked violent video games to severe acts of violence. The current article examines whether the findings from these studies, which have been conducted primarily in laboratories with college students, generalize to severe forms of violent behavior occurring in the “real world.”
In the aftermath of the 1999 Columbine High School shootings, many media outlets discussed violent video games as one of the potential causes of this tragic event (cf., Simpson & Blevins, 1999). Following the shootings at Virginia Tech, local and national media noted that the gunman, Seung-Hui Cho, was a fan of the violent video shooter game Counterstrike (cf. Benedetti, 2007). In a similar manner, media sources reported that the Sandy Hook Elementary School gunman, Adam Lanza, played the video game Call of Duty, a game that mimics wartime violence (cf., Smeltz, 2012). A search of an online database of newspapers (ProQuest NewsStand) found that nearly 5,000 articles were released in the aftermath of these three tragedies, which discussed video games in the context of these three school shootings. The implication in many of these articles was that these violent acts were precipitated and perhaps even caused by exposure to violent video games. For example, on the popular ABC news program 20/20, one TV commentator noted, “In every school shooting, we find that kids who pull the trigger are video gamers” (Thompson, 2000).
The growing concern about a link between violent video games and severe forms of violent behavior prompted President Barack Obama in 2013 to encourage scientists to research the effects of violent video games (Molina, 2013). In the 30 years preceding this request, federal and local lawmakers conducted numerous hearings, proposed various legislative acts, and passed approximately a dozen laws in an effort to regulate the sale of violent video games. One of the most salient political events concerning video games was the 1993 hearing on video game violence led by Senator Joseph Lieberman, resulting in the creation of the Entertainment Software Ratings Board (ESRB; Kent, 2010). The ESRB is a self-regulatory organization whose purpose is to assign age and content ratings to video games. In 2011, the United States Supreme Court struck down a California law prohibiting the sale of violent video games to youth, thereby affording video games the same first-amendment protection as films, music, and other artistic works (Helle, 2011). This decision by the Supreme Court has not curtailed the concern of lawmakers. Following the tragedy at Sandy Hook Elementary School, several local and federal bills were introduced, including the federal bill “Video Games Enforcement Act” (H.R. 287), which would regulate the sale of violent video games. Various legislators continue to be concerned that violent video games are a main contributor to youth violence, with Senator Lamar Alexander arguing that “ . . . video games is (sic) a bigger problem than guns, because video games affect people” (Linkins, 2013).
In addition to lawmakers and the media, violent video game researchers have linked this medium to serious and deadly assaults. This connection has been explicit at times, such as when researchers described violent video games as “murder simulators” (Grossman, 1998), or when arguing that video games can train children to kill in a manner similar to how a flight simulator teaches a person to pilot a plane (Bushman, 2008; Gentile & Anderson, 2003). Some researchers have even contended that the negative effect of violent video games on public health is similar to the causal relationship between smoking and lung cancer (cf., Bushman & Anderson, 2001). Other times this link has been more subtle, such as when researchers reference real-world violence to substantiate the rationale of their research. For example, in the peer-reviewed “best practices” studies identified by Anderson et al. (2010), 28% of the studies discussed severe forms of violence, most often within the introduction or abstract of the article. Of these studies, 42% presented their research in the context of the Columbine High School shooting, with the remaining discussing other school shootings (e.g., the Heath High School shooting, Westside Middle School shooting, etc.), homicide rates, and terrorism, including the September 11th attacks on the World Trade Center and the Pentagon. Outside of journal pages, researchers have also linked laboratory findings with severe forms of violence during interviews with the popular media. For example, in searching for possible motivations of the 2013 Washington Navy Yard shootings, one prominent researcher noted that “ . . . video game use may have been a contributing factor,” and argued the shooter was probably a more accurate shot because he liked to play the video game Call of Duty (Bushman, 2013).
The concern about violent video games expressed by the media, lawmakers, and researchers may be justifiable, given the prevalence of this medium. Media researchers define a video game as violent if a character in the game displays realistic or cartoonish aggressive behavior toward another character. This classification is not focused on how graphic the violence is in a game; rather, it centers on whether the behavior the game character exhibits is intentionally harmful to another game character (cf., Gentile, Saleem, & Anderson, 2007; Thompson & Haninger, 2001). This definition is consistent with claims that cartoonish violence in E-rated games has the same negative effects on violent behavior as realistic violence portrayed in M-rated games (Gentile et al., 2007). Consequently, regardless of a game’s ESRB rating, games that contain cartoonish and realistic violence are both considered to be violent. For example, the violent video games used in the studies identified as using the “best practices” (Anderson et al., 2010) included a cartoonish platformer (Ty2; Rated E – content is suitable for everyone), a violent first person shooter (Call of Duty; Rated M – content is suitable for 17 years and up), an exaggerated version of baseball (MLB Slugfest; Rated E – content is suitable for everyone), and an adventure game where the main character uses cartoonish attacks such as “pepper breath” (Herc’s Adventure: Rated E – content is suitable for everyone). According to this definition, the majority of video games contain violence (Gentile, 2009; Thompson & Haninger, 2001; Thompson, Tepichin, & Haninger, 2006). In fact, among the most popular games sold in the past 5 years, more than 90% portray some form of violent behavior.
Realistic and cartoonish violent video games have been linked to aggressive behavior and cognitions in numerous correlational, experimental, and longitudinal studies (for reviews see Anderson & Bushman, 2001; Anderson et al., 2010; Ferguson, 2007; Sherry, 2001). The typical correlational study in this area uses mainly questionnaires, asking participants to first describe their video game playing habits and then self-report feelings or behaviors related to aggression and violence. For example, Anderson and Dill (2000) found that preference for violent video games was related to self-reported aggressive delinquency. The majority of experimental studies involve having one group of participants play a violent video game (e.g., Grand Theft Auto, Call of Duty, etc.) and another group play a nonviolent video game (e.g., Tetris, Top Spin Tennis, etc.) for a very short period (e.g., 15 minutes). Immediately after playing the assigned video game, the aggressive cognitions or behaviors of the participants are measured. Researchers using this methodology have found that individuals who play violent video games are more likely to expose others to “noise blasts” (a loud sound that punishes others with an unpleasant noise; Anderson & Dill, 2000), report feeling more hostile on a questionnaire (Markey & Scherer, 2009), and even give hot sauce to hypothetical individuals who do not like spicy food (Barlett, Branch, Rodenheffer, & Harris, 2009).
One limitation of previous research examining violent video games and aggressive behavior is the manner in which “aggressive behavior” is operationalized. The majority of research studies in this area assess minor forms of aggression (e.g., giving an unpleasant noise or too much hot sauce to another person) or self-reports of aggressive feelings or behaviors. As pointed out by others (Anderson, Bushman, & Groom, 1997), laboratory studies are somewhat limited because how aggressive behavior is measured in these contexts is different from severe forms of aggression in the “real world,” which can involve aggravated assaults and homicides. In other words, is a person’s indication that they would give another person hot sauce analogous to shooting another person?
Given the ethical problems associated with studying real violence in a laboratory setting, researchers who study such severe forms of violence have often examined changes in violent crime rates in relation to changes in a variable of interest across time. Consistent with this notion, Anderson and colleagues have stressed the importance of examining changes in criminal data to determine whether these data point to the same conclusion as results from other methodologies (e.g., laboratory studies; Anderson, 1987; Anderson et al., 1997). Because each type of methodology affords different strengths and weaknesses, the confidence in the validity of a finding is strengthened if diverse methods support the same hypothesis. Conversely, if they do not support the same hypothesis, then the validity of the hypothesis is called into question.
To illustrate this notion, Anderson et al. (1997) presented a series of studies that examined the “heat hypothesis”—the idea that uncomfortably hot temperatures increase aggressive behaviors. These researchers found that between the years 1950 to 1995, the average annual temperatures were positively related to aggravated assaults and homicides, even after controlling for trends and serial dependency in the time series. What is perhaps most impressive about this study is, although violent behavior has a multitude of causes, the effect of heat was strong enough to express itself in real forms of violent behavior. By linking temperature to the changes in violent behavior across time, the authors of this study concluded that the “heat effect is real and significant when applied to large populations.” (Anderson et al., 1997, p. 1222).
To determine whether violent video games have a “real and significant” effect when applied to large populations, the current study also examined changes in aggravated assaults and homicides across time. Based on previous research and the speculation from the popular media, lawmakers, and researchers, it was predicted that years or months where many individuals were exposed to violent video games would yield relatively high rates of serious and deadly assaults. To examine this prediction, four time-series analyses were conducted to investigate the relations between various assessments of video game habits and violent crime within the United States. Analysis One examined annual changes in video game sales and changes in violent crime between 1978 and 2011. Analysis Two investigated monthly video games and monthly reports of aggravated assaults and homicides between 2007 and 2011. Analysis Three examined how changes in Internet searches for video game walkthroughs and guides of popular violent video games were related to monthly changes in serious and deadly assaults. Analysis Four investigated the change in aggravated assault and homicide rates following the release of three extremely popular violent video games. In each analysis, both concurrent and delayed effects were examined. It is hoped that this comprehensive and diverse set of analyses will be able to detect any possible links between violent video games and real-world violent behavior.
Analysis One: Annual Changes in Video Game Sales and Violent Crime: 1978 to 2011
Although the video game industry began in 1971, with the first commercially sold coin-operated video game Computer Space, it did not gain widespread attention until the release of the Atari 2,600 game console in 1977 (Goldberg & Vendel, 2012). Over the past 40 years, the video game industry has grown to include hundreds of companies, with worldwide sales expected to grow to $82 billion by 2017 (Gaudiosi, 2012). It is estimated that four of five homes in the United States with a male child have a video game system, with children playing video games an average of 9 hr a week (13 hr for boys and 5 hr for girls; Gentile, Lynch, Linder, & Walsh, 2004; Sherry, 2001). However, during this same period, violent crime has decreased. In 1978, the homicide rate in the United States was 9.0 homicides per 100,000 people, but this rate dropped dramatically to 4.7 homicides per 100,000 people in 2011 (United States Department of Justice, 2013). Given the prevalence of violence in most popular video games (Gentile, 2009; Thompson & Haninger, 2001; Thompson et al., 2006), the first analysis examined the link between overall video game sales between 1978 and 2011 and changes in aggravated assaults and homicides.
Method
Data and sources
Annual video game sales
Annual game sale data between 1978 and 2011 were provided by researchers at SuperData. SuperData is an independent marketing company that collects data from publishers and developers to examine various trends within the video game market space (Van Dreunen, 2011). A second set of annual video game sales data were obtained from the NPD Group for the years 1997 to 2011 (data were not available from this group prior to 1997). The NPD Group is a market research company that collects actual sales data from retailers and distributers in addition to tracking consumer-reporter purchasing behavior (NPD Group, 2013). Based on these data, the NPD Group releases monthly and annual reports to subscribers concerning video game sales. Both of these data sources provided almost identical sales information from overlapping years (r = .92) and sales data were averaged for overlapping years. Annual sales figures were then adjusted for inflation, and annual population counts from the United States were used to derive the amount of money spent on video game merchandise per 100,000 individuals (Figure 1).
Figure 1. Annual changes in video game sales and violent crime between 1978 and 2011.
Violent crime rates
The Federal Bureau of Investigation’s annual Uniform Crime Reports (UCRs) were used to compute annual aggravated assault and homicide rates (United States Department of Justice, 2013). The UCRs contain crime-related statistics from most law enforcement agencies located in the United States, and consist of more than 17,000 city, county, state, and federal law enforcement agencies who voluntarily submit data concerning various crimes brought to their attention. Aggravated assaults are defined as an unlawful attack on a person with the intent of inflicting severe or aggravated bodily injury and often involve the use of a weapon. Homicide counts include the willful killing of another human being and exclude deaths caused by negligence or accidents. For each violent crime, annual crime rates per 100,000 were computed between 1978 and 2011 (Figure 1).
Analytic Strategy and Results
All analyses were conducted using SPSS 21. Simple correlations revealed that annual video game sales were negatively related to aggravated assault (r(32) = −.40, p < .05) and homicide (r(32) = −.84, p < .01) rates. However, these results need to be interpreted with extreme caution, as time series often contain a considerable amount of autocorrelation, indicating that an observation for a given period is correlated with past periods (Sadler, Ethier, Gunn, Duong, & Woody, 2009; Warner, 1998). Autocorrelations and trends were removed from each time series using the Box–Jenkins approach to fit time-series data to an autoregressive integrated moving average (ARIMA) statistical model (Box, Jenkins, & Reinsel, 2008). ARIMA models are a popular technique for dealing with time-series data and have been used in numerous research studies across various disciplines. For each time series, an ARIMA model is created by identifying autocorrelations and trends in the data. This model is then used to estimate the parameters for a given time series. By applying resulting ARIMA models to each time series, a set of residuals for each series can be generated, which are free of trends, cycles, and autocorrelations (a process called prewhitening; West & Hepworth, 1991).
The removal of trends is especially important; otherwise, a spurious relationship may be found between two time series simply because they share similar (or opposite) trends. For example, video game sales have tended to become more popular across these 36 years, whereas crime (especially homicide) has tended to decrease during this period (Figure 1). Stronger evidence of the link between video game sales and violent crime would be provided if deviations from these trends were related to each other. To examine this possibility, the residuals from each of the time series were related to each other using the cross-correlation function (CCF). The CCF allows comparisons at the same time point in both series (concurrent effect) and up to a specific number of lagged periods (see Warner, 1998 and West & Hepworth, 1991, for additional information).
Based on autocorrelation and partial correlation functions, it was found that both annual reports of video game sales and aggravated assault were fit by an ARIMA (2,1,0) model, and an ARIMA (1,1,0) model was adequate to fit annual homicide rates (for additional information about ARIMA models, see Box et al., 2008). Ljung–Box Q tests for white-noise residuals revealed that when this model was applied to video game sales (Ljung–Box Q at lag 10 = 5.43, P = .86), aggravated assault rates (Ljung–Box Q at lag 10 = 7.86, P = .64), and homicide (Ljung–Box Q at lag 10 = 7.59, P = .70) rates, there were nonsignificant autocorrelations among the residuals. The ARIMA residuals for video game sales were then cross-correlated with the residuals for the crime assessments both concurrently and up to a 4-year lag. As seen in Figure 2, violent annual video game sales were unrelated to concurrent rates of aggravated assaults and homicides and remained unrelated up to 4 years later.
Figure 2. Cross-correlations between annual video game sales and violent crime. Note. The horizontal lines represent the 95% confidence interval under the null hypothesis.
Analysis Two: Monthly Changes in Video Games and Violent Crime: 2007 to 2011
Results from the previous analysis revealed no link between changes in annual video game sales and changes in serious and deadly assaults across 33 years. However, it is possible that rather than affecting violent behavior years later, the negative effects of violent video games are only expressed months later. Such a possibility is consistent with one large-scale meta-analysis that found that, after controlling for gender, violent video games have a small but significant concurrent effect on aggressive behavior assessed in the laboratory (average r = .14), but this effect becomes much smaller when examined longitudinally (average r = .07; Anderson et al., 2010). To investigate the possibility that the negative effects of violent video games express themselves quickly, the second analysis examined monthly video game sales and monthly reports of aggravated assault and homicide between 2007 and 2011.
Method
Data and sources
Monthly video game sales
Monthly video game sales data were provided by the NPD Group. For the current analyses, the NPD Group provided monthly sales data between January 2007 and December 2011. Monthly sales were adjusted for inflation, and yearly population counts were then used to derive the monthly amount of money spent on video game merchandise per 100,000 individuals (Figure 3).
Figure 3. Monthly changes in video game sales and violent crime between 2007 and 2011.
Monthly crime rates
The UCRs were used to compute monthly aggravated assault and homicide rates between January 2007 and December 2011 (Figure 3). Because the current analysis focused on monthly reports of violent crime only, law enforcement agencies that consistently provided monthly crime statistics for a given year were included in the analyses.
Analytic Strategy and Results
Simple correlations revealed that monthly video game sales were negatively related to aggravated assault rates (r(58) = −.45, p < .01) and were unrelated to homicide rates (r(58) = −.15, p = .25). However, as before, owing to the trends and dependency contained within these time series, these findings need to be interpreted with caution. Because monthly reports of video game sales and violent crime follow a seasonal pattern (Figure 3), the seasonal ARIMA (SARIMA) extension was used (Box et al., 2008). SARIMA models are able to deal with a time series that possesses a seasonal component that repeats every s observations (e.g., every 12 months). The removal of seasonal trends is especially important if two time series share similar (or opposite) cycles. For example, video game sales peak during the winter (around December), whereas violent crime increase during the warm months (as predicted by the heat hypothesis; Anderson et al., 1997). Similar to the previous analysis, each time series was prewhitened to remove trends, cycles, and autocorrelations. The residuals from these time series were then related to each other using the CCF both concurrently and up to 4 months later.
Using the autocorrelation and partial correlation functions, game sales could be fit with the seasonal model SARIMA (0,1,0)(0,1,1)12. Aggravated assault required two additional autoregressive terms (SARIMA [2,1,0][0,1,1]12), and homicide required four autoregressive terms (SARIMA [4,1,0][0,1,1]12) to achieve adequate fit and remove trends and cycles in the data (for additional information about SARIMA models, see Box et al., 2008). Ljung–Box Q tests for white-noise residuals revealed that when this seasonal model was applied to video game sales (Ljung–Box Q at lag 12 = 4.19, p = .98), aggravated assault rates (Ljung–Box Q at lag 12 = 6.95, p = .86), and homicide rates (Ljung–Box Q at lag 12 = 9.52, p = .66), there were nonsignificant autocorrelations among the residuals. The SARIMA residuals for video game sales were then cross-correlated with the residuals for the crime assessments both concurrently and up to a 4-mo lag. As seen in Figure 4, a negative relationship was found between video game sales and concurrent rates of aggravated assault (r = −.39). There were no significant lagged correlations, indicating that monthly video game sales were unrelated to monthly rates of assaults and homicide up to 4 months later.
Figure 4. Cross-correlations between monthly video game sales and violent crime. Note. The horizontal lines represent the 95% confidence interval under the null hypothesis.
Analysis Three: Keyword Searchers for Violent Video Games and Violent Crime: 2004 to 2011
Although the majority of video games contain some form of violence (Gentile, 2009; Thompson & Haninger, 2001; Thompson et al., 2006), Analysis Three examined the possibility that only extremely violent and realistic video games affect serious forms of violent behavior. The current analysis focused solely on popular M-rated video games, which contain graphic and realistic forms of violence. Additionally, instead of examining the sales of video games, Analysis Three used a different assessment of game play.
One way to assess when individuals are playing a specific game is to examine behaviors that are related to this activity. When playing video games, players often use “walkthroughs” or strategy guides to augment their play experience. Before the popularity of the Internet, retail sales of the strategy guides for specific games frequently sold more than 1 million copies (Snider, 2004). However, since the Internet has become popular and widely accessible, a simple search using Google allows players to quickly find these guides online. Such walkthroughs and game guides are available on various Web sites, one of the most popular being the CBS Interactive-owned “GameFaqs.” (Alexa, 2013). To estimate when a large group of individuals are playing violent video games, Analysis Three examined the Internet searches for walkthroughs and game guides for popular M-rated violent video games between 2004 and 2011.
The current analysis investigated Internet keyword searches via the popular search engine Google. Using this service, a person might type the words “walkthrough Grand Theft Auto” or “gamefaqs Grand Theft Auto” into Google’s search engine when attempting to find a walkthrough or game guide to Help him or her with playing this video game. For example, Figure 5 displays the frequency of searches for the term “walkthrough Grand Theft Auto” during the first year Grand Theft Auto IV was released. As would be expected, searches for this keyword phrase peaked in May following the release of the game on April 29, 2008. If playing the video game Grand Theft Auto contributed to violent crime, it seems likely a similar increase in crime would have also occurred around May. Past researchers have successfully used Internet keyword searches to examine interest in a wide variety of topics, including seasonal affect disorder (Yang, Huang, Peng, & Tsai, 2010), dieting (Markey & Markey, 2013), suicide (McCarthy, 2010), pornography searches (Markey & Markey, 2010a, 2011), and even to track H1N1 outbreaks (Ginsberg et al., 2009). In a similar manner, Analysis Three examined whether there was a link among keyword searches for violent video game walkthroughs and game guides and concurrent and future rates of aggravated assault and homicide.
Figure 5. Internet keyword searches for Grand Theft Auto guides following the release of the Grand Theft Auto IV on April 29, 2008.
Method
Data and sources
Keyword searches for violent video game walkthroughs and guides
Google Trends was used to determine how often individuals searched for walkthroughs and game guides of popular violent video games between January 2004 (the earliest time point data were available) and December 2011. Violent video game searches included the keywords “walkthrough” or “gamefaqs” along with the name of popular M-rated violent video games sold within this period (e.g., Call of Duty, Grand Theft Auto, Gears of War, Halo, etc.). For example, a user who searched for “walkthrough of Halo” would be included in this analysis, but a user who only searched for “Halo” or “walkthrough” would be excluded. Google Trends examines Google Web searches to determine how many searches for the given set of keywords had been conducted in a given week relative to the average number of searches on Google for those keywords over the entire observed period. Search data were standardized by dividing the search volume for each period by the greatest search volume and multiplying by 100. In this range, a value of 100 would indicate the period with the greatest overall searches for a set of keywords. A period with half of the keyword searches as the highest period would receive a value of 50, and so forth (Google, 2013). Weekly reports were then aggregated to estimate the volume of Internet searches for walkthroughs and game guides of violent video games that occurred each month between January 2004 and December 2011 (Figure 6).
Figure 6. Monthly changes in searches for violent video game guides and violent crime between 2003 and 2011.
Monthly crime rates
Violent crime statistics from the UCRs were used to compute monthly violent crime rates for aggravated assault and homicide. In the current analyses, monthly reports were collected between January 2003 and December 2011 (Figure 6).
Analytic Strategy and Results
Searches for violent video game walkthroughs and guides were negatively related to aggravated assault (r(94) = −.31, p < .01) and were unrelated to homicide (r(94) = −.12, p = .27). SARIMA models were used to prewhiten each time series. Using autocorrelation and partial correlation functions, it was found that keyword searches for violent video game guides and tips fit a SARIMA (1,0,0)(0,1,1)12 model, and both aggravated assault and homicide time series were fit by the same seasonal models used in the previous analysis. Ljung–Box Q tests for white-noise residuals further revealed that when these models were applied to keyword searches (Ljung–Box Q at lag 12 = 9.49, p = .66), aggravated assault (Ljung–Box Q at lag 12 = 10.00, p = .61), and homicide (Ljung–Box Q at lag 12 = 8.15, p = .83), it produced nonsignificant autocorrelations among the residuals. The residuals of these time series were then related to each other using CCF concurrently and up to 4 months later. As seen in Figure 7, keyword searches for violent video game walkthroughs and guides were negatively related to both aggravated assaults (r = −.22) and homicides (r = −.22) 2 months later. None of the other lags produced significant relations between keyword searches for violent video walkthroughs and guides, aggravated assaults, or homicides.
Figure 7. Cross-correlations between searches for violent video game guides and violent crime. Note. The horizontal lines represent the 95% confidence interval under the null hypothesis.
Analysis Four: Violent Crime Following the Release of Three Popular Violent Video Games
Video game releases are similar to movie releases in that the majority of the public consumes the product when it is first released. The violent first person shooter, Call of Duty: Black Ops, earned $360 million the first day it was released, $650 million within the next 4 days, and more than $1 billion in sales by 41 days (Associated Press, 2010). These impressive sales are not limited to this single game. For example, between 2003 and 2011, three of the most popular M-rated violent video games (Grand Theft Auto: San Andreas, Grand Theft Auto IV, and Call of Duty Black Ops) combined earned more than $3.5 billion in sales. Given the violent content of these games and their popularity, the media, lawmakers, and researchers have linked Call of Duty and Grand Theft Auto to serious acts of violence in the real world. Some have implied that Call of Duty was a causal factor in numerous mass shootings, including the 2011 Norway attacks, the Sandy Hook Elementary School shooting in 2012, the Toulouse and Montauban shootings in 2012, and the Washington Navy Yard shootings in 2013 (Bushman, 2013; Smeltz, 2012). Grand Theft Auto has been associated with both general trends in violence and specific violent crimes, including the arrests of William and Josh Buckner in 2003 for homicide; Devin Moore in 2003 for first-degree murder; Cody Posey in 2004 for homicide; Ryan Chinnery in 2008 for rape and grievous bodily harm; Stephen Attard, Samuel Philip, Dylan Laired, and Jaspreet Singh in 2008 for various robberies and assaults; and Zachary Burgess, only 4 days after the release of Grand Theft Auto IV, in 2013 for vehicle theft and kidnapping (cf., Crowely, 2008; Newcomb, 2013).
If violent video games are causes of serious violent crimes, it seems probable that serious and deadly assaults would increase following the release of these three popular violent video games. To examine this hypothesis, the final analysis used an interrupted time-series analysis. Such a methodology has been used in the past to examine numerous health and social issues and is among the strongest, quasi-experiment design available to evaluate longitudinal effects of “real world” outcomes (Wagner, Soumerai, Zhang, & Ross-Degnan, 2002). It is predicted that following the release of these extremely popular violent video games, there will be an increase in aggravated assaults and homicides. Because the duration of this effect is unknown, increases in violent crime will be examined for continuous periods of 1 to 12 months after the release of these violent video games.
Method
Data and sources
Release dates of popular violent video games
The North American release dates of Grand Theft Auto: San Andreas, Grand Theft Auto IV, and Call of Duty: Black Ops were obtained from each game’s publisher (Figure 6). These three video games were selected because they were among the top-selling M-rated video games during the period examined and owing to their frequently discussed links with violent criminal behavior (cf., Bushman, 2013; Crowely, 2008; Newcomb, 2013; Smeltz, 2012).
Monthly crime rates
UCR crime statistics from the previous analysis were used to compute monthly violent crime rates for aggravated assault and homicide between January 2003 and December 2011 (Figure 6).
Analytic Strategy and Results
Interrupted time-series analyses were computed to compare violent crime before and after the release of three popular violent video games. In the current analysis, the violent crime rates following the release of Grand Theft Auto: San Andreas, Grand Theft Auto IV, and Call of Duty: Black Ops were examined to determine whether these games were related to changes in aggravated assaults and homicides. Specifically, monthly changes in violent crime were examined for continuous periods of 1 to 12 months after the release of these violent video games. This methodology provides insight into whether the release of these violent video games predicted violent crime over and above the prediction derived from understanding the trends and cycles of violent crime, 1 to 12 months after these games were released.
The same SARIMA models used in Analysis Three were again used to eliminate trends, cycles, and autocorrelated errors in each time series. Binary dummy variables were then used to model pulse effects on violent crime for continuous periods of 1 to 12 months after the release of these violent video games. Ljung–Box Q statistics at a lag of 12 revealed nonsignificant autocorrelations among the residuals for each of the 24 analyses (12 for aggravated assault and 12 for homicide). The resulting t tests for the pulse effects of each period were transformed to r-values to provide assessments of effect sizes. As can be seen in Figure 8, aggravated assault rates tended to show a decrease following the release of these three violent video games, but this change failed to reach significance. Homicides also decreased after these violent video games were released and displayed significant decreases 3 and 4 months following the release of these games.
Figure 8. Changes in aggravated assault and homicide rates following the release of three popular violent video games. Note. The horizontal lines represent the 95% confidence interval under the null hypothesis.
Discussion
Laboratory and correlational studies suggest violent video games are a causal risk factor for increased aggressive cognition, aggressive affect, and aggressive behavior (Anderson & Bushman, 2001; Anderson et al., 2010; Ferguson, 2007; Sherry, 2001). Based on the results from these studies, the media, lawmakers, and researchers have linked violent video games to serious forms of violent behavior, including aggravated assaults and homicides. The current study sought to examine whether such studies, which tend to examine mundane forms of aggression (e.g., giving an unpleasant noise or too much hot sauce to another person), generalize to serious and deadly assaults reported in the real world. Crime data provided by the FBI for the past 30 years along with sales of video games, Internet keyword searches for violent video game guides, and release dates of popular violent video games were examined annually and monthly using large-scale time-series data analytic techniques (e.g., ARIMA, SARIMA, interrupted time designs, CCFs, etc.). Concurrent effects of violent video games and lagged effects lasting months and years were considered.
Contrary to the claims that violent video games are linked to aggressive assaults and homicides, no evidence was found to suggest that this medium was a major (or minor) contributing cause of violence in the United States. Annual trends in video game sales for the past 33 years were unrelated to violent crime both concurrently and up to 4 years later. Unexpectedly, monthly sales of video games were related to concurrent decreases in aggravated assaults and were unrelated to homicides. Searches for violent video game walkthroughs and guides were also related to decreases in aggravated assaults and homicides 2 months later. Finally, homicides tended to decrease in the months following the release of popular M-rated violent video games.
The findings that violent crime was more likely to show decreases instead of increases in response to violent video games were contrary to what was expected. One possible explanation for this reduction in violence is that playing violent video games leads to a catharsis. In other words, when people play violent video games, they are able to release their aggression in the virtual world instead of in the real world. Consistent with this notion, adolescent boys tend to report feeling less angry after playing violent video games, and even actively select to use this medium to control their aggression (Olson, Kutner, & Warner, 2008). However, other researchers have found little evidence to suggest that venting one’s anger on a safe target actually reduces aggression (cf. Bushman, 2002).
A more parsimonious and less contentious explanation than the catharsis effect focuses on the qualities and desires of people who are innately predisposed to violent behavior (cf., Ferguson et al., 2008; Pinker, 2002). Individuals who are prone to aggression and violence tend to seek out violent media, like video games, to provide them with models that express behaviors and desires consistent with their own innate motivational system (Markey, in press; Surette, 2012). When violent games, like Grand Theft Auto or Call of Duty, are released, these aggressive individuals likely spend time playing these video games. Such a behavior effectively removes these individuals from the streets or other social venues where they might have otherwise committed a violent act. In other words, because violent individuals are playing violent video games in their homes, there may be a decrease in violent crime when popular violent video games are released.
The results from this study should be considered within the context of the methodological limitations of this study. Given both the number of analyses conducted and the unexpected direction of the results in the current study, researchers should examine whether these results generalize to future periods and other geographic regions. Additionally, although theories of video game violence operate at the level of the individual, data for the current study were collected at the aggregate level. Owing to this ecological fallacy, caution is warranted when attempting to draw causal relations between these variables, as trends sometimes become altered when subpopulations are aggregated (i.e., Simpson’s paradox; Wagner, 1982). However, even with this concern, prominent video game researchers have argued that theories framed at the individual level can translate into concrete empirical predications at the aggregate level (Anderson et al., 1997). Consistent with this notion, it was predicted that years or months when many individuals were exposed to violent video games would yield relatively high serious and deadly assault rates. Such an empirical prediction is falsifiable, constituting “a legitimate test of the theory despite its cross-level nature” (Anderson et al., 1997, p. 1221).
This research is also limited because it only examined a single risk factor for violent behavior—violent video games. Researchers have often adopted a risk factor approach when discussing the negative effects of violent video games. This approach acknowledges there are many risk factors for violent crime. Each factor may elevate the risk for violent behavior, and with enough risk factors present, it becomes likely a person will act violently (Anderson, Gentile, & Buckley, 2007). No scientist has suggested violent video games are the only cause of violent behavior, just as no scientist has suggested that heat is the only cause of violent crime or smoking is the only cause of lung cancer. Of course, risk factors like heat and smoking are strong enough that when it becomes hotter, there is a significant increase in violent crime (Anderson et al., 1997), and as more people have stopped smoking, there has been a dramatic decrease in lung cancer rates (Centers for Disease Control and Prevention, 2013). Such a pattern does not exist for violent video games. As more people have been exposed to violent video games, serious and deadly assaults have not increased. It appears that any adverse effects violent video games have on serious violent behavior are either nonexistent or they are dwarfed by the effects of other factors that make the effects of violent video games appear nonexistent.
The rhetoric used by some in generalizing the findings of research conducted primarily in laboratories and with questionnaires to serious and deadly assaults appears to be unfounded. The current study found no evidence that violent video games are contributing to the high rate of violence in the United States (Anderson, 2000) or that controlling the use of violent video games would protect our society from violent crime (Bushman, 2013). The effect of violent video games on public safety does not appear to be equivalent to the effect of smoking on lung cancer (Bushman & Anderson, 2001). Although video games might “affect people,” it is unlikely they are a bigger problem than guns (Linkins, 2013). If video games are really the equivalent of flight simulators training people to kill (Bushman, 2008; Gentile & Anderson, 2003; Grossman, 1998), it is difficult to explain why homicide rates would go down after millions of these “murder simulators” have been sold. When the media, politicians, or researchers link the murderous rampages of male adolescents with violent video games, they are conveying a classic illusory correlation (Ferguson, 2013). These individuals are ignoring that 90% of young males play video games (Lenhart, 2008). Finding that a young man who committed a violent crime also played a popular video game, such as Call of Duty, Halo, or Grand Theft Auto, is as pointless as pointing out that the criminal also wore socks. The rhetoric about violent video games does not match the data.
It is important to note that in no way does this conclusion imply previous research examining violent video games is unimportant. There is ample evidence that violent video games do increase aggressive cognition, aggressive affect, and some aggressive behaviors. It is possible that although violent video games are not related to severe forms of violence, they may affect other types of less aggressive behaviors, such as bullying, spreading gossip, minor fights at school, pushing and shoving, or hurling insults. This study also does not provide insight into whether certain subpopulations are adversely affected by violent video games. Although research has been mixed on this issue (Ferguson & Olson, 2014), it is possible that violent video games adversely affect only some individuals, and those who are affected have preexisting dispositions (e.g., high levels of psychoticism, anger, etc.), which make them susceptible to such violent media (Markey & Markey, 2010b; Markey & Scherer, 2009) Finally, the current research does not address how exposure to violent video games at a young age might affect later adult behavior. As scientists, we can reflect about such a relationship based on available research, but we need to be upfront that this is only supposition. We need to be clear with our peers and the general audience about the claims we make that are backed up by research and those that are speculation. In short, as scientists, we need to be careful that we do not blur the line between our scientific results and our scientific conjecture.
Footnotes
1 A governmental panel created after the Virginia Tech shootings found no evidence that Seung-Hui Cho ever played or owned the game Counterstrike or any other violent video game (Virginia Tech Review Panel, 2007).
2 A governmental report noted that although the shooter owned Call of Duty, he spent most of his time playing nonviolent games, including Dance-Dance Revolution and Super Mario Brothers (Sedensky, 2013).
3 The first author of the current research article is also the author of one of the “best practices” studies that discussed violent video games in the context of school shootings.
4 Represents the percent of game units sold between 2007 and 2011, as reported by the sale tracking site vgchartz.com, which has an ESRB content descriptor for any type violence (e.g., intense violence, fantasy violence, cartoonish violence, etc.).
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Submitted: November 26, 2013 Revised: January 31, 2014 Accepted: February 3, 2014
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Source: Psychology of Popular Media. Vol. 4. (4), Oct, 2015 pp. 277-295)
Accession Number: 2014-33466-001
Digital Object Identifier: 10.1037/ppm0000030
