Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -1- High School (Colorado) schoolmates and one teacher, wounded another 24 people, and then

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1 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -1- Chapter 5: Research Methods On April 20, 1999, Eric Harris and Dylan Klebold shot and killed 12 of their Columbine High School (Colorado) schoolmates and one teacher, wounded another 24 people, and then committed suicide. The news media, pundits, and politicians offered numerous explanations for this tragic event. Some blamed lax gun control laws. Others blamed uninvolved parents, violent video games, a school culture that glorifies athletes at the expense of non-athletes. Interestingly, there were few (if any) simple explanations offered by aggression and violence researchers. Why might this be? One reason has already been mentioned in prior chapters; we known that aggressive and violent tendencies are influenced by many factors. Therefore, people who really know the relevant research literatures rarely give the simplistic answers sought by news media for use in juicy sound bites. A second reason is that there had been little scientific research specifically directed at understanding school shootings. As we will see shortly, it is a topic that is very difficult to research in a way that allows clear scientific answers. In this chapter, we describe how aggression researchers create and test modern scientific models of aggression and violence, as well as how they summarize large numbers of empirical studies. We also give some specific examples of the types of measures used by aggression researchers. But first, we discuss the strengths and weaknesses of the three main empirical research designs used by any field of sciences. THREE EMPIRICAL DESIGNS Generally, one can divide original empirical research studies into one of three categories based on research design experimental, cross-sectional correlational, or longitudinal. Each has its own characteristic pattern of strengths and weaknesses, as shown in Figure 5.1.

2 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -2- Figure 5.1. Main strengths and weaknesses of key research designs. Experimental Studies Experimental studies are those in which the researcher randomly assigns participants to different treatment and control conditions. For example, participants might be randomly assigned to a condition in which they are provoked either in the presence of guns or the presence of badminton rackets, and then be given an opportunity to harm the provocateur by giving electric shocks (Berkowitz & LePage, 1967). Such a study is designed to test the hypothesis that the presence of weapons (i.e., the independent variable of whether guns or badminton rackets are in view) increases aggression (i.e., the dependent variable, amount of electric shock administered to another individual). Major Strength: Strong Causal Evidence The main strength of experimental studies is that they allow the strongest causal inferences. This is because random assignment dramatically reduces the possibility that the comparison groups (those in the guns condition compared to those in the badminton rackets condition) differed at the outset in ways that could yield statistically significant differences in the key outcome measure (amount of electric shock). In other words, random assignment allows the researcher to rule out a host of alternative explanations of the obtained differences between the

3 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -3- violent and nonviolent video game conditions. If the treatment and control conditions yield different results on the aggression measure, then it is very likely that the experimental manipulation (e.g., guns versus badminton rackets) caused the difference, and very unlikely that the naturally most aggressive individuals just happened to wind up in the guns condition. Major Weakness: Ethics Prevent Use of Extremely Harmful Dependent Variables The main weakness of experimental studies concerns the type of aggression that can be measured. For ethical reasons one cannot measure the truly serious types of aggression. For example, one cannot randomly assign participants to high versus low provocation conditions, give all participants guns, and then assess which group does the most killing. Thus, researchers have had to develop ways of measuring aggressive behavior in experimental studies that are ethical and that validly measure the concept "aggressive behavior." A later section of this chapter describes a number of such measures. There is one type of experimental study in which extreme types of aggression can be used as the dependent variable. This is intervention studies, in which the experimental manipulation involves randomly assigning some participants to an intervention that is designed and theoretically expected to reduce aggression, while randomly assigning others to a no-intervention control condition. This is analogous to medical studies in which a new treatment (e.g., a new headache remedy) is tested to see if it works better than a placebo control condition. Cross-sectional Correlational Studies Cross-section correlational studies (usually called "correlational" studies) are those in which the independent variable is measured, rather than manipulated, and both the independent and the dependent variables are measured once, frequently at the same point in time. For example, a researcher might ask adolescents to fill out questionnaires that assess a number of their personal characteristics (e.g., trait irritability, gender, socioeconomic status, media use

4 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -4- habits) and a measure of aggressive and violent behavior. In such a study, one or more variables are thought of as independent variables that are associated with (or "predict") the dependent variable. For example, habitual use of violent media might be theoretically expected to "predict" aggressive behavior. Major Strength The main strength of such correlational studies is that even extreme types of aggression can be measured, such as using force to get money from other people (i.e., robbery, assault). Sometimes the measures are from self-reports. Other cross-sectional studies have used reports by parents, teachers, peers, even the judicial system (e.g., arrests). Major Weakness The main weakness of cross-sectional correlational studies is that causality is very difficult to establish. Consider, for example, a correlational study that measures only educational achievement (as the independent variable) and having a violent criminal record (as the dependent variable). The typical finding would be that adults whose highest level of education was junior high school are much more likely to be in prison for a violent crime than those with a high school or college degree. In statistical terms, highest level of educational attainment is negatively correlated with violent criminal behavior. But does a poor education cause violent behavior? Or, does being a violence prone individual interfere with educational achievement? Or does some other unmeasured variable such as low IQ, attention deficit disorder, or poor parenting, or early drug use cause both violent behavior tendencies and poor school abilities? In other words, there remain many plausible alternative explanations of the strong negative correlation between educational achievement and violent behavior, alternatives that do not require assigning a causal role to educational achievement. Statistical Control of Alternative Explanations

5 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -5- Of course, some cross-sectional correlational studies are better than others at teasing out causality. A well-designed correlational study might also measure some (or all) of the most theoretically plausible alternative explanation variables along with the independent variable of interest. In such cases, the researcher can statistically control for the alternative explanatory variables and then see whether the independent variable of interest remains a statistically significant predictor of the dependent variable. If so, then the causal hypothesis gains some strength, even though it may not be seen as conclusive. Figure 5.2 illustrates this approach, called regression, with Venn diagrams. Panel A shows two variables, labeled Media Violence and Physical Aggression. The media violence circle represents the variance of the measured predictor (independent) variable. In other words, it represents the extent to which different people in the sample have different scores on the media violence measure. The other circle represents the variance in the dependent variable, physical aggression. The area of overlap between these two circles represents the size of the target correlation, labeled T. That is, T is the simple correlation between media violence scores and physical aggression scores. The larger area T is, the bigger the correlation. If these two variables were perfectly correlated (r = 1.0), then the two circles would perfectly coincide. If the two variables were totally uncorrelated (r = 0), there would be no overlap at all. One alternative explanation of the correlation between media violence exposure and physical aggression concerns sex of the participants in the study. It is known that males consume more media violence than females, and that males tend to behave more physically aggressive than females, at least in many contexts. In other words, sex may be correlated with or confounded with both the measured predictor variable (media violence) and the dependent variable (physical aggression). Panel B illustrates these correlations as well. Area A+B represents the correlation between media violence exposure and sex, whereas Area B+C

6 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -6- represents the correlation between physical aggression and sex. Furthermore, note that Area B is part of both the media violence correlation with physical aggression, and the sex correlation with physical aggression. That is what is meant by saying that sex is confounded with the media violence effect on physical aggression. In a regression analysis, the researcher statistically removes the confounded part of the target relationship, and tests the magnitude of what is left. In Panel B, the new target correlation, T', is a much smaller area. It represents the correlation between media violence and physical aggression after controlling for sex. It sometimes is called a semipartial correlation. If this semipartial correlation is still statistically greater than zero, then we can say that the alternative explanation for the observed relation between media violence and physical aggression fails to account for the target relation. In other words, media violence exposure is associated with higher levels of physical aggression even after controlling for sex. Overcorrection in Regression Let's consider once more Area B, the part of physical aggression that overlaps both media violence and sex. In the analysis of media violence effects on physical aggression, Area B is ignored. Actually, it is subtracted from what counts as the effect of media violence on physical aggression. But, if media violence does actually have some causal effect on the likelihood of physical aggression, and if males are exposed to more media violence than females, then some portion of Area B actually "belongs" to the media violence effect. In other words, the test of whether Area T' (called "T prime") is significantly greater than zero is statistically very conservative, because it assumes that none of the confounded variance (Area B) belongs to media violence. Now imagine what happens in more complex cross-sectional studies in which numerous alternative variables are statistically controlled. That is, imagine adding additional overlapping

7 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -7- circles to Panel B, perhaps representing parental income, neighborhood violence, and other potential predictors of physical aggression. In each case, only the part of physical aggression that uniquely overlaps media violence is counted as part of the media violence effect. In short, such regression techniques likely overcorrect confounded variables. The deck is stacked against finding a significant target relation. However, researchers often are willing to do this in order to provide the most stringent tests of their theoretical hypotheses. This is because scientists tend to believe that it is better to make a Type II error (failure to reject the null hypothesis when in fact it should be rejected) than a Type I error (rejection of the null hypothesis when it should not be rejected). However, to the extent that the target relation (in this example, media violence and physical aggression) survives the addition of several plausible alternative explanations, the causal theory behind that target relation gains strength. Figure 5.2. Using statistical regression to control for plausible alternative explanations. For example, Anderson and Anderson (1996) used this type of "destructive testing" approach to test the target hypothesis that hot temperatures increase aggression. They began by observing that there is a positive correlation between the hotness of U.S. cities and their violent

8 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -8- crime rates. The most important theoretically plausible alternative explanation was that warmer areas of the U.S. had a southern culture of violence involving honor that had been created either by the types of people who had originally settled in the south, or by the needs of a herding economy that characterized the early days of this warmer region, or by the slavery economy of the old south (Cash, 1941; Gastil, 1971; Hackney, 1970; Nisbett, 1993). So, Anderson and Anderson created a variable that assessed each city's southernness according to these criteria and statistically controlled for it. Other variables that could also be seen as providing plausible alternative explanations of the heat/violence relation were included as well (e.g., poverty rate, age distribution, education level). Even after controlling for over a dozen variables, the heat/violence correlation remained significant. By ruling out these alternative explanations, the plausibility of the heat/violence relation being a causal one was greatly increased. Table 5.1 shows the main results of that study. In this table, all of the effect sizes (i.e., the correlations and semipartial correlations) refer to the effect of a composite measure of temperature on violent crime rates. The "None" column presents the simple raw correlation between temperature and each of the violent crime measures. The "SCV" column shows the semipartial correlations after composite measures of Southern Culture of Violence were statistically controlled. As one might expect from our discussion of Figure 5.2, panel B, these correlations are somewhat smaller than the raw correlations in the "None" column. However, the fact that the semipartial correlations remain statistically significant contradicts the claim that the Southern Culture of Violence accounts for the temperature/violent crime relationship. The last column of semipartial correlations, labeled "12 Social Variables" shows that the temperature/violent crime effect remains significant even after controlling (statistically) for SCV and 12 additional variables. This is a remarkably robust finding, especially when you consider how conservative these regression

9 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -9- procedures are; imagine adding another dozen predictors to Panel B in Figure 5.2, each slicing out another portion of area T'. Table 5.1. Correlations between temperature and violent crime in U.S. cities. Variables partialled from the temperature-violent crime effect Type of Violent Crime None SCV SCV & 12 Social Variables Violent Crime Index (N=253).61**.37**.21** Murder (N=253).61**.32**.19** Rape (N=260).48**.29**.13* Assault (N=260).48**.28**.16* Notes: SCV = southern culture of violence index. Social variables included unemployment rate, per capita income, poverty rate, mobility, 2 education variables, population size, 3 age variables, and 2 race variables. * p <.05. **p <.001. Adapted from Anderson, C.A., & Anderson, K.B. (1996). Violent crime rate studies in philosophical context: A destructive testing approach to heat and southern culture of violence effects. Journal of Personality and Social Psychology, 70, It also is important to note that in this same study, temperature was not strongly related to nonviolent crime rates. This also supports the basic theory underlying the study of temperature effects on crime. Finally, this study also showed that if you treat the SCV/violent crime relation as the target hypothesis, you find that controlling for temperature reduces the SCV/violence effect to statistical nonsignificance. Longitudinal Studies Longitudinal studies are those in which the independent and dependent variables are assessed at two or more points in time, separated by theoretically meaningful time intervals. For

10 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -10- example, a researcher might assess parental use of aggressive discipline techniques and children's aggressive behavioral tendencies for the same group of children at four points in time separated by one or more years. Major Strengths The main advantages of longitudinal designs are two-fold: (a) Consequential, real world aggression can be assessed (e.g., fighting at school, criminal behavior); (b) It is easier to establish causality than with cross-sectional designs because one can rule out a host of key alternative explanations. For example, Sheehan and Watson (2008) found that mothers' use of aggressive discipline at one point time predicted the level of aggression displayed by their children at later points in time, even after statistically controlling for each child's aggressive tendencies at the earlier point in time. In other words, early parental use of aggressive discipline techniques was positively associated with level children's aggressive behavior at a later point in time, even after statistically controlling for how aggressive each child was at the earlier time period. One key alternative explanation of typical cross-sectional studies of this type that the only true causal relation is opposite in direction, with the aggression level of children causing the aggression level of the parents is ruled out by controlling for children's level of aggression at the first measurement time period. Sophisticated longitudinal designs and statistical methods also make it possible to uncover cases of bi-directional causality. This occurs when two correlated variables, such as parental aggressiveness and child aggressiveness, exert causal influences on each other. Such bidirectional causality occurs with some frequency in the aggression domain, where aggression by one person in a relationship causes an increase in aggression by the other person, which in turn causes further increases in aggression by the first person, and so on. Indeed, Sheehan and Watson found evidence of bi-directional causality in their study, as shown in Figure 5.3. This was

11 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -11- particularly true from Time 1 to Time 2. For example, the amount of aggressive discipline that mothers used at Time 1 was positively related to the child's aggression level at Time 2, whereas the child's aggression level at Time 1 was positively related to the mother's use of aggressive discipline at Time 2. Note, however, that time lagged associations between mother's aggressive discipline and child's later aggression were more consistent across the waves of data (i.e., at all three lags) than the time lagged associations between child's aggression and mother's later use of aggressive discipline (i.e., this was significant only for the Time 1-Time 2 lag). Figure 5.3. Results of structural equation analysis (maximum likelihood estimation using standardized coefficients) evaluating the theoretical model of reciprocity over time between a mother s use of aggressive discipline and child aggression. Correlated residuals between observed indicators of discipline at each time point are not shown. ***p <.001, **p <.01, *p <.05, t p <.10, not tested (fixed loading). CBCL, Child Behavior Checklist. Major Weaknesses Longitudinal studies also have their drawbacks. Two of the biggest concern practicality rather than scientific validity. First, they are expensive. Collecting data on the same participants on multiple occasions over a long period of time is difficult, frequently requiring expensive

12 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -12- procedures to keep track of them and payments to keep them as active participants. Nonetheless, one set of longitudinal studies of aggression have followed the same participants from childhood to their late 40s (Huesmann, Dubow, Eron, & Boxer, 2006). Second, for many aggression and violence issues, it can take decades and huge sample sizes before the results are known. For example, conducting a prospective longitudinal study of childhood precursors of habitual violent offending would require an initial sample size of at least 10,000 and a duration of at least 30 years. Few researchers can stake their careers on such studies, and funding agencies have been very reluctant to fund such long term projects. One related problem does concern validity: drop-out rate. Some initial participants move to other school districts and other cities, others decide they no longer want to participate, still others die. Even if the original sample is highly representative of the population, drop-outs tend to reduce that representativeness. In the aggression and violence domain, those with the most risk factors for later violent behavior also tend to be those most likely to drop out of the study. Mixed Designs Many of the best studies use multiple design features. Experimental studies of aggression often include correlational variables as well. For example, Giancola and Parrott (2008) randomly assigned participants to either an alcohol or a placebo drink condition prior to performing a standard laboratory aggression task involving the delivery of electric shocks to a fictitious game opponent. Prior to the aggression task, the researchers also measured each participant's level of trait physical aggressiveness. Both the experimental independent variable (alcohol versus placebo drink) and the correlational independent variable (trait physical aggression) predicted the average shock intensity that participants delivered to their opponent. Those who had consumed the alcohol drink behaved more aggressively (gave higher shocks) than those who had consumed

13 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -13- the placebo drink. In addition, participants who scored high on trait physical aggressiveness behaved more aggressively than those who scored low on trait physical aggressiveness. The inclusion of appropriate cross-sectional correlational variables in experimental designs is advantageous for several reasons. First, the inclusion of such variables allows additional tests of key theoretical hypotheses. Giancola and Parrott were able to test not only the hypothesis that alcohol consumption increases physical aggression, but also the hypothesis that people who score high on trait measures of aggression tend to behave more aggressively even in the artificial confines of the experimental laboratory. Second, including individual difference variables can increase the precision or power of the statistical analyses, which increases the likelihood that the study will successfully detect true experimental effects. It does so by reducing the error variance or "noise" in the measurement of the dependent variable. Third, including individual difference variables allows tests of whether particular subgroups are relatively more or less susceptible to the experimental manipulation. Giancola and Parrott, for instance, demonstrated that participants who scored high on the measure of trait physical aggression were more influenced by the alcohol experimental manipulation than were the low aggressive participants, as shown in Figure 5.4.

14 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -14- Figure 5.4. Effects of alcohol and trait physical aggression on unprovoked shock intensity. Adapted from Giancola, P. R., & Parrott, D. J. (2008). Some experimental studies also have a longitudinal component. For example, well designed intervention studies randomly assign participants (or schools) to the various experimental manipulations, measure the dependent variable (e.g., aggression at home and in school) as well as relevant correlational predictor variables (e.g., SES, personality traits, media habits) before the experimental manipulation takes place. They then re-measure these variables at appropriate intervals after the experimental manipulation. One such study was conducted by Robinson, Wilde, Navracruz, Haydel, and Varady, Two elementary schools similar on many key factors were selected for the study; one was randomly chosen to participate in the intervention, and the other served as a control. The intervention consisted of 18 classroom lessons over a 6-month period. It included elements of media education and attitude interventions. At the end of this 6-month period, the children were encouraged to not watch TV or movies or play video games for a "TV Turnoff" period of 10 days. The children also were encouraged to create and follow a video-entertainment budget of 7 hr per week. Newsletters

15 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -15- enlisted parents' support in helping their children achieve these goals. Aggressive behavior was assessed before the intervention (September) and again 7 months later (April). Controlling for pre-intervention levels of aggression, the results showed that the intervention group behaved less aggressively at post-intervention than did the control group. MEASURING AGGRESSION AND RELATED VARIABLES Choice of outcome measure is crucial to any study, both because it influences the likelihood that the study will yield useful results and because of theoretical relevance. A measure of aggressive behavior that is appropriate in one research context may well be inappropriate in another. For example, a count of how often each child trips, pushes or bites another child in a daycare setting can be a useful measure of physical aggression in that research context (i.e., young children at daycare), but would not be a valid measure of physical aggression for college students in a laboratory setting. A less obvious but equally important example frequently arises in the study of violent video game effects. Because violent video games involve a lot of physical aggression and almost no indirect or relational aggression, the dominant theoretical models of social learning and development all predict that playing such games is most likely to influence physical aggression. Measures of verbal and indirect aggression are unlikely to provide sensitive tests of the main hypothesis that exposure to violent video games increases the likelihood of aggressive behavior. It is impossible to succinctly describe all of the measures that have been used or could be used in the study of aggression. We focus here on a few of the most common measures of aggressive behavior, cognition, and affect. Aggressive Behavior Measures Physical aggression in lab settings. Numerous methods have been developed that allow direct observation and measurement of aggressive behavior in laboratory settings. A common

16 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -16- procedure used to measure physical aggression is the teacher/learner paradigm, sometimes known as the Buss aggression machine paradigm (e.g. Buss, 1961; Donnerstein & Berkowitz, 1981; Geen & O Neal, 1969; Milgram 1974). In this procedure, participants are told that purpose of the study is to explore effects of punishment on learning. They are paired with a supposed second participant (actually a member of the research team, known as a "confederate"). The real participant is selected to be the teacher and the confederate is selected to be the learner. The participant presents stimuli to the confederate who seemingly tries to learn them. When the learner gives an incorrect response, the participant is supposed to punish him or her with an electric shock. Aggression is measured by the intensity and/or the duration of the shock the participant chooses to give the confederate. For example, Donnerstein and Berkowitz, (1981) used this procedure to measure effects of combining violent and sexual media content on aggression of males towards a female target. Participants who had viewed a violent, sexual film delivered shocks of a higher intensity to a female learner than did those who viewed films containing only violent or sexual content. There have been many variations of this task, including use of different types of punishments (e.g., hand in ice water instead of electric shock, Ballard & Lineberger, 1999) and different rationales for why the participant is delivering punishments (see Baron & Richardson, 1994, pp ). Another common method of measuring physical aggression in the laboratory is the competitive reaction time task (Bushman, 1995; Giancola & Parrott, 2008; Taylor, 1967). Participants in this task compete against a supposed opponent on a reaction time task in which the winner delivers aversive stimulation (an electric shock or a noise blast) to the loser. In actuality, the pattern of wins and losses is predetermined by the experimenter. Provocation can be manipulated by increasing the intensity of shocks set by the "opponent." Aggression can be measured as the intensity, the duration or the number of high-intensity blasts given. As noted

17 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -17- earlier, Giancola and Parrott (2008) used this task to assess the effects of alcohol and trait aggression on aggressive behavior. Like the teacher/learner paradigm described earlier, the competitive reaction time task has been used in various modified forms in hundreds of studies, and is one of the most extensively validated measures of physical aggression (Gaebelein & Taylor, 1971; Giancola & Zeichner, 1995; Bernstein, Richardson, & Hammock, 1987). A third commonly employed method to study direct physical aggression is to place the participant and the confederate in a situation that requires the confederate to evaluate the participant and later requires the participant to evaluate the confederate using physically noxious stimuli. In the evaluation paradigm (Berkowitz, 1962) for example, participants are led to believe that they will be evaluating another student s performance on an assigned task. Solutions are evaluated by delivering anywhere from 1 to 10 electric shocks, where 1 shock indicates a very favorable evaluation and 10 shocks indicates a very unfavorable evaluation. In some studies, the confederate evaluates the participant s solution. Generally, half of the participants receive a positive evaluation from the confederate (e.g., 1 shock), whereas the other half receive a negative evaluation (e.g., 7 shocks). After exposure to some treatment (e.g., a violent or nonviolent film), the participant then evaluates the confederate s solution. The measure of aggression is the number of shocks the participant gives the confederate. A more recently developed laboratory measure of physical aggression is the hot sauce paradigm, developed by McGregor and colleagues (McGregor, Lieberman, Greenberg, Solomon, Arndt, Simon, & Pyszczynski, 1998; Lieberman, Solomon, Greenberg, & McGregor, 1999). In this procedure, participants decide how much hot sauce another person who is known to dislike spicy food must consume. Alternatively, one can have the participant determine the degree of hotness of the sauce that the other person must consume. Aggression is measured as the amount of hot sauce given to the target and/or the degree of hotness of the sauce selected. A wide range

18 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -18- of experimentally manipulated variables have been shown to affect aggression on this measure, including attacks on one's world view, provocation in the form of having to drink an unpleasanttasting liquid, exposure to violent media, alcohol, rumination after a mild provocation, and ostracism (Barlett, Branch, Rodeheffer & Harris, 2009; Brummert & Warburton, 2011; Denson, White, & Warburton, 2009; Lieberman et al., 1999; Warburton, Williams, & Cairns, 2006). Other laboratory aggression measures. Some laboratory studies use verbal aggression measures. For example, in some studies the participant is given the opportunity to provide a potentially harmful written or verbal evaluation of another person (e.g., another participant, a confederate, or the experimenter), and does so knowing that the evaluation could hurt the other person. Sometimes the verbal aggression is direct, meaning that the participants believe that the target of their harmful evaluations will see or hear it. For example, Wheeler and Caggiula (1966) had participants listen and later evaluate another person's (actually, a confederate) extreme and socially undesirable statements. The participants believed that the other person would hear their evaluations, so anything negative in the evaluations would presumably cause some harm. These evaluations were recorded and later coded for the degree of hostility. Sometimes the evaluation is in the form of ratings that the target will not see, but that the participant believes will harm the target indirectly. For example, Berkowtiz (1970) randomly assigned some female undergraduates to an anger induction condition (in which they listened to a job applicant's insulting statements about university women) or to a control condition. Half in each condition then listened to either a hostile or a non-hostile comedian. All participants then rated the job applicant on several measures, with the knowledge that their ratings could affect the applicant's chances of getting the job. Interestingly, the women who had heard the hostile humor gave the applicant worse ratings than those who had heard the neutral humor. Other similar

19 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -19- indirect verbal aggression measures have ranged from ratings of competence, to liking, to job performance, and to grades (e.g., Dill & Anderson, 1995; Ohbuchi, Kameda, & Agarie, 1989). Perhaps the most recent addition to the list of laboratory aggression tasks is the tangram task (Gentile et al., 2009). In one study Gentile and colleagues randomly assigned participants to play a violent video game, a prosocial video game, or a game that was neither violent nor prosocial. Later, participants assigned an anonymous partner a set of 11 easy, moderately complex or difficult tangram puzzles to attempt to solve within 10 minutes. Participants were led to believe that the partner would win a prize if they completed a sufficient number of puzzles in 10 minutes. The number of hard puzzles chosen constituted a measure of aggression, whereas the number of easy puzzles measured helping behavior. As expected, the violent video games increased aggressive choices whereas the prosocial games increased helpful choices. Because this measurement task is the newest, it has received less empirical attention that the older measures described earlier, and thus does not yet have the extensive network of validation studies. Aggression measures outside the lab. The variety of ways that one can measure aggressive behavior outside of controlled laboratory setting is huge, limited only by the combination of the conceptual definition and the creativity of researchers. Generally, they can be categorized as self-reports, other reports, and archival. Self-reports may be very specific, such as reporting how many physical fights one has been in during the past school year. Or, they may be broad trait-like measures of habitual aggressiveness. They may include any type of aggression (e.g., verbal, physical) at any severity level (e.g., said mean things about a classmate, attacked a peer with a knife or gun). Common self-report measures of trait aggression include the physical and verbal aggression subscales of the Buss and Perry (1992) Aggression Questionnaire and the physical violence subscale from the

20 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -20- National Youth Survey (Anderson & Dill, 2000; Elliot, Huizinga, & Ageton, 1985). Other commonly used self-report trait aggression scales that include relatively more items that are not strictly aggressive behaviors are the Caprara Irritability scale (Caprara et al., 1985) and the Cook-Medley Hostility Inventory (Cook & Medley, 1954). Of course, there are many self-report measures, and researchers create new ones as the empirical and theoretical need arises. Others' reports of aggression include a wide range of measures, usually subcategorized into peer reports, teacher/supervisor reports, parent reports, and direct observation. Peer reports are frequently used in pre-high school settings. Often these involve asking each student in a classroom to rate each of their classmates on specific behaviors, or to nominate classmates who do certain aggressive behaviors. For example, it is common to ask, "Who pushes, shoves or hits other kids to get what they want?" Teacher/supervisor reports ask similar questions about those under their care or supervision. Parent reports oftentimes ask about the frequency with which their child has done specific aggressive behaviors; other parent reports are more vague, asking for ratings of "how aggressive" is your child. Direct observation studies often involve audio/video recordings of behavior in some naturalistic setting, followed by standardized coding of the recorded behavior. Sometimes, however, trained observers watch and code behaviors in the setting, such as while watching children on a playground. In a field study of the effects of uncomfortably hot temperatures on aggression, Kenrick & MacFarlane (1984) measured horn honking in response to a confederate blocking an intersection. The study took place in the spring and summer in Pheonix, Arizona, with temperatures ranging from 84 to 108 F. They also coded whether the participant's car was air conditioned (windows rolled up) or not (windows rolled down). They used three different measures of horn honking: latency to honk, number of honks, and total time spent honking. One can reasonably argue that honking the horn one time and doing so quickly is instrumental in

21 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -21- alerting the driver of the blocking car that the light has changed. It is particularly instrumental in extremely hot conditions, especially for participant's whose car is not air conditioned. However, the latter two measures frequent and long blasts are not differentially instrumental as a function of temperature or as a function of air-conditioning. Once one has honked the horn the instrumental role of further honking is negligible. Because the three measures were highly intercorrelated and yielded the same results, a composite of them was created and reported by Kenrick and MacFarlane (1984) as a measure of aggressive horn-honking. The temperatureaggression hypothesis predicts greater horn honking at greater temperatures, especially by participants in cars without air-conditioning. As was expected, horn-honking increased as temperature increased, especially for those in un-air-conditioned cars. Archival measures are derived from written records, such as crime reports and school incident records. For example, a number of studies of the relation between uncomfortably hot temperatures and aggression have used such archival data as violent crimes, riots, spouse abuse, and even the frequency of Major League Baseball batters being hit by pitched balls (see Anderson, 1989, for a review). For example, Carlsmith and Anderson (1979) found that the likelihood of a riot occurring in the U.S. during the high riot years depended to some extent on how hot the temperature was. Figure 5.4 illustrates this effect.

22 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -22- Figure 5.4. Conditional probability (likelihood) of a riot as a function of ambient temperature. From Carlsmith & Anderson, Reprinted by permission. Frequently, archival measures are included with other types of aggression measures in the same study. For example, in a 15 year longitudinal study of television violence effects Huesmann and colleagues (Huesmann, Moise-Titus, Podolski, & Eron, 2003) obtained self-reports and peerreports of aggressive behavior in childhood (1 st through 4 th grades) and self-reports of aggression (including criminal behavior) and archival criminal records in early adulthood. They found that high exposure to television violence in childhood predicted adult aggressive behavior (including criminal behavior) even after controlling for childhood aggressiveness, childhood intellectual achievement, and parental education. Aggressive Cognition Measures Aggressive behavior is related to a wide range of aggression-related thoughts, beliefs, and attitudes, both empirically and theoretically. For these reasons, many different measures of aggressive cognition have been used to help aggression scholars understand the genesis of aggression.

23 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -23- Accessibility of aggressive thoughts. Many laboratory experiments measure short-term influences of aggression-related variables on cognitive processing. Such short-term effects mainly occur through priming of aggressive knowledge structures, making them temporarilty more accessible (Anderson & Huesmann, 2003). Various methods have been successfully used in laboratory settings to measure these brief changes in aggressive thinking. One common procedure is thought listing. One can simply ask the research participants to list their thoughts in response to some stimuli or situational context. Usually, the thought listing task has some structure imposed. For example, Bushman and Anderson (2002) had participants play either a violent or nonviolent video game, and then read the beginning of a story (such as a car accident) and then list 20 things that one of the characters in the story would do/say, think, and feel. These thoughts were then coded for aggressive content by trained coders who did not know which video game condition the participant had played. As expected, participants who had just played a violent game generated more aggressive thoughts than those who had played a nonviolent game. Figure 5.5 illustrates these findings for each of the three types of thoughts.

24 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -24- Figure 5.5. Frequency of aggressive responses to story stems as a function of type of video game played and type of thought. From Bushman, B.J., & Anderson, C.A. (2002). Violent video games and hostile expectations: A test of the general aggression model. Personality and Social Psychology Bulletin, 28, Reprinted by permission. Other studies impose less structure on the thought listing task. The simplest version is to simply provide participants with a sheet of lined paper and ask them to list the thoughts that occurred to them during some specified period of time, such as during the previous task. Many other ways of measuring accessibility of aggressive cognition have been used. Examples include: (a) free associations to homonyms with one meaning more aggressive than the other (e.g. cuff, mug); (b) similarity ratings of word pairs that include words that are ambiguously aggressive (e.g., stick, blood); (c) word completion task in which participants are given a list of word fragments (e.g., h_t) some of which can be completed to form aggressive or nonaggressive words (hit or hat); (d) reading reaction times to aggressive and nonaggressive words (also called the word pronunciation task). Interestingly, measures of recent or current accessibility of aggressive thoughts also tend to correlate with longer term personality traits and attitudes related to aggression. Aggressive beliefs and attitudes. Numerous scales and procedures have been used to assess

25 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -25- aggressive cognitions that are usually considered to be more stable or less malleable than those described in the previous section. One commonly used measure is the Normative Beliefs about Aggression Scale (NOBAGS, Huesmann, Guerra, Miller, & Zelli, 1992). The NOBAGS is a 20- item scale with two major subscales. One measures general approval of aggression (e.g., "it is okay to hit others"). The other assesses beliefs about the appropriateness of retaliatory aggression (e.g., "it is okay to hit others if they hit you first"). Respondents rate the extent to which they agree with such pro-aggression statements. The NOBAGS has been used with children and adults, generally finding that those who score higher on the NOBAGS also behave more aggressively. One recent study found that this positive relation between NOBAGS scores and aggressive behavior is especially strong after people have consumed alcohol (Levinson, Giancola, & Parrott, 2011). Participants in this experiment completed the NOBAGS, and were randomly assigned to consume a beverage with alcohol or a placebo beverage (which smelled and tasted like alcohol). Later, they performed a version of the competitive reaction time task to measure aggressive behavior. As shown in Figure 5.6, participants who scored high on the NOBAGS behaved more aggressively than those who scored low, but this was especially true for those who were intoxicated. Figure 5.6. Aggression (electric shock) as a function of Normative Beliefs about Aggression (NOBAGS) and alcohol. From Levinson, C. A., Giancola, P. R., & Parrott, D. J. (2011). Beliefs about aggression moderate alcohol's effects on aggression. Experimental and Clinical Psychopharmacology, 19, Reprinted by permission.

26 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -26- There are many other scales designed to measure attitudes and beliefs concerning aggression. Some focus on attitudes and beliefs concerning particular targets of aggression, based on race, gender, religion, nationality, and so on. For example, there are a number of scales assessing attitudes towards rape such as Burt's (1980) rape myth acceptance scale (e.g., "women who get raped while hitchhiking get what they deserve") and her adversarial sexual beliefs scale (e.g., "most women are sly and manipulating when they are out to attract a man"). Similarly, some focus on hostility towards women, such as Check, Malamuth, Elias, & Burton's (1985) hostility towards women scale (e.g., I feel that many times women flirt with men just to tease or hurt them"). Other scales are more general in nature, and include a wide range of aggression domains. For example the Revised Attitudes towards Violence Scale assess attitudes in four separate domains, including war, penal code violence, corporal punishment of children, and violence towards intimates (Anderson, Benjamin, Wood, & Bonacci, 2006). Other commonly used scales assess normative beliefs about violence (Bryant, Carveth & Brown, 1981; Grebner, Gross, Jackson-Beeck, Jeffries-Fox & Signorelli, 1978), and positive attitudes towards violence (Funk, Elliott, Urman, Flores, & Mock, 1999). Biased cognitive processes. Several aggressive cognition measures assess biased cognitive processes. For example, the Buss-Perry Aggression Questionnaire mentioned earlier

27 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -27- includes a subscale designed to measure trait hostility, conceived of as a biased cognitive view of the world. That is, the beliefs embodied in such a hostile attitude lead to systematically negative interpretations of events. The most widely studied bias in this domain is the hostile attribution bias, a key concept in Dodge's social information processing theory of aggressive children (Crick & Dodge, 1994; Dodge & Coie, 1987). Hostile attribution bias typically is measured by presenting participants with a series of stories involving provocations commonly experienced by children and adolescents (e.g., Crick, 1995). For each story, participants imagine themselves as the person in the story who has been harmed (e.g., had milk spilled on them), and then answer two questions. The first presents four possible reasons for the peer s behavior, two of which indicate hostile intent and two reflect benign intent. The second question asks whether the provocateur(s) intended to be mean or not. In general, children who score higher on this measure, that is, those who tend to believe that harmful actions were intentional and meant to be mean, also tend to behave more aggressively. Implicit association test. The implicit association test (Greenwald, McGhee, & Schwarz, 1998) is a general compter-based procedure for measuring implicit associations between pairs of concepts. The procedure is for creating an IAT measure is too complex to detail here. Suffice it to say that one can assess implicit beliefs over a wide range aggression-related domains, including racial biases (Park, Felix, & Lee, 2007) and self-perceptions of aggressiveness (Uhlmann & Swanson, 2004). Aggressive Affect Measures Many studies assess various types of affect and emotion that are related to aggression. Some measures are designed to assess affective states that may be fairly fleeting, such as anger or state hostility. Most common are self-report measures, such as the state anger scale

28 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -28- (Spielberger, Jacobs, Russell, & Crane, 1983), the state hostility scale (Anderson, Deuser, & DeNeve, 1995). the Multiple Affect Adjective Checklist (Zuckerman, Lubin, Vogel, & Valerius, 1964) and the Positive and Negative Affect Schedule (PANAS; Watson, Clark, Tellegen, 1988). This last scale is widely used and has the advantage of assessing both positive and negative affect, as well as several more specific subtypes of affect. However, it is a less sensitive measure of hostility/anger, most likely because it contains fewer items for this more specific affective state Other self-report measures attempt to assess more persistent or trait-like aggressionrelated affect, such as various trait anger scales (e.g., Spielberger et al., 1983) or the trait anger subscale of the Buss and Perry Aggression Questionnaire. ADDITIONAL METHODOLOGICAL AND PHILOSOPHICAL CONSIDERATIONS Case Studies When one gathers information on a specific instance or "case" of violence, with the goal of understanding why the incident occurred, we have a case study. For example, one could try to

29 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -29- understand the Columbine High School case by examining characteristics of the shooters, their families, events that happened to them at school, and so on. From a theory-testing standpoint, individual case studies are not very useful. One cannot establish causality with any degree of certainty. However, if one carefully collects the same information on a larger set of conceptually related cases, one sometimes can create a useful correlational study. For example, Leary, Kowalski, Smith, and Phillips (2003), collected information on 15 school shootings that occurred in the U.S. between 1995 and The most consistent finding was evidence that the shooters had experience some form of either acute or chronic rejection (or both) bullying, ostracism, or romantic rejection in 13 of the cases. Although this is one of the best case-study-based empirical studies on violence, it does not provide very good evidence for or against the hypothesis that rejection causes an increase in the likelihood of aggression and violence. The reason is because there is no relevant comparison group, other than an implicit one of students not involved in extreme acts of violence. What proportion of U.S. school students of the same age and demographic characteristics as the shooters have evidence of either acute or chronic rejection? Is it higher or lower than the 13/15ths (87%) of shooting cases that included rejection? We know from other studies that bullying rates, ostracism, and romantic rejection are very high in the middle school through high school age range, so perhaps the 87% rate among school shooters isn't particularly high. Although the case study is not a useful design for testing hypotheses, it can be scientifically useful in another way. Specifically, case studies often yield interesting hypotheses that can be refined and tested using stronger research designs. Studying the background of extremely violent people, or of extremely nonviolent people, can lead a thoughtful scholar to ideas about the conditions that might promote the development of aggressive tendencies or of nonaggressive tendencies.

30 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -30- Science, Triangulation, Causality, and Alternative Explanations Very often, scientists and nonscientists use the same language to mean very different things. Furthermore, among themselves scientists differ on issues of preferred research method, comfort in making causal statements, and willingness to generalize. Nonetheless, several general principles accurately describe how most modern science (at least, behavioral science) operates. Theory Development and Empirical Research Iteratively Cycle All modern sciences advance through a complex and recursive interplay of theory and empirical research. Scientists identify a question of interest (e.g., what makes children grow up into violent or nonviolent adults?), develop hypotheses (e.g., observing violence when young increases the likelihood of later aggression, or poor school performance increases the likelihood of later aggression), test the hypotheses using various research methods, then revise and refine the original hypotheses. Over time, what may have been a single question evolves into a more complex set of related hypotheses and empirical findings. Eventually, the surviving hypotheses (i.e., those that have survived or been refined by numerous empirical tests) can be integrated into a larger conceptual model or theory. The theory itself can then be used to generate additional hypotheses, some of which can be subjected to further empirical tests. The results of those tests may confirm key aspects of the theory or require further refinements. The general biosocialcognitive model of human behavior described earlier and the more specific models on which it is based all result from over a hundred years of hypothesis generation/empirical testing/theoretical refinement, dating back to the beginnings of modern psychology. Because of this huge data base and the extended test/revise/test/revise process that accompanies it, the main principles rest on a very sound foundation and are unlikely to be overturned by future research. In this sense, the "theory" is more like a "fact" than an educated guess. However, the specific details and interpretations are always subject to change as new studies are conducted and new discoveries

31 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -31- are made. In this sense, no scientific theory in any field of science is regarded as "fact," at least not by scientists. This difference in meaning of "theory" by scientists and nonscientists leads to much confusion when these two groups attempt to communicate, as in debates about public policy. Scientific Theories and Models Are Causal The vast majority of scientific theories are based on causal principles. The hallmarks of good theory the ability to predict outcomes and to control them require causal models. This principle does not deny that some theories (and sciences) are based primarily on correlational data; the laws of planetary motion have never been tested by randomly assigning planets to various locations in order to observe their effects on other planets, yet astronomy is a science with causal laws. At a given point in history a particular scientific theory may not have generated sufficient evidence to conclude that its causal precepts are without doubt true, but that does not mean that the basic structure of that theory isn't causal. Sometimes the ability to test the causal precepts must await further technological development. Scientific Causality Often Is Probabilistic The old Logic 101 principles regarding the establishment of a factor as being a necessary and sufficient cause of an effect don't apply to most modern science (Anderson & Bushman, 2002). In modern science, many causal factors are seen as probabilistic rather than as absolute. Consider the smoking/lung cancer link. Practically everyone now agrees that smoking tobacco causes lung cancer. But, not everyone who smokes gets cancer, and some people who don't smoke do get lung cancer. What scientists really mean when they say that "smoking causes lung cancer" is that smoking tobacco causes an increase in the likelihood that one will contract lung cancer. The probabilistic nature of modern science results from the fact that multiple causal factors are involved in most medical, psychological, and behavioral phenomena. For this reason

32 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -32- the old necessary and sufficient rules do not apply. Thus, when people argue that violent video games or poverty or parental abuse can't be considered causes of aggression because not all violent gamers or poor people or child abuse victims are excessively aggressive, they are misapplying the "sufficient" rule to a multiple cause phenomenon. Similarly, pointing to violence committed by non-gamers, rich people, or non-abused adults misapplies the necessary rule to multiple cause phenomena. Indeed, extreme instances of violence almost always require the convergence of multiple causal risk factors. Risk Factors Accumulate The principle that risk factors accumulate operates in three ways. First, the more risk factors for aggression that are present in a given situation, the greater the chances are that some type of aggression will occur. Second, more risk factors also means a greater likelihood of extreme forms of aggression. Third, the effects of any given risk factor increase with greater exposure to that risk factor. Just as the effect of smoking on likelihood of lung cancer increases with the number of cigarettes smoked, the effects of most aggression risk factors (such as media violence exposure, having antisocial peers) also get larger with greater exposure. Establishing Causality Largely Involves Ruling Out Plausible Alternative Explanations Creating and testing scientific theories involves creating plausible explanatory networks of ideas, testing those ideas by looking for plausible alternative explanations and gathering relevant data, and then either rejecting the alternative explanations or revising the explanatory network. As the number and plausibility of alternative explanations decline, the strength of the remaining causal explanatory network (i.e., the theory) grows. Thus, even purely cross-sectional correlational data are relevant to establishing causality, in two ways. First, they provide tests of the causal link, hypotheses that are falsifiable by the data. If a causal theory predicts that college students who were physically abused as children will behave more aggressively than those who

33 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -33- were not abused, then that causal hypothesis can be tested with cross-section correlational data. Failure to find such a correlation contradicts and therefore weakens the theory; finding such a correlation supports and therefore strengthens the theory. Second, such data often provide opportunities for the testing of alternative explanations. For example, one alternative explanation might be that college students who grew up in poorer families were also more likely to be physically abused, and that the poverty/aggression link wholly accounts for the apparent link between abuse and aggression. If the correlational study has measured all three variables (abuse, poverty, aggressiveness), then one can test whether or not controlling for poverty eliminates the correlation between abuse and aggressiveness. When such alternative explanations fail, and the target causal hypothesis holds up, the over-arching theoretical explanation gains strength. When alternative explanations succeed or when the target causal hypothesis does not hold up, the overarching theoretical explanation loses strength, perhaps requiring revision of the theory. Different Methodologies Allow Triangulation On The Most Plausible Causal Model The principle of triangulation is widely used both within and outside of science. When the same hypothesis is tested using multiple methods, and all yield the same answer, we have much more confidence in that answer than when only one method is used or when different methods yield different answers. If experimental and cross-section correlational and longitudinal studies are conducted with various participant populations and measures, and if the results converge on the same answer, we should believe that answer. Or as Richard Cardinal Cushing put it (when asked about the propriety of calling Fidel Castro a communist), "When I see a bird that walks like a duck and swims like a duck and quacks like a duck, I call that bird a duck" (New York Times, 1964). The reason for this is that each research method has its own set of strengths and weaknesses. Furthermore, the strengths of one method tend to counteract the weaknesses of other

34 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -34- methods. Thus, the likelihood that a large number of studies using a wide array of methods all happen to have weaknesses that lead to the same incorrect results is very remote. When different methods do lead to different results, it becomes clear to researchers that additional conceptual, theoretical, and empirical work is needed before reaching firm conclusions. Conceptual Relations Between Variables Generalize Occasionally scientists and nonscientists alike bemoan the discrepancies between laboratory procedures and scientific measures versus events in the real world. In the aggression domain, for example, skeptics frequently criticize the use of delivery of electrical shock (or loud noise blasts) to another person as a measure of aggression. The argument is that no one uses electrical shock (or noise) to intentionally harm other people in the real world, so how can we generalize from such laboratory studies to real world aggression? There have been numerous excellent answers to such complaints (e.g., Banaji & Crowder, 1989; Berkowitz & Donnerstein, 1982; Kruglanski, 1975; Mook, 1983). Two points are most relevant. First, what is most important is that conceptual variables generalize, even if specific operationalizations do not. Aggression researchers now know that one of the best predictors of aggression is provocation. As one of us discovered years ago, calling someone a "poo-poo-head" was a very powerful provocation for preschoolers at a daycare facility in Columbia, Missouri. Although we never conducted the relevant experiment, it seems unlikely that this specific operationalization of high provocation would have worked as well on college students. This doesn't mean that provocation effects on preschoolers do not generalize to college students, only that specific operationalizations (e.g., poo-poo-head) of conceptual variables (e.g., provocation) do not always generalize. In general, provocation effects on aggression (at a conceptual level) generalize across age, sex, culture, and a wide range of additional person and situation characteristics. Nonetheless, this example makes it clear that the

35 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -35- specific operationalization of a conceptual variable must fit the participants and the context in which a study (or an intervention) is conducted. This is true of outcome measures as well as experimental manipulations. For example, the best measures of naturalistic aggression for 6 year olds are different from the best measures of naturalistic adulthood aggression. Physical abuse of one's spouse or children is an appropriate measure for adults, but clearly is not appropriate for children. Second, questions concerning the validity and generalizability of laboratory procedures and measures can be addressed empirically. Rather than assuming that findings from studies that use electrical shock do not generalize to other measures of aggression, one can test the generalizability hypothesis. Aggression researchers have done this, finding, for example, that different laboratory measures of aggression correlate positively with each other and tend to respond to the influences of other aggression-inducing variables in the same way (Carlson, Marcus-Newhall, & Miller, 1989). Similarly, variables that predict real life aggression and violence usually have the same effect on laboratory-measured aggression (Anderson & Bushman, 1997). Some of these studies show that situational variables known to increase aggression and violence in the real world have the same type of effect on laboratory aggression. Such variables include provocation, alcohol, the visible presence of a weapon, and media violence. Other studies show that people who are highly aggressive or violent in real life also display heightened aggression on laboratory measures of aggression. Examples include people who score high on indicators of psychopathic characteristics, hypermasculinity, trait aggression, and Type A personality, to name just a few. For example, McCloskey, Berman, Noblett, and Coccaro (2006) investigated the psychiatric diagnosis of intermittent explosive disorder (IED), a condition that involves frequent and disproportionate anger and aggression in the real world. These researchers compared a group

36 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -36- of IED individuals to 3 additional groups of participants: healthy controls (no history of any psychiatric disorders), Axis I controls (people with a diagnosed DSM-IV Axis I disorder such as depression, anxiety, phobias, or schizophrenia but without an IED or Axis II disorder), and Axis II controls (people with a diagnosed Axis II personality disorder such as antisocial personality disorder but not IED). Participants completed a large number of interviews, questionnaires, and a standard laboratory task for measuring aggressive behavior, involving receiving and delivering electric shocks to a reaction time game opponent. This task involves choosing a level of punishment (electric shocks) to be given to an opponent on each of a series of reaction time contests. As expected, participants with a history of aggression and violence (IED participants) were much more likely to choose the most extreme level of shock than any of the three control groups, F(3, 159) = 7.80, p < Figure 5.7 illustrates this result. This is exactly what one would expect if the laboratory measure of aggression generalizes to the real world.

37 Anderson, Huesmann, & Bartholow Chapter 5 Research Methods -37- Figure 5.7. Average number of extreme shocks selected as punishment for the opponent. Adapted from McCloskey, Berman, Noblett, and Coccaro (2006). HC = healthy control; AIC = Axis I control; PDC = personality disorder control; IED-IR = intermittent explosive disorder. Effects of Many Risk Factors Increase With Amount of Exposure Many environmental risk factors for human aggression and violence have cumulative effects that sometimes don t emerge until years after the initial exposure. This is especially true if the outcome being assessed is at the more extreme end of the aggression/violence continuum. This is similar to the fact that relatively few teens or young adults die from cigarette-induced lung cancer (relative to smokers in their 50s and 60s). However, less severe effects such as increased aggressive thought patterns or mild forms of aggression often are seen even with low or brief exposure to the risk factor. Again, this is similar to the fact that smoking even one cigarette produces a number of measurable physiological effects. Null results do not mean "no effect"