SOCIAL-COGNITIVE PREDICTORS OF REACTIVE AND PROACTIVE AGGRESSION: INVESTIGATION IN A DIVERSE, URBAN 5TH GRADE SAMPLE
Shauna K McCarthy
A Thesis
Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of
MASTER OF ARTS
December: 2005
Committee:
Eric Dubow, Advisor
Dara Musher-Eizenman
Anne Gordon ii
ABSTRACT
Eric Dubow, Ph.D., Advisor
Researchers have identified two subtypes of aggression, reactive and proactive, that have unique patterns in terms of their etiologies, correlates (i.e., behavioral, affective, cognitive, social), and long-term outcomes. Reactive aggression is associated with angry affect and is enacted in response to a real or perceived provocation, whereas proactive aggression is not associated with angry affect and is directed toward the fulfillment of some instrumental or social goal. The present study investigated the associations between reactive and proactive aggression and victimization experiences, anger control, and several social-cognitive variables in a diverse,
5th grade, urban sample.
Children who participated in the evaluation of an aggression prevention program
completed surveys assessing their victimization experiences, anger control, and social-cognitive
patterns (i.e., predictor variables). Teachers provided reports of children’s reactive and proactive aggression. Four hypotheses were addressed: 1) reactive and proactive aggression will have low prevalence in this community sample; 2) reactive and proactive aggression will be highly positively correlated in the total sample and gender sub-samples; 3) reactive and proactive aggression will have distinct correlation profiles with the predictor variables; and 4) reactive and proactive aggression will have distinct profiles of predictors. Results of correlation, regression, and discriminant function analyses provided at least partial support for all hypotheses.
Unexpected results emerged including a positive association between low anger control and proactive aggression and greater positive association of several social-cognitive variables with iii reactive as compared to proactive aggression. Potential explanations for these results and areas for future research are discussed. iv
I dedicate this work to the people who have given me the support and encouragement I needed to
reach my goals – family, friends, and faculty. v
ACKNOWLEDGMENTS
I would like to express gratitude to my advisor, Eric Dubow, Ph.D., who was an invaluable support and resource as I worked on this project. I would also like to thank my other committee members, Dara Musher-Eizenman and Anne Gordon, whose insightful comments and input truly improved this paper and contributed to my development as a researcher. I give special thanks to the Powerful Choices Aggression Prevention group for allowing me to access their data set. And finally, I am grateful to the principals, teachers, and students from Toledo
Public Schools who gave their time and shared their beliefs and perceptions so that we might better understand aggression in children. vi
TABLE OF CONTENTS
Page
INTRODUCTION ...... 1
METHOD ...... 42
Participants...... 42
Procedures...... 43
Measures ...... 44
RESULTS ...... 55
Preliminary Analyses...... 55
Hypothesis 1: Reactive and Proactive Aggression Will Have Low
Prevalence in This Community Sample...... 59
Hypothesis 2: Reactive and Proactive Aggression Will be Correlated
Moderately to Highly in the Total Sample and Gender Sub-Samples...... 62
Hypothesis 3: Reactive and Proactive Aggression Will Have Distinct
Correlation Profiles with the Predictor Variables...... 62
Hypothesis 4: Reactive and Proactive Aggression Will Have Distinct
Profiles of Predictors...... 64
DISCUSSION...... 69
Hypothesis 1: Reactive and Proactive Aggression Will Have Low
Prevalence in This Community Sample...... 69
Hypothesis 2: Reactive and Proactive Aggression Will be Correlated
Moderately to Highly in the Total Sample and Gender Sub-Samples...... 70
Hypothesis 3: Reactive and Proactive Aggression Will Have Distinct vii
Correlation Profiles with the Predictor Variables...... 70
Hypothesis 4: Reactive and Proactive Aggression Will Have Distinct
Profiles of Predictors...... 73
Overall Conclusions...... 74
Limitations and Future Directions ...... 77
REFERENCES ...... 81
APPENDIX A. Parent Consent Form...... 104
APPENDIX B. Child Assent Form...... 106
APPENDIX C. Teacher Letter and Consent Form ...... 107
APPENDIX D. Victimization...... 109
APPENDIX E. Anger Control ...... 111
APPENDIX F. Hostile Attribution Bias ...... 112
APPENDIX G. Retaliation Beliefs...... 113
APPENDIX H. Negative Self Evaluation...... 115
APPENDIX I. Concern for Consequences ...... 118
APPENDIX J. Aggressive Fantasy...... 121
APPENDIX K. Reactive and Proactive Aggression...... 123
viii
LIST OF TABLES
Table
1 Sample Characteristics...... 88
2 Changes in measures across cohorts...... 90
3 Means, Standard Deviations, and Ranges of Study Variables...... 92
4 Intercorrelations among Study Variables...... 93
5 Factor Analysis with All Social-Cognitive Predictors...... 94
6 Analyses of Variance Test for Sex, Race, and School Differences for Study
Variables ...... 95
7 Analysis of Variance Test for Cohort Differences for Study Variables with
Common Measurement...... 96
8 Analysis of Variance Test for Time of Data Collection Differences for
Study Variables...... 96
9 Descriptive Statistics for Reactive and Proactive Aggression...... 97
10 Descriptive Statistics for Extreme Groups...... 98
11 Demographics for Extreme Groups ...... 99
12 Chi-Square for Extreme Group Differences in Sex, Race, School, and Cohort ...... 100
13 Partial Correlations of Criterion Variables with Predictors Controlling
for Opposing Subtype of Aggression...... 101
14 Hierarchical Regressions of Victimization and Social-Cognitive
Variables Predicting Reactive and Proactive Aggression...... 102
15 Discriminant Functions for Extreme Groups...... 103
1
INTRODUCTION
Aggression, defined as behavior that is intended to injure or irritate another person
(Berkowitz, 1993), represents an important and costly social problem. Aggression and anti- social behavior are the most frequent reasons for the referral of children to mental health services
(Kazdin, 1995). Furthermore, aggression among children has implications for their social and emotional development (Kazdin, 1995). Not only are aggressive children negatively affected by their behavior, but the cost to others and to society is also high. For example, victims of aggression suffer negative consequences including emotional, behavioral, and academic problems (Lopez & DuBois, 2005). Problems experienced by aggressors and victims lead to increased need for interventions and mental health services, incurring a substantial cost to society. Gaining a better understanding of the mechanisms that influence aggression is important for the development and improvement of interventions to prevent and/or decrease aggressive behavior among children. Improving interventions for aggression requires a better understanding of different types of aggression and their associated etiological, behavioral, cognitive, and social aspects.
Many researchers (e.g., Dodge, 1991; Dodge & Coie, 1987) have proposed that reactive and proactive aggression represent two distinct subtypes of aggression. Reactive aggression, also called angry, hot-blooded, or hostile aggression, is aggressive behavior in response to a provocation that is real or perceived. Proactive aggression, also called instrumental or cold- blooded aggression, is aggressive behavior that is directed toward the fulfillment of some instrumental or social goal. Although the utility of the distinction between these aggression subtypes has been questioned (Bushman & Anderson, 2001), the distinction between reactive and proactive aggression has been supported empirically across numerous studies (Connor et al., 2
2003; Crick & Dodge, 1996; Dodge & Coie, 1987; Dodge, Lochman, Harnish, Bates & Pettit,
1997; Poulin & Boivin, 1999; Price & Dodge, 1989) and has implications for the prevention and
treatment of aggression in children. Research that distinguishes reactive and proactive
aggression has focused primarily on etiological, contextual (i.e., family factors, peer
victimization), and individual (i.e., demographic variables, cognitions) variables. For example,
two contextual variables that have been identified as important predictors of reactive but not
proactive aggression are peer victimization (Poulin & Boivin, 2000) and family abuse (Dodge,
Lochman, Harnish, Bates & Pettit, 1997). Similarly, a body of literature has emerged which
identifies individual variables in both the affective (i.e., anger control) and social cognitive (e.g.,
hostile attribution bias, aggressive fantasy) domains as having different associations with
reactive and proactive aggression (Connor et al., 2004; Crick & Dodge, 1996; Dodge & Coie,
1987; Dodge, Lochman, Harnish, Bates & Pettit, 1997; Price & Dodge, 1989). For example,
greater anger reactivity is positively associated with reactive and not proactive aggression
(Hubbard et al., 2004), whereas positive outcome expectancies for aggression are positively
associated with proactive and not reactive aggression (Crick & Dodge, 1996). The present study
examined proactive and reactive aggression in a 5th grade, school-based, urban sample of
students and attempted to identify the differential relation of various contextual (i.e.,
victimization) and individual (i.e., anger control, social cognitive variables) variables in
predicting reactive and proactive aggression. This research considers aggression within the
context of a social-cognitive information processing model of aggression. Therefore, a
description of this model will be presented prior to a discussion of the reactive and proactive
subtypes of aggression.
Social Cognitive Information Processing Model of Aggression 3
The social cognitive information processing model (SCIP; Crick & Dodge, 1994;
Huesmann, 1988, 1998) suggests that aggressive behavior is supported by stable cognitive
processing patterns that are developed through environmental experiences, biological factors,
and specific social situations. Both Huesmann (1988) and Crick and Dodge (1994) proposed
social-cognitive information processing models and then Huesmann (1998) attempted to
integrate these models. Each of these models will be presented in the following section. The original Huesmann model is reviewed as context for his later integrated model and the Crick and
Dodge model is presented due to its centrality in the discussion of reactive and proactive
aggression. Finally, Huesmann’s integrated model will be presented as the model central to the
conceptualization of aggression in the current study.
Huesmann’s Original Social Cognitive Information Processing Model
Huesmann (1988) initially proposed a model of aggression that deemed social cognitive
processes as having a central role in the development and perpetuation of aggressive behavior.
His model emphasized the role of scripts, which are guides for behavior that include information
about social events including sequence, appropriate behavior, and potential outcomes. Scripts
are learned and reinforced through the observation and enactment of aggressive behavior.
According to Huesmann, social-cognitive information processes follow a five-step
sequence: individuals encounter a social problem, evaluate the environmental cues, search their
memory for a script to guide behavior, evaluate scripts one at a time until one is deemed
acceptable (i.e., considering what is acceptable socially and personally, what are the potential
outcomes, and their capability for enacting script), and then behave according to the script.
These steps are proposed to occur rapidly and automatically. This process is considered
common across individuals, and differences in individual behavior are a function of 4
interpretations, the contents of memory (i.e., available scripts acquired through past learning
experiences), the cognitive searching process, and the evaluation of scripts. Furthermore,
individuals’ emotional states (i.e., physiological and cognitive) can impact the process in many
ways, such as limiting their attention to social cues, influencing interpretations, or priming the retrieval of certain scripts.
The social-cognitive information processes in aggressive children are characterized by
particular types of processing. First, in the retrieval of scripts, angry arousal limits the attention
these children pay to social information and biases their interpretations toward perceived
hostility. These interpretations and angry arousal experienced by aggressive children bias script
selection such that scripts associated with angry affect and hostility are more likely to be
selected. Second, aggressive children encode more aggressive scripts than other children and
they engage in mental rehearsal (i.e., fantasy) of these scripts, which makes them more
accessible for retrieval. Third, aggressive children are more likely to have biased evaluation of
scripts that includes more concern for short-term (versus long-term) consequences, expecting
positive outcomes for aggressive behavior, higher self-efficacy for aggressive behavior (i.e.,
belief that they can successfully enact the behavior), and social and personal norms (i.e.,
normative beliefs) that are more supportive of aggressive behavior (e.g., “It’s okay to hit
somebody back if they hit you first”).
According to Huesmann, children continue to engage in aggressive behavior despite
negative environmental feedback for several reasons: the negative consequences might not be
salient and therefore go unnoticed, they lack the capacity to generate alternative prosocial
responses, they harbor belief systems that justify their aggressive behavior (e.g., “an eye for an 5 eye”), and/or they select social environments (e.g., peer groups) that support and reinforce aggressive behavior.
Crick and Dodge’s Model
Crick and Dodge also proposed a social cognitive information-processing model.
Whereas Huesmann paid particular attention to the role of scripts and normative beliefs in his model, Crick and Dodge tended to emphasize the role of perceptions and attributions in aggressive behavior. Nonetheless, both models have similar core structures. Crick and Dodge’s model is presented here because most of the reactive and proactive aggression research is based on Crick and Dodge’s model of social-cognitive information processing.
Crick and Dodge (1994; Dodge & Schwartz, 1997) described the processing of social information as taking place in six sequential, inter-related stages. Individuals can have
“competent” processing that leads to socially adaptive behavior or they can have biased processing that leads to problematic behavior. Specific deficits in processing (described shortly) lead to aggression and other maladaptive behaviors. Individual differences exist among children in terms of their “database,” which consists of memory storage, learned rules of behavior, social schemas (i.e., memory structures that organize information for efficiency), and social knowledge.
Aggressive children tend to have rules that are more supportive of aggression, and have access to more aggressive schemas. Experience with childhood abuse and encounters with aggression contribute to a database that is more likely to bias processing toward aggressive behavior. This database influences processing at each level described below.
First, within a social situation individuals must encode social cues into their memory.
The information encoded is influenced by individuals’ sensory capabilities and their selective attention to social cues. Children who are especially attentive to hostile cues will be primed to 6 respond to more social situations with aggression. Children’s emotional states are considered internal social cues that could be encoded. Aggressive children tend to encode fewer cues and their encoding is biased toward hostile cues.
In the second stage, children interpret the social cues they have encoded. In transferring the information encoded into short-term memory, children must create a mental representation of the situation that includes an attached meaning. Interpretation of meaning is based on a number of cognitive processes including causal attributions, attributions of the others’ intentions, and meaning of the situation in relation to self and others. These processes are influenced by the cognitive development of children and their biases. Emotion can be involved in this stage by influencing interpretations or it can be aroused by an interpretation, such as “that kid bumped into me on purpose.” Aggressive children tend to attribute hostile intent to ambiguous provocations.
Once the children interpret the event, they clarify their goals within the social situation.
Children can have instrumental goals involving desired access to some object or social goals involving some desired position or status. The goals selected are influenced by the children’s temperament/personality, situational circumstances, and emotional arousal. For example, children who tend to be angry are more likely to have hostile goals. Aggressive children tend to have more hostile goals and/or evaluate goals of control or domination more favorably than relational goals.
After clarifying goals, children access or construct potential responses to the social situation that include strategies for achieving the goal identified in the earlier stage. All children have learned behaviors that are stored in their long-term memories which potentially can be accessed in the current social situation. Alternatively, they might create a new response to the 7
situation. At this stage, emotions can influence the types of responses that are accessed and
emotions can result from the generation of different responses. Children who have learned many
aggressive responses will be more likely to select aggressive responses to social situations. In
general, aggressive children tend to generate fewer potential responses.
Next, children evaluate the responses and decide to enact a specific behavioral response.
In evaluating responses, children consider whether they can enact the response successfully (i.e.,
self efficacy), the potential outcome of the response, and whether the response is consistent with
their moral values. Children will decide to enact responses that they feel capable of enacting,
will lead to desired outcomes, and are consistent with their belief systems. This process could be
truncated and compromised if children have limited processing capacity (e.g., delayed cognitive
development, fatigue). Aggressive children tend to have more favorable evaluation of potential
outcomes for aggression, they minimize potential negative outcomes, and they have high self-
efficacy for aggression.
Finally, children enact the selected response strategy. Successful enactment is contingent
upon the motor and verbal skills of the children. Crick and Dodge (1994) suggested that children who lack the requisite skills to enact prosocial responses might resort to aggression as an alternative behavioral response. After enacting the behavioral response, the social information processing continues as children encode the social cues from the environmental response to their behavior.
Huesmann’s Integrated Model
Recognizing the common threads in Huesmann’s original model (1988) and Crick and
Dodge’s model (1994), Huesmann (1998) attempted to integrate the two models into a single model. His integrated model incorporates the unique emphases of the two original models (i.e., 8
the role scripts and beliefs in Huesmann’s original model and the role of perceptions and
attributions in Crick and Dodge’s model). In this integrated model, Huesmann (1998) describes social cognition as a “mediating process that connects external situations, internal schemas, and social behavior in predictable ways” (p. 84). The model breaks social cognitive information processing down into four stages, each of which represents a potential site for cognitive biases contributing to aggressive behavior.
In the first stage, cue attention and evaluation, children are confronted with a social situation and they attend to certain social cues and make interpretations about what those cues say about the current situation. Attention to cues is influenced by children’s biological predispositions, their emotional arousal or mood state, and their past learning experiences. For example, children who have an irritable mood state might attend to fewer cues, attend selectively to hostile cues, and interpret ambiguous cues as hostile. The children’s mood state influences their processing at this stage in a way that biases them to see the situation as hostile. Previous learning experiences also are an important influence at this stage. For example, children who have been exposed to high levels of violence (e.g., in the home, neighborhood media) might be more likely to attend to cues that signal potential danger, such as the concealed hand of a passing individual. Furthermore, they might have a biased interpretation and conclude that the concealed hand means the passerby has a weapon. Because they have seen a high frequency of violence, they are more likely to interpret new situations as hostile.
Attention and interpretation at the first stage of processing influence the next stage of processing, script and schema activation and retrieval. Scripts are collections of information about social situations, including potential behavioral responses and beliefs about the appropriateness of each, that function as guides for behavior. At this stage, children search their 9
“database” of scripts to identify potential sequences and responses to the social situation. The
database of scripts available at this stage will influence the potential responses generated by the
child. Aggressive children tend to have higher number of aggressive scripts available to them in
their “database” and their aggressive scripts are more easily accessible, meaning that they are
more likely to retrieve aggressive scripts than non-aggressive scripts. As in the first stage,
arousal and mood state also influence retrieval of scripts. For example, if children are angry,
they are more likely to retrieve scripts that are associated with angry affect even if the social cues
present in the situation do not support the selection of an aggressive script. Finally, the
children’s beliefs, including self perception and normative beliefs, serve as filters in the script
activation and selection process. Children who sees themselves as “not willing to take crap from
anybody” and who believe that “it’s okay to hit somebody who insults you” will be more likely
to retrieve a physical aggression script when friends call them a bad name.
Once children have identified a potential set of scripts for the situation, they evaluate the
activated and retrieved scripts. At this stage, children consider the appropriateness of different
scripts within the context of the social situation by determining their ability to behave as
proscribed by the script, anticipating the potential outcomes of the behavior, and evaluating the desirability of the potential outcomes for each script. Evaluation of scripts is influenced by the individuals’ previous experiences, affective states, and normative beliefs. Children who are angry or aroused tend to spend less time evaluating scripts and therefore make more impulsive decisions about their behavior without evaluating each option fully. Aggressive children are more likely to harbor normative beliefs that support aggression and therefore evaluate aggressive scripts more positively. Also, children with a history of aggressive behavior are more likely to have high self-efficacy for aggressive scripts, which increases the likelihood that they will 10 evaluate those scripts positively. Finally, more aggressive children tend to evaluate the potential outcomes of aggression as desirable and/or they fail to consider long-term negative consequences of aggressive behavior. Based upon this evaluation process, children select a script for behavioral enactment.
After children respond to the social situation behaviorally, there is a response from the environment. In the final stage in Huesmann’s model, children interpret the environmental responses. The response of the immediate social context to the children’s behavior is important, but its impact on the children’s future behavior (via schemas and moods) is mediated by children’s interpretation of that response. Children who can “explain away” a negative environmental response are less likely to interpret the negative response as meaningful. For example, children who get in trouble with a teacher but believe that the teacher has it out for them are less likely to encode this environmental response as important information that should alter their future behavior. Rather, they believe that the teacher will yell at them regardless of their behavior, so changing their behavior would not make a difference. It also is important to note that the responses from different components of the children’s social environment, such as their parents and their peer group, might have different influences on their behavior. For example, children who receive a negative response from teachers following aggressive behavior but receive positive responses from their peer group might place greater value on the peer response and therefore maintain their aggressive behavior in the future.
Theoretical Limitations
The social-cognitive information processing theories presented have several shortcomings that will be reviewed briefly. First, these models describe cognitive processes in a way that suggests they occur in a simple, organized, and sequential manner. However, in social 11
conflict situations thinking is complex, fast, and automatic. The model is considered as a useful
heuristic for understanding information processing. The processes described, however, are
proposed to occur rapidly. As described, the processes also seem to be linear and unidirectional.
However, information processing is expected to be cyclical and bi-directional. Crick and Dodge
(1994), for example, stated that there are multiple feedback loops that occur in processing social information.
In addition to theoretical limitations, research that supports social-cognitive information processing theory has important limitations as well. Much of this research uses measures that ask individuals to consider hypothetical situations and how they might think or respond in those situations. However, the processing of these situations in “real life” is automatic and not as reflective as measured in these studies. Therefore, the ecological validity of the research might
be limited. Finally, this research often focuses on the cognitive outputs involved in social
situations (e.g., beliefs) rather than the process itself. Thus, there is little empirical support for
the “process” part of the information processing models. These limitations should be kept in
mind as relevant to the research presented in this paper.
In light of this theoretical backdrop, the following sections will outline the aggression
subtypes. Specifically, the distinction between reactive and proactive aggression will be
reviewed in terms of their topography, prevalence, measurement, theory, and empirical research
regarding contextual, affective, and social cognitive correlates.
Aggression Subtypes
Topography
Dodge and Coie (1987) described two distinct forms of aggression: proactive and reactive. Reactive aggression, also called hostile aggression, is associated with the experience of 12 angry affect in response to real or perceived provocation. This type of aggression is consistent with the frustration theory of Berkowitz (1993) in that the behavior occurs in response to some type of provocation or frustration. The perceived provocation is the “push” toward aggression in the reactive form. On the other hand, proactive aggression is not associated with anger but rather is motivated toward the achievement of a particular goal, such as domination. This latter form of aggression is more consistent with social learning theory (Bandura, 1973) in which goals and anticipated reward, drive aggressive behavior. The anticipated benefit is the “pull” toward aggression in the proactive form.
Reactive aggression is described as angry, hot-blooded, and out of control and co-occurs with angry nonverbal gestures and facial expressions (Hubbard et al., 2002; Hubbard et al.,
2004). Autonomic arousal is positively associated with reactive aggression, which has been supported with research that shows a unique positive connection between reactive aggression and skin conductance reactivity (Hubbard et al., 2004). Alternatively, proactive aggression is described as a “cold-blooded” and controlled behavior that is less associated with autonomic arousal or angry nonverbal behaviors (Hubbard et al., 2002; Hubbard et al., 2004). It is important to note that these two forms of aggression are not mutually exclusive and are highly correlated with each other (i.e., r = .76; Dodge & Coie, 1987; Dodge & Coie, 1987). Therefore, children often display both types of aggression and at times it is difficult to identify whether an aggressive act should be considered reactive or proactive (Bushman & Anderson, 2001).
However, at extremes there are clinically-relevant differences across these forms of aggression in many domains including peer relationships, emotional control, social-cognitive patterns, and developmental trajectories. These differences will be reviewed briefly. 13
Perhaps related to their visible emotional arousal, reactively aggressive youth experience peer rejection and victimization moreso than do proactively aggressive children (Dodge et al.,
1997; Poulin & Boivin, 2000; Price & Dodge, 1989; Schwartz et al., 1998). Proactively aggressive children, on the other hand, are often evaluated positively by peers, such as being a leader or having a sense of humor (Dodge & Coie, 1987; Poulin & Boivin, 2000) and tend to be more accepted by their peers (Dodge et al., 1997; Dodge & Price, 1989; Poulin & Boivin, 2000), especially among those who are proactively aggressive themselves (Poulin & Boivin, 2000).
Potentially the covert and controlled nature of proactive aggression allows this behavior to go unnoticed among peers and therefore limit its effect on peers’ evaluation of proactively aggressive children. Positive peer evaluation is more clearly identified for children who exhibit proactive aggression that is instrumental in nature (i.e., to gain access to some material goal) than for children who exhibit proactive aggression that is bullying in nature (i.e., to achieve a social goal, such as power; Price & Dodge, 1989).
In terms of potential etiologies, reactive aggression has been shown to be positively correlated with childhood abuse and harsh discipline (Dodge, Lochman, Harnish, Bates & Pettit,
1997), whereas proactive aggression is positively associated with exposure to aggressive role models such as violence on television, in the community, or in their families (e.g., parents who verbally endorse and/or use aggression to obtain goals; Dodge, Lochman, Harnish, Bates, &
Pettit, 1997). Thus, the foundation for reactive and proactive aggression is, at least potentially, laid in the home. In general, reactive aggression has an age of onset (e.g., M = 4.4 years) that is approximately two years earlier than that of proactive aggression (e.g., M = 6.8 years; Dodge,
Lochman, Harnish, Bates, & Pettit, 1997). 14
Reactive aggression seems to be positively correlated with higher levels of attention problems and impulsivity (these correlates might play important roles in the social-cognitive information processing patterns of reactively aggressive children; Dodge, Lochman, Harnish,
Bates & Petit, 1997), internalizing difficulties such as depression (Vitaro, Brendgen, &
Tremblay, 2002), somatization and sleep disorders (Dodge, Lochman, Harnish, Bates & Pettit,
1997), and a greater risk for suicide (Conner, Duberstein, Conwell & Caine, 2003). Proactive aggression, on the other hand, seems to be positively associated with higher levels of delinquency (Vitaro, Brendgen & Tremblay, 2002; Vitaro, Gendreau, Tremblay, & Oligny,
1998), disruptive behaviors (Vitaro, Gendreau, Tremblay & Oligny, 1998), substance use disorders (Connor, Steingard, Cunningham, Anderson & Melloni, 2004), and higher levels of psychopathy in incarcerated adults (Cornell, Warren, Hawk, Stafford, Oram, & Pine, 1996).
In terms of gender differences, Connor, Steingard, Anderson, and Melloni (2003) conducted an investigation with 323 inpatient and outpatient clinically referred children and adolescents who ranged in age from 5 to 18 years old. The sample was unbalanced in terms of gender (i.e., 21% female). Reactive and proactive aggressive groups were defined based on a cutoff score based on the average teacher ratings of reactive and proactive aggression. These researchers found that proactive aggression among males and females had similar correlates (i.e., drug use, expressed hostility, and disruptive behavior disorder diagnosis). Boys’ proactive aggression also was correlated with parental violence and hyperactive/impulsive behavior, whereas girls’ proactive aggression also was correlated with psychotic disorder diagnosis and parental substance abuse problems. Fewer similarities across gender were present among the correlates of reactive aggression. Hyperactive and impulsive behaviors accounted for a large proportion of the variance (20%) in reactive aggression for boys, whereas drug use (1.3%), 15 hostility (7.9%), and victimization by an adult (2.1%) were less predictive of reactive aggression.
Girls’ reactive aggression was related largely to experiencing abuse at a young age, low verbal
IQ, and treatment with stimulant medication (23.2% of the variance collectively). These results do not fit neatly with the profiles of reactive and proactive aggression described earlier. For example, hostility was found to be a stronger predictor for proactive aggression in this study, though hostility would be expected to relate more to reactive aggression based on the definitions of the subtypes. These inconsistent findings are potentially a function of the sample and methods used by Connor and his colleagues. Specifically, the age range in the current study (i.e., 5 to 18) is broad and the researchers did not take into account differences across different age groups.
Second, the researchers did not control for the common variance between reactive and proactive aggression in their analyses. Given the high correlation typically seen between these subtypes, unexpected results might reflect this overlap.
Although the high correlation between reactive and proactive aggression can complicate research and clinical intervention with aggressive children (Dodge, 1991; Day, Bream & Pal,
1992), the distinction between the subtypes at extremes is marked and has important implications for diagnosis (Connor, Steingard, Anderson & Melloni, 2003; Vitaro, Brendgen & Tremblay,
2002; Vitaro, Gendreau, Tremblay, & Oligny, 1998; Waschbusch, Porter, Carrey, Kaxmi, Roach
& D’Amico, 2004) and treatment (Connor, Steingard, Anderson & Melloni, 2003; Dodge &
Coie, 1987; Little, Brauner, Jones, Nock, & Hawley, 2003; McAdams, 2002; Waschbusch,
Porter, Carrey, Kaxmi, Roach & D’Amico, 2004). The different experiential, affective, and cognitive associations identified in exclusively reactive and proactive aggressive children suggest that there are different mechanisms contributing to aggression in these groups, therefore implicating different means of identification and treatment for these types of aggression (Crick & 16
Dodge, 1996). In the interest of addressing the full spectrum of aggressive behavior in children, then, it is important to understand the subtypes and their social-cognitive underpinnings.
Measuring Reactive and Proactive Aggression
Reactive and proactive subtypes of aggression are measured primarily using teacher report, though direct observation, parent report, staff-report, peer nomination, and self report have also been used. Dodge and Coie (1987) developed a teacher-report measure that consists of six items, three that assess reactive (e.g., “when this child has been teased or threatened, he or she gets angry easily and strikes back”) and three that assess proactive (e.g., “this child uses physical force to dominate other kids”) aggression. The researchers found a high degree of correlation between the subtypes (r = .76). A principal-components factor analysis was used to support the two-factor nature of the scale. However, the eigenvalue for the proactive aggression factor was less than 1 (e.g., .74) in the initial and in subsequent analyses. Dodge and Coie
(1987) suggest that the factor analysis results were weak because teachers tend to see aggression as unidimensional which leads to undistinguished reporting of both reactive and proactive aggression for children who fit that broader “aggressive” category. They concluded that, though the factor support was weak, the items fell as expected into two fairly distinct factors that were consistent with theory and with additional research the discriminant validity of the subtypes could be supported. Additional concerns regarding this scale include potential teacher bias based upon the influence of aggression-unrelated student factors such as attractiveness, reputation, or academic performance. It has also been suggested that the Dodge and Coie scale measures proactive-bullying aggression and not proactive-instrumental aggression (Price & Dodge, 1989).
Though there are important concerns about Dodge and Coie’s measure, support for the construct validity of the scale has been gathered and will be reviewed below. 17
A second teacher-report scale was developed by Brown, Atkins, Osborne, and Milnamow
(1996) consisting of 21 total items with 10 proactive aggression items (e.g., “plays mean tricks”), six reactive aggression items (e.g., “mad when doesn’t get his way”), and five unclassified items
(e.g., “needs to be the leader”). This scale had somewhat better psychometric properties than
that developed by Dodge and Coie, but the Dodge and Coie scale (1987) is most commonly used
in the literature.
Used less often, parent reports of reactive and proactive aggression have typically
consisted of adapted teacher-rating forms, such as that developed by Coie and Dodge (1987;
Connor, Steingard, Anderson & Melloni). Similarly, Marcus and Kramer (2001) adapted Brown
et al.’s (1996) scale for use with parents. Adapted teacher scales also have been used for staff-
report when youth resided in a mental health or juvenile detention facility (e.g., Marcus &
Kramer, 2001). Other types of measurement, including direct observation (Dodge & Coie,
1987), peer nomination (Prinstein & Cillessen, 2003), self report (Boxer, Tisak & Goldstein,
2004; Little, Brauner, Jones, Nock, & Hawley, 2003), and classification based on past behavior
recorded in psychiatric charts (Dodge, Lochman, Harnish, Bates, & Pettit, 1997) are less common.
Dodge and Coie’s teacher-rating scale, though not without limitations, is the most commonly used measure of reactive and proactive aggression. In terms of the scale’s length,
brevity is a potential liability, but also is a strength in that it is practical for teachers to complete
in a short amount of time. For these reasons, the Dodge and Coie (1989) scale was selected and
used for the current study.
Prevalence of Reactive and Proactive Aggression 18
Reactive aggression is generally more prevalent than proactive aggression in community and clinical samples (Connor, Steingard, Anderson, & Melloni, 2003; Day, Bream & Pal, 1992;
Dodge & Coie, 1987; Dodge, Lochman, Harnish, Bates, & Pettit, 1997). For example, in a community sample, using the Dodge and Coie scale (1987), Dodge et al. (1997) identified reactive, proactive, and pervasive (i.e., reactive and proactive) subgroups using cutoff scores of one standard deviation above the mean. Whereas most children were non-aggressive (79%), the pervasively aggressive type was most prevalent aggressive group (11%), and reactive aggression
(7%) was more prevalent than proactive aggression (3%). Similarly, Connor and his colleagues
(2003) reported that in a sample of clinically-referred children and adolescents, reactive aggression (58.7% males, 55.9% females) was more prevalent than proactive aggression (19% males, 20.6% females). Day, Bream, and Pal (1992) suggested that higher teacher-reports of reactive aggression versus proactive aggression could be a function of the behavioral display of inappropriate, hostile behaviors in reactive aggression. Certainly the nature of reactive aggression suggests a greater visibility of this subtype in comparison with proactive aggression.
Defining reactive aggression as impulsive, relieving negative emotion (i.e., frustration, anxiety, fear), associated with remorse, and emotionally driven, and proactive aggression as calculated, a tool for gain, without remorse, and intellectually driven (i.e., planful), McAdams
(2002) investigated the prevalence of these subtypes across school and clinical settings. He surveyed school administrators and clinical service providers who reported that, since the start of their careers, they perceived an increase in aggression overall with a significant increase in the relative proportion of proactive aggression incidents. This perceived increase was reported across all settings surveyed, including elementary, middle, and high schools as well as clinical settings. The greatest perceived increase was found in the elementary schools where proactive 19 aggression incidence almost tripled. Proactive aggression represented 21.6% of reported aggressive incidents in elementary schools, 22.7% in middle schools, 28.9% in high schools, and
26.4% in clinical settings. McAdams (2002) concluded that the presence of similar trends for reactive and proactive aggression in school and clinical populations suggests that clinical populations are not driving the increase in aggression among youth, but rather the increase in proactive aggression seems to be a broader phenomenon. In considering these results, however, it is important to keep in mind the definition of reactive and proactive aggression, which differs slightly from other researchers’ definitions (e.g., Dodge and Coie’s measure does not specify reactive aggression as associated with remorse). Furthermore, reports made by participants in this study were retrospective, leaving more room for bias and error in reporting. Connor and his colleagues (2004) also investigated reactive and proactive aggression among pediatric psychopharmacology outpatients and youth referred to a residential treatment center and, based on parent/legal guardian and/or staff-report using the Dodge and Coie (1987) scale, found higher prevalence of proactive aggression among the inpatients as compared to the outpatients in the study.
Proactive aggression has been reported to be more prevalent among older youth and reactive more prevalent among younger youth, though in recent years there seems to have been an increase of proactive aggression among the younger group (McAdams, 2002). Higher levels of reactive aggression were found among younger children from a clinically-referred sample in a study conducted by Connor and his colleagues (2004), but proactive aggression was equally prevalent among younger and older children. Dodge and Coie (1987) suggested that age differences potentially reflect differences in levels of cognitive development which impact social-cognitive processes. 20
Those professionals surveyed by McAdams (2002) reported no significant gender differences for the prevalence of reactive and proactive aggression. In agreement, Connor and his colleagues (2003) investigated gender differences in reactive and proactive aggression among a clinically-referred sample and also found no differences in prevalence.
Theory of Social Cognitions in Reactive and Proactive Aggression
Huesmann (1998) suggested that the same social-cognitive processes mediate aggression whether it is reactive or proactive, though there are differences across the subtypes in terms of associated characteristics, such as anger. He described the involvement of anger in aggression as falling along a continuum, with reactive aggression falling at the high end and proactive at the low end of anger. Along these lines, Huesmann described certain tendencies that are exhibited by children who qualify as reactively aggressive or proactively aggressive. Reactively aggressive children are more emotionally reactive in response to provocation, they exhibit higher levels of hostile attribution bias, and as a result are more impulsively aggressive in response to provocation. Proactively aggressive children, on the other hand, have a greater number of aggressive scripts and have more normative beliefs that support aggressive behavior.
Furthermore, these children are potentially less susceptible to arousal, whereas reactively aggressive children would be expected to have higher levels of arousal in general.
Consistent with Huesmann, Dodge (1991; Dodge & Schwartz, 1997) described that the differences between proactive and reactive aggression relate to biased processing at different stages in the social information processing model, with reactively aggressive children having more deficits earlier in the processing model and proactively aggression children having more deficits later in the processing model. Specifically, reactive aggression results from biases in the intention-cue interpretation process. These children attend to fewer cues, are extra sensitive to 21
hostile cues, and are biased toward unwarranted hostile interpretations, which lead to aggressive
behaviors. Proactively aggressive children, on the other hand, can accurately attend to and
interpret social cues but they exhibit biases at later stages of processing. They tend to have fewer
response options available in their database and they are biased toward evaluations that include
positive efficacy for aggression and positive outcome expectations for aggressive behavior.
These theoretical assertions have been supported by several studies. The following section will
review a portion of this research including evidence that differentiates the contextual, affective,
and social-cognitive variables associated with reactive and proactive aggression.
Empirical Evidence for the Role of Contextual, Affective, and Social Cognitive Variables
Several contextual, affective and social cognitive variables have been found to
distinguish proactive and reactive aggression. The following section describes these variables and reviews the empirical evidence that relates each variable to the aggression subtypes. These variables contribute to unique profiles of reactive and proactive aggression.
Contextual Variables
Peer victimization. Reactive aggression is found more frequently in children who have been victimized. Schwartz and his colleagues (1998) investigated the relation between peer victimization and the aggression subtypes among third-grade African-American boys.
Playgroups consisting of four aggressive dyads met for five videotaped sessions and their behavior was rated for reactive aggression, proactive aggression, and victimization.
Victimization was correlated significantly with reactive aggression (r = .53, p = .0005) but not
with proactive aggression (r = -.07, p > .05). The researchers noted that, due to the correlational
nature of their study, they could not determine the direction of causation. Therefore, one of two
possibilities exists: either reactively aggressive children are evaluated more negatively by peers 22
and are therefore victimized more frequently or children who are victimized have more
opportunities to retaliate aggressively and are therefore higher in reactive aggression.
In a rural sample of fifth-grade boys and girls, Pellegrini, Bartini, and Brooks (1999)
found that self-reported peer victimization had a significant positive correlation with teacher-
rated reactive aggression and a non-significant (but positive) correlation with teacher-rated
proactive aggression. On the other hand, proactive aggression was positively related to peer
nominations of popularity within a sub-group of bullies but was related negatively to peer nominated popularity within a sub-group of victims. Furthermore, among victims, higher levels of reactive aggression related to higher levels of within-group victimization. This study supports
the notion that reactively aggressive children are victimized by their peers, whereas proactively
aggressive children are evaluated positively within their peer groups.
In a longitudinal study conducted in the Netherlands, Camodeca and her colleagues
(2002) investigated the relation between bullying, victimization, and proactive and reactive
aggression. The authors surveyed 215 third and fourth grade boys and girls at two time points
one year apart. Self-rated bullying behavior at the second time point was related to teacher-rated
proactive and reactive aggression, whereas victimization was related to reactive aggression
exclusively. This study supports the notion that reactive and not proactive aggression is
associated with peer victimization.
Peer rejection can be considered one form of victimization. Studies also have shown that
reactive aggression is associated with peer rejection. For example, Price and Dodge (1989)
investigated the relation of peer status to proactive and reactive aggression among boys in
kindergarten and first grade. Teacher ratings of proactive and reactive aggression were
compared to peer ratings of social preference and behavioral descriptions (e.g., starts fights) as 23
well as observer-rated behavior during play including reactive aggression, proactive-instrumental
aggression, and proactive-bullying aggression for children and their peers. Teacher-rated
reactive aggression was associated negatively with social preference ratings and perception as caring about peers and it was positively associated with peer ratings of “starts fights” and “gets angry.” In contrast, observer-rated proactive-instrumental aggression was related positively to
social preference and positively related to peer ratings. Therefore, children who are reactively
aggressive are more likely to be socially rejected and to be disliked by peers, whereas
proactively-instrumental aggressive children are perceived by peers as leaders and receive more positive peer evaluations. Due to the cross-sectional nature of the study, Price and Dodge were unable to establish whether the experience of peer rejection led to reactive aggression in children or if the reactive aggression led to peer rejection. In contrast to the expectation that reactively aggressive children are more likely to be victimized by their peers, Price and Dodge found no relation between reactive aggression and peer victimization in their study.
In a longitudinal study with an adolescent sample, Prinstein and Cillessen (2003) found that peer-rated reputational reactive aggression, a form of reactive aggression in which an adolescent responds to provocation by attempting to damage the provocateur’s social reputation, at Time 1 significantly predicted low social preference at Time 2 after controlling for social preference at Time 1. In this case, it appears that reactive aggression contributed to the development of negative peer evaluations across time. These researchers further found that proactive-instrumental overt aggression was associated with peer-rated popularity, whereas reactive overt aggression was associated with low popularity and peer preference (Prinstein &
Cillessen, 2003). 24
Family context and abuse. Research also has shown that children who are reactively
aggressive are more likely than children who are proactively aggressive children to have a
history of familial abuse, whereas proactively aggressive children are more likely to be exposed to aggressive role models (not directed toward them). In a four-year longitudinal study Dodge,
Harnish, Lochman, Bates, and Petit (1997) examined the developmental histories of 504 children in a community sample, following children from kindergarten through 4th grade. The authors
classified the participants into proactive, reactive, pervasively aggressive (i.e., both proactive and
reactive), and non-aggressive groups based on teachers’ ratings of their behavior at year 4.
Interviews with the participants’ mothers at the beginning of the study (i.e., year 1) revealed that
children in the reactive aggression group and the pervasively aggressive group had experienced
higher levels of physical abuse and harsh discipline in comparison with the other groups.
Specifically, 41% of children who had experienced abuse before they turned 5 years of age were
classified as reactively aggressive by third grade in comparison with non-abused children of
which only 15% came to be classified as reactively aggressive. In contrast, none of the children
classified as proactively aggressive had reported experiences with physical abuse. In fact, proactively aggressive children did not differ from non-aggressive children in terms of their developmental histories. Reactively aggressive children also tended to have an earlier onset of problems than the proactively aggressive children (i.e., behavior problems were greater for reactively than proactively aggressive children in year 1 but not in subsequent years) and they
experienced more peer rejection than proactively aggressive and non-aggressive children. An
earlier age of onset for reactive aggression was also found when the researchers extended their investigation in a clinical sample (i.e., age of onset for reactive aggression M = 4.4, SD = 2.8; proactive aggression M = 6.8, SD = 3.3). Therefore, it appears that early experiences of abuse 25
are related to reactive aggression and potentially contribute to the earlier onset of this subtype of
aggression relative to proactive aggression.
Dodge et al. (1997) also examined similar variables in a clinical sample of 50 boys (mean
age = 12.7, 62% African American) who were participating in a treatment program for children with severe behavior problems and had a history of treatment with inadequate services. In this sample, case reviews revealed that proactively aggressive children had more exposure to
aggressive role models than did reactively aggressive children (though the specifics regarding these role models were not described). Finally, Connor and his colleagues (2003) investigated family histories of reactively and proactively aggressive children in a clinical sample and found that the experience of abuse was predictive of reactive but not proactive aggression.
Although the research in this domain is more limited, there seems to be support for the differentiation of reactive and proactive aggression based upon family victimization, with reactively aggressive children experiencing more abuse and adult victimization than proactively aggressive children. However, methodological issues such as the use of retrospective reports or case reviews somewhat limit confidence in the validity of these findings. In addition to these contextual variables, research also demonstrates a distinction between the affective variables
associated with reactive and proactive aggression.
Affective Individual Variables
Anger control. Reactive aggression is associated with higher angry affect and lower
levels of anger control. Although anger is central to the definition of reactive aggression and its
distinction from proactive aggression, few studies have examined the relation of anger with the
subtypes. Hubbard and her colleagues (2002) investigated the association of anger measured by
self-report, facial expressions and nonverbal behaviors, and physiological indicators (i.e., skin 26
conductance reactivity, heart rate reactivity) with teacher-rated reactive and proactive aggression
in a second-grade sample. Anger measures were collected in relation to a competitive game
procedure in which participants lost a game to a confederate peer who engaged in blatant unfair
play behavior. Angry nonverbal behaviors were found to be related to reactive aggression and
not proactive aggression. Furthermore, children who were reactively aggressive had sharper
increases in skin conductance reactivity and angry nonverbal behaviors over the course of the
game than did other children. These results suggest that reactively aggressive children are more
easily aroused to anger than are other children. Though theoretically consistent, these findings
were not robust. The regression models, which included reactive aggression, accounted for no
more than 8% of the anger variance. Inconsistent with expectations, children’s self-reported anger increased more rapidly in proactively aggressive children than in reactively aggressive children.
In a separate analysis, Hubbard and her colleagues (2004) reported that, to a moderate degree, different anger measures (e.g., skin conductance reactivity, angry nonverbal behavior) were more closely related to each other for reactively aggressive children than for proactively aggressive children. This finding suggests a more cohesive, and possibly more powerful, experience of anger for reactively aggression children. Hubbard et al. (2004) speculated that the increased anger response in reactively aggressive children could account for some of the social rejection and victimization they experience because their more noticeable response to provocation is potentially more rewarding for the provocateur.
Research supporting anger’s association with reactive aggression is limited. These two studies came out of the same laboratory and used the same dataset. The sample was limited to second grade children and therefore might not reflect general tendencies but rather those of this 27
particular sample. Furthermore, the lab design of the study potentially limits the external validity
of these results. Children might behave very differently when they are playing a familiar game
in a familiar setting in comparison with their lab experience. Finally, only some of the anger
indices studied were found to relate as expected to the subtypes (and expected results were not
robust). Although this research provides initial support for anger’s positive relation with reactive
and not proactive aggression, findings are somewhat inconsistent and should be considered tentative. Beyond anger, a substantial portion of the research that differentiates reactive and
proactive aggression has focused on social-cognitive variables, which will be reviewed in the
following section.
Social Cognitive Individual Variables
Hostile attribution bias. Children with hostile attribution bias tend to interpret
ambiguous situations as indicating hostile intent. For example, if a peer bumps into a child who
has a hostile attribution bias, the child will be more likely to interpret the peer’s intent as hostile
even in the absence of evidence of hostility. Crick and Dodge (1996) proposed that reactive
aggression is maintained by a three-part self-fulfilling prophecy: first, children are biased toward
hostile attributions and retaliate aggressively in response to a benign provocation; second, their peers develop increased hostility toward them; and third, the peer response confirms the children’s initial interpretation that others are hostile toward them. Several studies have supported the notion that reactively aggressive children exhibit hostile attribution biases moreso than do proactively aggressive children.
In a series of two studies, Dodge and Coie (1987) investigated the role of hostile attribution biases in relation to reactive and proactive aggression. First, to evaluate the validity of their teacher-report scale for reactive and proactive aggression, the authors administered the 28 scale to African American boys in first and third grade. Based on peer ratings, the authors identified subgroups within this sample of socially rejected (n = 56) and socially average (n =
58) boys. Teacher ratings of reactive and proactive aggression were obtained for both groups of children and these ratings determined the rejected boys’ placement in one of four subgroups: reactive (not proactive) aggressive (n = 6), proactive (not reactive) aggressive (n = 7), pervasive
(proactive and reactive) aggressive (n = 18), and non-aggressive (n = 25) children. Group membership was defined based on a median-split on teacher-rated proactive and reactive aggression (e.g., those above the median for proactive and below the median for reactive were classified as “proactive aggressive”). The researchers also obtained measures of the children’s intention-cue detection based on their response to 12 videotaped vignettes that portrayed hostile, accidental, ambiguous, or prosocial interpersonal provocations. Analyses of variance revealed that reactively aggressive children had greater deficits in interpreting non-hostile intentions
(though they were able to interpret accurately hostile intentions), they tended to make errors of presumed hostility, they interpreted ambiguous provocations as hostile, and they tended to select aggressive responses in response to ambiguous and hostile situations. These tendencies were not seen among proactively aggressive or nonaggressive children.
Some limitations in this study should be considered in relation to these results. First, aggressive responses were defined in this study as “get angry at the peer” and “tell the teacher to punish the peer.” More obvious aggressive responses (e.g., hit, yell) were not offered as response options. These response options are clearly limited and should temper conclusions that reactively aggressive children were more likely to endorse “aggressive” responses. Second, the small sample size with narrow representation (i.e., socially rejected, African American males from one Southern city) raises concerns regarding the external validity of this study. 29
In a second study, Dodge and Coie (1987) investigated the relation of teacher-rated and
observer-rated reactive and proactive aggression, and the observer ratings’ relations to
attributional biases and deficits. The sample included 144 African American first and third grade
boys who were divided into 24 playgroups with 6 children in each. A measure of attributional
biases was similar to that used in the previous study, and observer ratings of reactive and
proactive aggression included a coding system that identified reactive and proactive behaviors.
The researchers found that the number of hostile attributions and the number of errors of
presumed hostility made by the children correlated significantly with observed levels of reactive
aggression and did not correlate significantly with observed levels of proactive aggression. The
authors concluded that hostile attribution biases are key mechanisms that influence reactive
aggressive behavior.
In additional research, Crick and Dodge (1996) attempted to distinguish proactive and
reactive aggression in terms of the social-information processing patterns (i.e., hostile attribution
biases and positive evaluations of aggression) associated with each, using a sample of 624 third
through sixth grade boys and girls. The authors obtained teacher ratings of reactive and
proactive aggression and assessed social-information processing patterns using self-report
measures designed to capture attributional biases, outcome expectancies, and self efficacy for
behavioral strategies. Analyses of variance showed that for older children only (i.e., 5th/6th
grade), the reactive group made significantly more hostile attributions than the non aggressive
group and they also made more hostile attributions than the proactive group, but this difference was not statistically significant. Although the pattern of results found in the older sample (i.e.,
5th/6th graders) was somewhat consistent with the hostile attribution hypothesis, it is not wholly consistent with the results reported by Dodge and Coie (1987) in that Crick and Dodge (1996) 30
failed to find significant difference in hostile attribution biases between reactive and proactive
groups. Differences across these studies, including gender and ethnic differences across samples
(i.e., Dodge and Coie’s study included only African American boys), or different measures of
hostile attribution biases, could account for the different results. The researchers also found
evidence for a unique pattern of social-cognitions associated with proactive aggression and
positive evaluations of aggression. This finding will be discussed below in combination with
other empirical research regarding positive evaluations of aggression.
As discussed earlier, Dodge, Lochman, Harnish, Pettit, and Bates (1997) investigated
differences in the “profiles” of reactively and proactively aggressive children from “normal” and
clinical samples. In addition to the developmental history factors outlined above, the authors
also examined differences in processing of information. Using a self-report instrument, the
researchers assessed attention to relevant cues, behavioral responses, social problem solving, and
hostile attributional biases. In the normal sample, the pervasively aggressive children exhibited
more encoding errors than the proactive aggressive and the non-aggressive groups. In the
clinical sample, the reactively aggressive children exhibited more deficits in encoding relevant
social cues. The researchers did not find a greater tendency for reactively aggressive children to
over-attribute hostile intent. Therefore, this study only lends partial support for the hostile
attribution bias in that it shows there are processing errors but not errors of interpretation that are
biased toward hostility. A study using a sample of juvenile delinquent males identified a link
between hostile attribution bias with reactive but not proactive aggression, however (Dodge,
Price, Bacharowski, & Newman, 1980).
Overall, although several studies have supported the conclusion that reactively aggressive children exhibit more hostile attribution biases than proactively aggressive children, empirical 31
research results have not uniformly supported that prediction. Differences in samples and
methodology could account for some of these differences. For example, studies that used
videotaped vignettes to measure hostile attribution bias tended to support the expected positive
relation between reactive aggression and hostile attribution bias, whereas those that used
questionnaire vignettes did not. The videotaped vignettes are more similar to “real life”
experiences and therefore might better elicit hostile attribution biases than vignettes read from a
questionnaire. Also, studies that included limited samples (e.g., socially rejected African
American boys, delinquent youth) tended to support the expected hostile attribution bias/reactive
aggression relation in comparison with those studies that used more diverse community samples.
The samples used in the former studies potentially represent groups that have developed stronger links between their reactive aggressive behavior and hostile attribution biases through their experiences (i.e., rejection by peers, delinquent behavior).
Despite the inconsistent results, however, theory and research suggest that reactive
aggression is associated with hostile attribution biases. It is expected that the current study will
demonstrate a stronger positive relation of hostile attribution bias with reactive than proactive
aggression.
Outcome expectancies. Whereas hostile attribution bias has more consistently correlated
with reactive aggression, proactive aggression has been shown to correlate more consistently
with positive outcome expectancies for aggression (Crick & Dodge, 1996; Smithmyer, Hubbard,
& Simons, 2000).
In their initial research differentiating reactive and proactive aggression, Crick and Dodge
(1996) assessed participants’ outcome expectancies in response to four vignettes describing peer
group entry or peer conflict situations. Participants were asked to evaluate two strategies (i.e., 32 verbal aggression, physical aggression) for each situation and then they were asked evaluate two potential outcomes for each situation (i.e., instrumental in which the behavior will/will not satisfy an instrumental goal, or relational in which the behavior will/will not lead to positive evaluation by the peer), rate whether the outcome would be positive or negative, indicate how likely the outcome was to occur, and indicate a preference for a positive instrumental or relational outcome. More than other groups, children in the proactive aggression group reported more positive outcome expectations for aggressive strategies in conflict situations and they tended to prefer instrumental to relational goals. The authors concluded that proactively aggressive children have a unique response decision process that is mediated by a tendency to expect positive outcomes for aggressive behavior and is more likely to lead to instrumental aggression. Furthermore, because proactively aggressive children prefer instrumental to relational goals, their goal clarification process also supports proactive aggressive behavior.
Specifically, their aggression is driven by a motivation toward achieving an instrumental goal and is less likely to be inhibited by concerns about the potential negative social implications of aggression.
In their research with a non-clinical population, Dodge, Lochman, Harnish, Pettit, and
Bates (1997) presented participants with social problems and asked them to choose one of three behavioral responses (i.e., aggressive, assertive, passive) and rate how they would feel if they tried the response. Discriminant function analyses revealed that proactively aggressive children anticipated more positive feelings after aggressive responses than did the other groups.
Similarly, in an extension of the study with a clinical population, the researchers assessed outcome expectations for aggression using a modified Outcome Expectations Questionnaire.
Participants imagined themselves enacting a peer-directed behavior (i.e., aggression, non- 33
aggressive direct action, non-aggressive verbal assertion) in each of 12 vignettes and then rated
the likelihood that specific outcomes would result (i.e., tangible rewards, reduction in aversive
treatment). Multivariate analyses of variance revealed that proactively aggressive children were
more likely than other groups to anticipate positive outcomes from aggressive behaviors.
Specifically, they had greater expectation that aggressive behavior toward a peer would lead to a reduction in the aversive behavior by the peer. Therefore, as expected, proactively aggressive children demonstrated tendencies to anticipate positive outcomes from aggressive behavior in both clinical and normal samples. Similar results have been found in a sample of boys from a correctional facility (Smithmyer, Hubbard & Simons, 2000). Also, other research has found that proactively aggressive children, but not reactively aggressive children, have non-normative
emotion expectancies in both aggressive and non-aggressive situations (e.g., anticipating
happiness following a proactively aggressive behavior). In this research, expecting happiness
after enacting proactive aggression was a significant predictor of externalizing problems.
(Arsenio, Gold, & Adams, 2004).
Some age differences have been found in relation to outcome expectancies and reactive
and proactive aggression. For example, in the study described earlier, Crick and Dodge (1996)
found that for group-entry situations, younger (i.e., 3rd/4th graders) children who were proactively
aggressive had more positive outcome expectancies for aggressive strategies than did older
children (i.e., 5th/6th graders). Also, older children were more likely than younger children to
prefer relational goals to instrumental goals in response to group-entry situations. Younger
proactively aggressive children reported more positive outcome expectancies for aggression than
did older children, potentially attributable to developmental changes in information processing
(Crick & Dodge, 1996). 34
It is important to keep in mind that proactively aggressive children presumably use this
form of aggression because they have found it to be an effective strategy. To the extent that
proactively aggressive youth have used proactive aggression successfully, positive outcome
expectancies for proactive aggression are accurate for these individuals. These expectations,
though accurate, are potentially myopic in that other potential negative, long-term consequences
might also occur. Research has not explored the presence and influence of multiple outcome
expectations on aggressive behavior. Children might be aware of long-term, negative
consequences such as peer mistrust, but they might place more weight on the immediate outcome
(e.g., gaining access to a toy) and therefore use proactive aggression for the immediate gain.
These more complex considerations need to be explored in future research.
In summary, empirical research seems to support uniformly the notion that children who
are proactively aggressive have more positive outcome expectancies for aggression than do
reactively aggressive children. Musher-Eizenman et al. (2004) suggested that outcome
expectancies play a role in the response evaluation step of the social-cognitive information
processing model. Therefore, these findings are consistent with theory, which asserts that
proactive aggression should be associated with unique processing in the latter stages of the social cognitive information processing model. Negative self evaluations represent a form of outcome expectancy in that the child is expecting to have negative emotions following an aggressive act.
This type of social cognition will be discussed in the following section.
Negative self evaluation. Negative self-evaluation, a construct based upon Egan et al.,’s
(1998) value of victim suffering, refers to the beliefs children have about how upset they would
feel if they were to perform an act of aggression that inflicted some form of suffering on a
victim. Neither construct has been researched in relation to reactive and proactive aggression. 35
However, in a longitudinal study, Egan and her colleagues (1998) investigated the influence of the value of victim suffering and victimization on aggressive behavior in boys and girls. Value of victim suffering in Egan et al.,’s study indicated how much participants expected they would care if a victim showed signs of suffering (e.g., crying) after being aggressed upon by the participant (i.e., higher scores indicated less concern). The authors found that among boys, greater value of victim suffering predicted decreases in aggression among those children who had experienced the most victimization (i.e., 1 SD above the mean for boys the sample). Among girls, however, the opposite result emerged. That is, for girls who had been victimized most, greater value of victim suffering was predictive of increased aggression. The authors speculated that the differences across genders potentially reflected the punishment value of victimization.
Specifically, the authors suggested that boys’ aggression decreased because the victimization they experienced was more punishing and therefore led to a decrease in their aggressive behavior. Girls, on the other hand, experienced victimization as less punishing and therefore were not compelled to decrease aggression. Instead, their experiences with victimization incited aggressive behavior.
Although the explanation provided by Egan et al. is plausible, an alternative explanation for this finding is that the researchers were not measuring proactive and reactive aggression separately and their methods were insensitive to the potential differences in patterns of relation of aggression subtypes to the value of victim suffering. Perhaps boys displayed more proactive aggression, which would be expected to have no correlation or an inverse correlation with victimization, whereas girls displayed more reactive aggression, which correlates positively with victimization. Consistent with this possibility, the authors note that the social cognitions 36
measured in their study (e.g., value of victim suffering) might relate more to proactive than
reactive aggression.
As discussed above, negative self evaluation can be considered a type of outcome
expectancy and other types of outcome expectancies have been positively associated with
proactive more than reactive aggression. Therefore, in the current study it is expected that low
negative self evaluation will predict higher levels of proactive but not reactive aggression. That
is, higher levels of proactive aggression will be associated with lower anticipated negative self
evaluation following aggression.
Retaliation beliefs. Retaliation beliefs are situation-specific normative beliefs about the
acceptability of different behaviors in response to provocation (Huesmann & Guerra, 1997).
Musher-Eizenman et al. (2004) described these beliefs as playing a role in the response
evaluation stage of social cognitive information processing. At this point, retaliation beliefs have
not been empirically examined in relation to reactive and proactive aggression.
Given theoretical assertions that proactively aggressive children exhibit processing biases
in the later stages of information processing (Dodge, Lochman, Harnish, Bates, & Pettit, 1997), proactively aggressive children would be expected to have higher levels of retaliation approval
which contributes to their aggressive behavior by influencing their response evaluation stage of
processing. Reactively aggressive children, on the other hand, behave aggressively in an
impulsive and emotional way, suggesting that they do not refer to their normative beliefs before
aggressing. Therefore, reactive aggression is less likely to be associated with retaliation beliefs.
This conclusion is not necessarily intuitive, however. A complicating factor in
determining the influence of retaliation beliefs on the subtypes of aggression is the retaliatory
nature of reactive aggression. If reactively aggressive children are more likely to aggress in 37
response to some provocation or, in other words, to retaliate, are these children likely to have
beliefs that this type of retaliation is justified? Certainly among reactively aggressive children,
past experiences in retaliation could contribute to a belief system that supports their retaliatory
behavior. Furthermore, reactively aggressive children have more victimization experiences.
These children might feel justified, then, in retaliatory aggression. Based on this logic,
retaliation beliefs would be expected to have a positive relation with reactive aggression and not
proactive aggression. In contrast with this possibility, Egan and her colleagues (1998) suggested
that the aggressive beliefs would not have an influence on the aggressive behavior of habitually victimized children because their victimization experiences have taught them that aggression does not work for them. Based upon this logic, given that reactively aggressive children are
more likely to have experienced victimization, Egan’s claim would suggest that their retaliation
beliefs might have less influence on their behavior in comparison with proactively aggressive
children who are less likely to be victimized.
In summary, there is no empirical basis upon which to formulate a hypothesis about how
retaliation beliefs will relate to reactive and proactive aggression. Theory, however limited,
suggests that retaliation beliefs should relate positively to proactive aggression, but that relation
does not fit with logical considerations of the retaliatory nature of reactive aggression as
discussed above. Despite these logical inconsistencies, theory will be used to guide the current
hypothesis. Therefore, based on theory that reactive aggression is associated with deficits in the
earlier stages of processing and the expected influence of retaliation beliefs on the response evaluation stage of processing (i.e., stage 3), it is expected that retaliation beliefs will be more strongly positively related to proactive than reactive aggression. 38
Aggressive fantasy. Aggressive fantasy is a form of elaborative rehearsal that focuses specifically on aggressive scripts. Huesmann (1998) described aggressive fantasy as playing a role in script search and retrieval, the second step of social-cognitive information processing.
The more an individual engages in elaborative rehearsal of information, the easier it is for him/her to access this information from memory. The more elaborate and detailed the rehearsal, the more accessible to retrieval is the rehearsed script. In support of this theory, research has demonstrated that aggressive fantasy is associated with increased aggressive behavior (e.g.,
Eron, 1984; Huesmann & Eron, 1984).
To date, no empirical research has investigated the potential differences in aggressive fantasy in relation to proactive and reactive aggression. Following assertions that reactive aggression is associated with biases at the beginning of information processing (Dodge,
Lochman, Harnish, Bates & Petit, 1997) and proactive is associated with biases in later steps in the process, it follows that aggressive fantasy would be more predictive of reactive than proactive aggression. This would mean that reactively aggressive children would have easier access to aggressive scripts which have been reinforced through mental rehearsal. On its surface, this picture is inconsistent with the emotion-driven, unplanned nature of reactive aggression.
That is, if a child fantasizes in advance about an aggressive act, then he/she likely has some goal or plan in mind. However, we do not know if children fantasize about specific acts and then follow through with those acts or if children fantasize about a variety of aggressive acts that are not tied in an event-specific way to later aggressive behavior. In other words, it is possible that fantasy does not represent a plan for a behavior but is rather a mere fantasy. If the impact of aggressive fantasy on behavior is generalized, then it does not necessarily reflect a “plan” for behavior. Furthermore, if aggressive fantasy is associated with angry arousal, then the fantasy 39
might fit more with reactive types of aggression. Angry, aggressive fantasizers would be
expected to have increased angry arousal when fantasizing and might come to associate this
affective state with aggressive scripts. Then, when they experienced anger within a real social situation, they would have an affective-context that would prime them to retrieve aggressive scripts.
In support of the notion that aggressive fantasy might be more associated with reactive aggression, Musher-Eizenman and her colleagues (2004) found that children who were victimized exhibited greater levels of aggressive fantasy. Because victimization is higher among reactively aggressive children, this result fits with the prediction that aggressive fantasy would be higher in reactively aggressive children. Results in the current study will contribute to our understanding regarding aggressive fantasy and aggression subtypes.
Concern for consequences of aggression. Musher-Eizenman and her colleagues (2004) presented concern for consequences as a variable that plays a role in the final step of information processing, evaluation of environmental response. Huesmann (1998) stresses the importance of the child’s perception of the environment’s response in order for the response to have an impact on his/her behavior. Concern for consequences reflects the degree to which the child places weight on potential environmental responses, such as parental or school punishment. Although this construct has not been evaluated in relation to proactive and reactive aggression, it is theorized to contribute to later steps in the social-cognitive information processing model and is therefore implicated as playing more of a role in proactive than reactive aggression.
Furthermore, the construct relates to the child’s perceptions of environmental contingencies, which are theorized to drive proactive aggression as opposed to emotion-driven reactive 40
aggression. Therefore, it is expected that low concern for consequences will be a stronger
predictor for proactive than reactive aggression.
Present Study
Although research has supported the relation of peer victimization, anger control, hostile
attribution bias, and some outcome expectancies in distinguishing reactive and proactive
aggression, there is limited research regarding other social cognitive variables, such as retaliation
beliefs, negative self evaluation, aggressive fantasy, and concern for consequences. The current
research contributes to the body of research regarding reactive and proactive aggression by
attempting to distinguish the relation of proactive and reactive aggression with these social cognitive variables. The current research investigated these social cognitive variables as simultaneous predictors of reactive and proactive aggression in a fifth-grade school sample with a diverse ethnic composition that includes boys and girls. Participants were part of the intervention and comparison groups from the Powerful Choices aggression prevention program
(Heretick et al., 2003). Teacher ratings of proactive and reactive aggressive behavior, and student ratings of victimization, anger control, and social cognitions (i.e., hostile attribution bias, retaliation beliefs, negative self-evaluation, concern about consequences, aggressive fantasy) were used for the analyses.
Four hypotheses were developed and will be addressed in the present study:
1. Reactive and Proactive aggression will have low prevalence rates in the current study’s sample. Reactive aggression will be more prevalent than proactive aggression. If extreme groups are identified, the order of most to least prevalent subtypes will be as follows: nonaggressive, pervasive aggressive, reactive aggressive, and proactive aggressive. 41
2. Reactive and Proactive aggression will be correlated moderately to highly in the total
sample and gender sub-samples.
3. Reactive and Proactive Aggression will have distinct correlations profiles.
Specifically, reactive aggression will have stronger positive correlations with victimization, low
anger control, hostile attribution bias, and aggressive fantasy. Proactive aggression will have
stronger positive correlations with retaliation beliefs, low negative self evaluation, and low
concern for consequences.
4. Reactive and proactive aggression will have distinct profiles of predictors. It is
expected that victimization, hostile attribution bias, and aggressive fantasy will be significant,
unique predictors of reactive aggression, whereas low concern for consequences, low negative
self evaluation, and retaliation beliefs will be significant unique predictors for proactive
aggression. 42
METHOD
Participants
The participants in this study were part of an ongoing school-based aggression prevention program, Powerful Choices (Heretick et al., 2003), conducted in urban schools in a moderately sized Midwestern city. Powerful Choices is an eight to ten session program that attempts to change social cognitions (e.g., normative beliefs about aggression, hostile attribution biases) that are associated with aggression. Over the 3 years during which the program has been implemented, it has been conducted across five semesters in four schools with eighteen intervention classrooms and ten comparison classrooms. Classrooms that participated as comparison classrooms were offered the intervention in subsequent semesters. An evaluation design includes assessment of children’s aggressive-supporting cognitions and behavior at pre- and post-intervention. Three cohorts were defined based upon the academic year in which the data were collected (i.e., cohort 1 = 2002 – 2003; cohort 2 = 2003 – 2004; cohort 3 = 2004 –
2005). For the current study, pre-intervention data from nineteen classrooms across three cohorts were used (i.e., 9 classrooms in cohort one, 6 classrooms in cohort two, 4 classrooms in cohort three). Seven classrooms that participated in the first implementation of the program (i.e., cohort 0) were excluded because teacher-report surveys were not administered that year. Two of the classrooms participated as a comparison group in the fall semester and an intervention group in the spring semester. Therefore, only the pre-test data collected at the beginning of the year were used for those two classrooms. For the purpose of analyses, time of data collection was defined as fall (1) which was coded for all participants who completed surveys at the beginning of the Fall semester and spring (2) which was coded for all participants who completed surveys at the beginning of the Spring semester. In total, 340 fifth graders served as participants (Mean 43
age = 10.75; SD = .72). The sample was 55% female and diverse in terms of ethnic composition
(42% Caucasian, 17% African American, 11% Hispanic, 25% Multiracial/Other). Additional sample characteristics can be found in Table 1.
Procedures
A waiver of parent consent was obtained for this school-based prevention program
because it is presented as part of the school’s curriculum. Nevertheless, letters were sent to
parents explaining the nature of the program and the need for pre- and post- assessment in
intervention and comparison classrooms (see Appendix A). Students whose parents returned the
letter indicating that they did not permit their child to complete surveys did not participate.
Student assent also was obtained at the time of pre-testing (see Appendix B). Almost all of the
students in each classroom participated in the survey. Classroom surveys were administered to
all eligible students by trained psychology graduate and undergraduate students. These
facilitators read the survey questions and responses to the class as the students read to themselves
and answered the questions. Survey completion lasted approximately 45 minutes. Classroom
teachers completed surveys for each student and were provided $100 compensation for their time
(this included payment for completing post-tests). Teachers were given approximately two
weeks to complete the surveys at pre- and post-test (see Appendix C).
Some potential limitations are present in the current design. First, the sample is limited because it is exclusively urban and exclusively 5th graders. Therefore results will not be
generalizable beyond these types of children. Second, the use of self-report instruments
completed in the context of a classroom of peers could contribute to reporter bias on the part of
the children. Despite assurances of confidentiality, children could be susceptible to social
desirability bias and attempt to portray themselves in a good light either in reference to the 44
survey administrators or in reference to their neighboring peers. Third, teacher reports are also
susceptible to reporter bias based on several factors such as the reputations, attractiveness, or
other attributes of the children. Furthermore, teachers have some access to aggressive behavior
but are not privy to all aggressive acts committed by their students and therefore might not be fully aware of the aggressive behavior of all students, especially when aggressive behavior is
more covert (such as proactive aggression).
Measures
Student self-report and teacher-report surveys included measures of several behaviors,
cognitions, and affective variables. A subset of these measures, including several measures of
contextual and individual predictors and criterion behaviors, were used for the current study.
Specifically, student measures for this study include the extent of peer victimization they
experienced and a number of social cognitions related to aggression, including hostile attribution
bias, retaliation beliefs, negative self evaluation, concern for consequences, and aggressive
fantasy. Teacher-reports include children’s reactive and proactive aggression.
Across cohorts of the implementation of Powerful Choices, alterations were made to the
survey on two occasions, resulting in three versions of the student survey and two versions of the
teacher survey. Changes made to the teacher survey did not impact the current study variables,
though several relevant student-report scales were altered across surveys. Specific changes that
were made will be discussed for each measure below and are summarized in Table 2. All
effected scales were standardized in the calculation of scaled scores to correct for differences in
scales across survey versions. Internal consistency reliability (Chronbach’s alpha) levels will be
reported separately for each cohort.
Measures of Contextual and Personal/Individual Predictors 45
Victimization. Students reported how frequently they experienced peer victimization
using six items adapted from a scale of exposure to aggression at school (Dahlberg, Toal &
Behrens, 1996). The items included questions about physical (e.g., hit/pushed), verbal (e.g., threatened), and relational (e.g., excluded) victimization (e.g., “How often have you had rumors
spread about you at school?”). Students rated how frequently they experienced victimization
using a 4-point scale that ranged from 0 (never) to 3 (a lot of times) (see Appendix D). Higher scores indicate more frequent victimization. For the second and third cohorts, the language used for one of the items was altered slightly for the purpose of simplification but the general meaning of the item was not changed (see Table 1). Specifically, “how often has it happened that no one would talk to you at school” was changed to “how often have you been ignored or avoided at school.” Following the procedure used by Musher-Eizenman and her colleagues (2004), a victimization scaled score was computed based on the mean of the six victimization items with a possible range from 0 to 3. Reliability for the victimization scale from past research is not available. However, Musher-Eizenman et al. (2004) used a scale that combined this victimization scale with an exposure to aggression scale and the combined scale had adequate reliability (coefficient alpha = .70). Furthermore, victimization correlated significantly with low anger control and aggressive behavior and was a significant predictor of aggressive fantasy, which lends support for its convergent validity. Coefficient alphas for this scale were .82 for cohort 1, .87 for cohort 2 and .87 for cohort 3.
Anger Control. Students completed a 3-item measure of the students’ anger control that was adapted from the State-Trait Anger Expression Inventory (Spielberger, 1991). They rated, on a 4-point scale, the extent to which they are able to prevent temper outbursts, control angry feelings, and calm down (e.g., “I can stop myself from losing my temper”). The scale ranged 46
from 0 (not at all) to 3 (all the time) (see Appendix E). Scores were reverse-coded such that
higher scores indicate lower levels of anger control. Musher-Eizenman et al. (2004) reported good reliability for the scale (coefficient alpha = .78) and a significant correlation between anger
control and aggressive behavior (r = .45), lending support for the scale’s convergent validity.
Consistent with Musher-Eizenman et al. (2004), a scaled score consisting of the means of the
three anger control items was calculated. Coefficient alpha for this scale was .79 for cohort 1.
Anger control was not included in the surveys for cohorts 2 and 3.
Hostile Attribution Bias. A student self-report scale adapted from Dodge and Frame
(1982) consisted of eight items and was used to measure students’ hostile attribution biases.
Students were asked to imagine themselves in three social situations that involved experiencing
an ambiguous social provocation (e.g., “Pretend that a friend of yours is having a party and
everyone you know got an invitation in the mail except for you”). Scenarios included one
physical, one verbal, and one relational provocation. For each situation, the students answered
two questions. One required that they rate whether the provocation was accidental or intentional
(e.g., “Pretend that you are standing in the gym and you get hit hard in the back by a ball thrown
by another kid. Why do you think this happened?”), and the other asked them to rate whether the
initiators of the provocation had hostile intentions (i.e., “Was the kid trying to be mean?”).
Response options for each question were dichotomous with the scale for the first item being 0
(on purpose) and 1 (by accident) and for the second item 0 (yes) and 1 (no) (see Appendix F).
Higher scores indicate greater perceived hostility. This scale was the same on the surveys used
across all cohorts.
In their original measure, Dodge and Frame (1982) presented children with vignettes of
situations with peers that had neutral or negative outcomes. The authors then asked the children 47
to decide how the outcome in the vignette had occurred. Blind coders listened to the children’s
responses and rated on a 2-point scale the extent to which they described the intent of the other
child in the vignette as malicious or 1 benign. Inter-rater agreement was 96%. Musher-
Eizenman and her colleagues (2004) adapted this measure into a self-report scale consisting of two vignettes with one question in response to each (i.e., “why do you think this happened?).
They found the reliability for this version of the scale was low (alpha = .27) and, as a result,
dropped the scale from their study. Subsequent alterations to the scale, specifically adding a
second question in response to each scenario and adding two scenarios, sought to improve the
reliability of the scale. Following Musher-Eizenman and her colleagues (2004), a hostile
attribution scale score was computed which consists of the mean of responses to all eight hostile
attribution items with a possible range from 0 to 1. Using the original measurement of hostile
attribution bias, Dodge and Frame (1982) found that aggressive boys were more likely than their
non-aggressive counterparts to attribute hostile intent in ambiguous situations, this lending
support to the convergent validity of hostile attribution bias as measured in this study.
Coefficient alphas for this scale were .61 for cohort 1, .44 for cohort 2 and .63 for cohort 3.
Retaliation Beliefs. Retaliation beliefs were measured using six to nine items adapted
from the Retaliation Approval subscale from Huesmann and Guerra’s (1997) Normative Beliefs
about Aggression Scale. The students were asked to imagine a social situation in which one
child behaved aggressively toward a second child, with one vignette describing physical
aggression, one describing verbal aggression (i.e., cohort one only), and the other describing
relational aggression. For each item, the students then rated the appropriateness of three
retaliatory responses, including verbal aggression, physical aggression, and relational aggression
(e.g., “Pretend one kid hits a second kid. Do you think it’s OK for the second kid to yell at the 48
first kid?”). Students rated the perceived appropriateness of each response using a 4-point scale
that ranged from 0 (It’s perfectly okay) to 3 (It’s really wrong) (Items in cohort one were re- coded to fit this scale; see Appendix G). Higher scores indicate greater support for aggression.
The scale used for cohort 1 included nine items, three in response to each of three scenarios (i.e., one verbal, one physical, one relational scenario). The scale used for cohorts 2 and 3 included six items with three questions in response to each of two scenarios (i.e., one verbal, one relational scenario).
Huesmann and Guerra’s (1997) original scale consisted of twelve questions in which they presented vignettes describing either verbal or physically aggressive situations occurring between two peers of specified genders. Using the same response scale described above, they asked children to rate the extent to which they believe it is okay for the victim to retaliate with verbal or physical aggression if the provocation was verbal and with physical aggression if the provocation was physical. They asked about each provocation/retaliation combination for each possible gender combination (i.e., boy/boy, boy/girl, girl/girl, girl/boy). The overall retaliation
approval scale in their study had good reliability (coefficient alpha = .82), was fairly stable
across one year among fourth graders (r = .44), and was shown to correlate significantly with peer-nominated aggression (r = .20), thus supporting convergent validity. This scale was adapted by Musher-Eizenman and her colleagues (2004) into the 9-item scale used in this study for cohort 1. The items were constructed as gender-neutral and a third type of provocation, relational, was added. Furthermore, three potential retaliations, including physical, verbal, and relational, were presented as potential responses for each of the three provocations. Musher-
Eizenman and her colleagues (2004) found the scale to have good reliability (coefficient alpha =
.90) and it correlated significantly with aggressive behavior (r = .51), lending support to its 49 convergent validity. Consistent with the method used by Musher-Eizenman and her colleagues, a retaliation beliefs scaled score was calculated that consists of the mean of all retaliation belief items with a possible range from 0 to 3. Coefficient alphas for this scale were .89 for cohort 1,
.78 for cohort 2 and .85 for cohort 3.
Negative Self Evaluation. Using a 6 to 8-item scale adapted from Egan et al. (1998), students rated the extent to which they would evaluate themselves negatively if they were to retaliate after being aggressed upon by another child. The students were asked to imagine themselves in two situations in which they experienced aggression from another child, one physically aggressive and one relationally aggressive. For each situation, the students rated the extent to which they would experience different negative evaluations (i.e., be upset with themselves, feel guilty, fear hurting the other child’s feelings, fear physically or socially hurting the other child) if they were to retaliate (e.g., “Pretend that a kid trips you in the lunchroom and you drop your tray. You’re thinking about pushing the kid. Some kids would be upset with themselves if they pushed the kid, but other kids would not. Would you be upset with yourself if you pushed the kid?”). They rated the likelihood of experiencing these evaluations on a 4-point scale that ranged from 0 (not at all) to 3 (a lot) (Items in cohort one were re-coded to fit this scale; see Appendix H). Scores were reverse coded such that higher scores indicate lower levels of negative self-evaluation. The scale used for cohort one included 8 items, with four questions in response to each of the two scenarios. The version of the survey used in cohorts two and three included six items with three in response to the same two scenarios used for cohort one.
Questions related to victim harm (i.e., hurt the other child physically, cause the other child to lose friends) were dropped. 50
The original scale developed by Egan and her colleagues (1998) included eight items focused on the value of victim suffering. In response to eight different scenarios, children rated on a 4-point scale the extent to which they would care if they hurt a child in an act of retaliatory aggression (physical or verbal). The original scale demonstrated good reliability (coefficient alpha = .85 - .90). This scale was adapted by Musher-Eizenman and her colleagues (2004) into a
4-item scale similar to the one used in the current study. Children read a description of a physically aggressive situation and then rated how upset they would be if they aggressed or how afraid they would be of hurting another child in the situation. The adapted scale had good reliability (coefficient alpha = .80) and it correlated significantly with aggressive behavior (r =
.46), lending support to its convergent validity. Consistent with Egan et al (1998) and Musher-
Eizenman et al (2004), a scaled score consisting of the mean of all negative self evaluation items was calculated with a possible range from 0 to 3. Coefficient alphas for this scale were .91 for cohort 1, .90 for cohort 2 and .91 for cohort 3.
Concern for Consequences. The extent to which students cared whether their aggressive behavior would lead to negative consequences for them was measured using a 3- to 6-item scale developed by Musher-Eizenman and colleagues (2004). Students were asked to imagine themselves in a social situation in which another student had physically aggressed against them.
They then were asked to rate the extent to which they would care about potential negative outcomes when deciding whether to respond aggressively to the provocation. Specifically, students rated how much they would care about getting into trouble at home, getting into trouble at school, and hurting the other child (e.g., “Pretend that a kid picks on you and starts to push you around. You have to decide what to do. You want to get even with the kid but there are
some things you want to decide about first. If you do something to get even with the kid who is 51
pushing you around, you might get into trouble at school. Would you care if this happened?”)
For the sake of consistent measurement across cohorts, however, only the two common items used in all cohorts were included in the scale for the current study (i.e., getting into trouble at home and getting into trouble at school). Children rated their degree of concern on a 3-point or
4-point scale. Items on the 3-point scale (cohort 1) ranged from 1 (I would REALLY CARE!) to 3
(I would NOT CARE AT ALL) and items on the 4-point scale (cohorts 2 and 3) ranged from 0
(Not at all) to 3 (A lot) (see Appendix I). Higher scores indicate lower degree of concern for consequences of aggression. The scale used for the first cohort originally included four items
(i.e., getting trouble at home, getting trouble at school, looking weak, and staying safe). Changes made to the scale used for the second cohort included changing the response scale (i.e., 3- reponse to 4-response with different language), dropping one item (i.e., staying safe), and simplifying the scenario and item language (i.e., no change to meaning). For the scale originally used for the third cohort, three new items were created and added (i.e., losing teacher trust, losing parent trust, being made fun of by peers). The mean of the two common scale items comprised the concern for consequences scaled score with a possible range from 1 to 3 (cohort one) or 0 to 3 (cohorts two and three). The three-item scale in the survey used for cohort 1 of the current study was created by Musher-Eizenman and her colleagues and they found the scale to have adequate reliability (coefficient alpha = .72). The scale correlated significantly with aggressive behavior (r = .40), supporting its convergent validity. Coefficient alphas for this scale were .53 for cohort 1, .71 for cohort 2 and .80 for cohort 3.
Aggressive Fantasy. Mental and behavioral rehearsal of aggressive scripts was measured using a 7-item aggressive fantasy scale that was adapted from the Child Fantasy Inventory
(Rosenfeld et al, 1982). Four items measured mental rehearsal (e.g., “How often do you think 52
about hitting or hurting somebody that you don’t like?”), and three items measured behavioral rehearsal (e.g., “How often do you play games where you pretend to use a gun or other weapon on somebody?”). Students reported the frequency with which they engage in these behaviors using a 3-point or 4-point scale. The 3-point scale (i.e., cohort 1) ranged from 0 (no) to 2 (a lot) and the 4-point scale (i.e., cohorts 2 and 3) ranged from 0 (never) to 3 (a lot) (see Appendix J).
Higher scores indicate greater frequency of aggressive fantasy. For cohorts two and three, the language of the items was adjusted slightly (i.e., “how often” was added to the beginning of the questions), and a 4-item response option with slightly different language was used.
The original Children’s Fantasy Inventory developed by Rosenfeld and her colleagues included a 6-item “aggressive” fantasy subscale (coefficient alpha = .64). Musher-Eizenman and her colleagues used three items from the Children’s Fantasy Inventory and added two items, resulting in a 5-item aggressive fantasy scale. The scale used in the current study includes these five items plus two additional items. Musher-Eizenman et al (2004) found acceptable reliability for their aggressive fantasy scale (coefficient alpha = .66) and correlations with aggressive behavior (r = .64) also lended support to the convergent validity of the aggressive fantasy scale.
Consistent with Musher-Eizenman, an aggressive fantasy scaled score, consisting of the mean of these items, was computed with a possible range of scores from 0 to 3 (cohort one) or 0 to 2
(cohorts two and three). Coefficient alphas for this scale were .78 for cohort 1, .73 for cohort 2 and .73 for cohort 3.
Criterion Measures
Proactive and Reactive Aggression. Students’ use of proactive and reactive aggression was measured by teacher reports using the reactive and proactive aggression items from Dodge and Coie’s scale (1987). Teachers reported how frequently the student engaged in aggressive 53
behavior with three items describing reactive aggression (e.g., “When the child is teased or
threatened, he or she gets angry easily”) and three describing proactive aggression (e.g., “This child gets other kids to gang up on a peer that he or she does not like”). A 5-point scale was used
that ranged from 0 (never) to 4 (almost always) (see Appendix K). Higher scores indicate
greater frequency of aggressive behavior. In their initial development of the scale, Dodge and
Coie (1987) created 12 items and collected data from the teachers of 259 third, fifth, and sixth
graders. Through factor analysis, the authors selected three items for each scale that contributed
most to the differentiation of the reactive and proactive subtypes. They found excellent
reliability for both reactive (coefficient alpha = .90) and proactive (coefficient alpha = .91)
aggression. The subscales were highly correlated with each other (r = .76). The researchers
claimed that the scales were distinct from each other because the within-scale item-item
correlations were higher than the between-scale item-item correlations. In a second study
conducted by Dodge and Coie (1987), the reliabilities were not as high for reactive aggression
(coefficient alpha = .64) or proactive aggression (coefficient alpha = .87). As discussed above,
in a series of studies, Dodge and Coie (1987) supported the validity of their teacher-report scales
which correlated with behavioral observations of proactive and reactive aggression. Teacher
reports also correlated in a theoretically expected way with social cognitions (i.e., reactive
aggression correlated with hostile attribution bias and proactive aggression correlated with
positive outcome expectancies for aggression). Some concerns regarding this scale include its
limited scope (i.e., proactive scale measures bullying aggression moreso than instrumental
aggression), its brevity, teacher-report bias and weak factor analysis support for the two-factor
solution. In the current study, proactive and reactive aggression scale scores were computed
separately by taking the mean of the three items associated with each sub-type with a possible 54 range of scores from 0 to 4. Coefficient alphas for the reactive aggression scale were .95 for cohort 1, .95 for cohort 2 and .94 for cohort 3, and for the proactive scale they were .94 for cohort 1, .86 for cohort 2 and .84 for cohort 3. 55
RESULTS
Preliminary Analyses
Three sets of preliminary analyses were computed. First, analyses to facilitate data reduction, including zero-order correlations among all predictor variables and a factor analysis with all social-cognitive predictor variables will be presented. Second, demographic, school, time of data collection (i.e., Fall or Spring), and cohort differences will be examined for all study variables to determine the need to control for these demographic/background variables in subsequent analyses. Third, chi-square tests were used to ascertain differences in sex and race across cohorts and schools. Means, standard deviations, and ranges for all study variables are presented in Table 3.
Zero-order correlations among all study variables. Zero-order correlations among all study variables are presented in Table 4. Results revealed a modest to moderate degree of multi- collinearity among many of the study variables. First, Reactive and Proactive Aggression were highly correlated (r = .74), as expected, based on prior research. Although Reactive and
Proactive Aggression were correlated highly, these variables will be examined separately due to the focus of the current study and their conceptual meaningfulness as distinct variables. These variables also were correlated with several of the predictor variables; however, these results will be reviewed as part of the main analyses. Second, Victimization (rs .14 - .21) and Anger Control
(rs .18 - .39) were correlated only modestly with the other predictor variables. Among the
social-cognitive variables, Hostile Attribution Bias also correlated modestly with other study
variables (rs .12 - .22), whereas Retaliation Beliefs, Low Negative Self Evaluation, Aggressive
Fantasy, and Low Concern for Consequences were correlated moderately with each other (rs .38
- .58). 56
Factor analysis with all social-cognitive variables. Due to the multi-collinearity among the social-cognitive variables, a principal components factor analysis with promax rotation was computed with all social-cognitive variables to determine if the five predictor variables could be reduced into fewer factors for subsequent analyses. Examination of the scree plot and the meaningfulness of one and two factor solutions led to the decision to accept the 2-factor solution.
Table 5 shows the factor loadings for each variable as well as the amount of variance accounted for and the eigenvalue for each factor. Retaliation Beliefs, Low Negative Self Evaluation,
Aggressive Fantasy, and Low Concern for Consequences loaded on the first factor (factor loadings ranging from .73 to .84) which accounted for 51.26% of the variance; and Hostile
Attribution Bias (factor loading = 1.0) loaded on the second factor which accounted for 19% of the variance.
Based on the pattern of correlations among the social-cognitive variables and the results of the factor analysis, a social-cognitive composite comprised of Retaliation Beliefs, Low
Negative Self Evaluation, Aggressive Fantasy, and Low Concern for Consequences was used in regressions and discriminant function analysis results to be reported later. These four variables were standardized and then their average was calculated and used as a composite score termed
Social-Cognitive Composite.
Demographic, school, time of data collection, and cohort differences. To determine which demographic/background variables needed to be controlled in subsequent analyses, four analyses were computed to test the main study variables for differences based on demographics, school, time of data collection, and cohort. First, a MANOVA tested for differences in sex, race, and school for the study variables (Anger Control was not included because it was measured only for the first cohort and therefore would have unnecessarily limited the sample size for the 57
analyses for the other study variables). A second MANOVA was computed to test for cohort
differences for those variables that had common measurement across cohorts. Third, a
MANOVA tested for differences in time of data collection for all study variables (again, Anger
Control was not included). Finally, an ANOVA and t-tests were computed to test for differences
in sex, race, school, and time of data collection for Anger Control.
First, a MANOVA was computed in which the main effects and 2-way interactions were
examined for sex, race, and school for the dependent variables Reactive Aggression, Proactive
Aggression, Victimization, Hostile Attribution Bias, Retaliation Beliefs, Low Negative Self
Evaluation, Aggressive Fantasy, and Low Concern for Consequences. Table 6 shows that
significant main effects were found for sex and school. No significant interactions were
indicated. Sex differences included higher levels among boys for Reactive Aggression,
Proactive Aggression, Retaliation Beliefs, Low Negative Self Evaluation, Aggressive Fantasy, and Low Concern for Consequences. A main effect for school was found for Reactive
Aggression and Proactive Aggression. While a marginal between-subjects effect was found for
Retaliation Beliefs and Low Negative Self Evaluation, differences between schools just missed
statistical significance in the pairwise comparisons (i.e., School 2 > School 1, p = .10). Children in School 3 reported the most reactive aggression, followed by those from School 2, and the lowest levels were reported by students from School 1. For proactive aggression, children from
School 3 reported the most proactive aggression, followed by the children from School 2 and
then the children from School 1. School 2’s proactive aggression was not significantly different
from School 1’s, however.
Second, a MANOVA was computed in which cohort differences were examined for those
study variables that had common measurement across all cohorts (i.e., Reactive Aggression, 58
Proactive Aggression, Hostile Attribution Bias, and Victimization). A marginal difference was
indicated by the multivariate test, but none of the between-subjects effects was significant (see
Table 7).
Third, a MANOVA was computed which tested for differences based on time of data
collection (Fall or Spring) for all study variables (except for anger control). Results of this
analysis are presented in Table 8. Significant main effects were found for Reactive Aggression,
Proactive Aggression, and Aggressive Fantasy. Specifically, Reactive Aggression and Proactive
Aggression both were higher for Spring data collection in comparison with Fall data collection and Aggressive Fantasy was higher for Fall data collection in comparison with Spring data collection.
Fourth, analyses for Anger Control included an ANOVA which tested for differences in
sex, race, and school effects, and t-tests which tested for differences in time of data collection.
No significant differences were found for sex, race, school, or time of data collection for Anger
Control.
Because significant sex and school differences were found among the study variables,
these factors were controlled in subsequent regression analyses. Codes for school were
developed as follows. Schools were given a rank number based on the levels of aggression
reported for their school. Specifically, aggression levels were lowest at School 1 (ranked 1),
higher at School 2 (ranked 2), and were highest at School 3 (ranked 3). Higher codes therefore
indicate higher levels of aggression. It was decided not to control for time of data collection
because it was partially confounded with school. That is, Fall data collection took place at
Schools 1 and 2, whereas Spring data collection took place at Schools 2 and 3. Therefore, it is 59
difficult to ascertain the extent to which time of data collection differences were due to school
differences or to actual time differences.
Differences in sex and race across school and cohort. Chi-square analyses were
computed to assess for differences in sex and race across schools and cohorts. No significant
differences were found for cohort in terms of sex and race. Also, schools were not significantly
different in sex, but a significant difference in race emerged across schools (χ2(6) = 20.79; p <
.01). Specifically, School 1 had more Caucasian participants than other schools (School 1 =
60%, School 2 = 48%, School 3 = 36%) and had fewer African American participants (School 1
= 5%, School 2 = 12%, School 3 = 27%). The following sections outline results as they pertain to each of the hypotheses proposed for the current study.
Hypothesis 1: Reactive and Proactive Aggression Will Have Low Prevalence in This Community
Sample
Based on previous research with community samples, it was hypothesized that Reactive and Proactive aggression would have low prevalence rates in the current study’s sample.
Furthermore, Reactive Aggression was expected to be more prevalent than Proactive Aggression in the total sample and in gender subsamples. Finally, it was hypothesized that if extreme aggression groups were identified, the largest group would be nonaggressive, followed by pervasive aggressive, reactive aggressive and proactive aggressive.
Table 9 shows the means, standard deviations, variances, ranges, and percentiles for
Reactive and Proactive Aggression for the entire sample and gender subgroups. On average, teachers reported that children engage in Reactive Aggression “rarely” to “sometimes” which is significantly more frequently than they reported for Proactive Aggression (i.e., between “never” and “rarely”) (t(339) = 19.22, p < .01.). A similar pattern was present for boys, whose reported 60
Reactive Aggression was “rarely” to “sometimes,” which is significantly more than reported
Proactive Aggression, which fell between “never” and “rarely” (t(150) = 15.64, p < .01). Also,
teacher reports of girls Reactive Aggression fell between “rarely” and “sometimes,” which was
significantly greater than reported Proactive Aggression, which fell between “never” and
“rarely” (t(187) = 12.12, p < .01).
Frequencies and quartiles were used to identify extreme groups within this sample. For
Reactive Aggression, measured on a 5-point scale ranging from 0 to 4, the first quartile included
scaled scores that were .00; the second quartile range was from .01 to .99; the third quartile range
was from 1.0 to 2.32; and the fourth quartile ranged from 2.33 to 4.0. For Proactive Aggression,
the first and second quartiles included Proactive Aggression scaled scores that were .00; the third
quartile included scores that fell between .33 and .99; and the fourth quartile included scores that
fell between 1.0 and 4.0. To place these scores in perspective, a “0” would indicate that teacher
answered “never” on all questions and “never” for the remaining two questions, a “.33” would
indicate that the teacher reported “rarely” for one of the questions, and a “1” would indicate one
of several possible patterns, such as the teacher reported “rarely” on all items or “usually” on one
item.
In order to identify different extreme groups of children based on their levels of Reactive
and Proactive Aggression (i.e., nonaggressive, reactive aggressive, proactive aggressive, pervasive aggression), crosstabs which examined the cross-distribution of quartiles for Reactive and Proactive Aggression were examined. The following extreme groups were identified: nonaggressive participants were those who reported levels of Reactive Aggression that fell in the first quartile and levels of Proactive Aggression that fell in the first or second quartile (i.e., were never identified as aggressive on any of the items); Reactive Aggressive participants were those 61
who reported levels of Reactive Aggression that fell in the fourth quartile and levels of Proactive
Aggression that fell in the first or second quartiles; and pervasive aggressive participants were
those whose Reactive Aggression and Proactive Aggression both fell in the fourth quartile. No
proactive aggressive group was identified because there were no participants who reported levels
of Proactive Aggression that fell in the fourth quartile and Reactive Aggression that fell in the
first quartile. Descriptive statistics for the extreme groups can be found in Table 10 and
demographics for each group are presented in Table 11. Nonaggressive participants were most
prevalent in this sample (n = 92), followed by pervasive aggressive (n = 71), reactive aggressive
(n = 67) and proactive aggressive (n = 0). Given that this study uses a community sample, the
low prevalence of the aggression groups is not surprising. A χ2 analysis was used to test for
differences in prevalence among the extreme groups. The analysis revealed a significant
difference in group prevalence (χ2(3) = 32.14, p < .001). The Nonaggressive group (n = 92) is
the most prevalent, with the Reactive (n = 71) and Pervasive (n = 67) groups being of relatively
equal prevalence.
Using this sample-specific approach to deriving extreme groups, a chi-square analysis
was computed to assess for differences in sex, race, school, and cohort for the extreme groups
(see Table 12). Significant differences were identified for sex, race, and school. Specifically,
sex differences (χ2 (2) = 20.53, p < .01) were primarily due to girls’ higher levels of membership
in the nonaggressive group and boys’ higher levels of membership in the reactive aggressive
group. Race differences (χ2 (6) = 12.65, p = .05) can be attributed to higher levels of reactive aggression and lower levels of pervasive aggression for Caucasian children as well as higher levels of nonaggression and lower levels of reactive aggression for Hispanic children. Finally, school differences (χ2 (4) = 50.22, p < .01) can be attributed to higher levels of nonaggression 62
and lower levels of pervasive aggression at School 1, higher levels of nonaggression and lower
levels of reactive aggression at School 2, and lower levels of nonaggression and higher levels of
reactive and pervasive aggression at School 3.
In summary, hypothesis 1 was confirmed by these results. Specifically, Reactive and
Proactive Aggression had low prevalence and Reactive Aggression was more prevalent than
Proactive Aggression in the total sample and gender subsamples. Also, the nonaggressive group was the largest group, followed by the pervasive aggressive group, reactive aggressive group,
and proactive aggressive group.
Hypothesis 2: Reactive and Proactive Aggression Will be Correlated Moderately to Highly in the
Total Sample and Gender Sub-Samples
Zero-order correlations were computed to determine the correlation between Reactive
and Proactive Aggression in the total sample as well as in gender subsamples. As expected,
Reactive Aggression and Proactive Aggression were highly and significantly correlated in the total sample (r = .74) and in gender sub-samples (Boys r = .73; Girls r = .75). In summary,
these results support the hypothesis that Reactive and Proactive Aggression would be positively
and moderately to highly correlated in the total sample and gender subsamples.
Hypothesis 3: Reactive and Proactive Aggression Will Have Distinct Correlation Profiles with
the Predictor Variables
Based upon previous research, Reactive and Proactive Aggression were expected to have distinct correlations profiles. Specifically, Reactive Aggression was expected to have stronger positive correlations with Victimization, Low Anger Control, Hostile Attribution Bias, and
Aggressive Fantasy. Proactive Aggression, on the other hand, was expected to have stronger 63
positive correlations with Retaliation Beliefs, Low Negative Self Evaluation, and Low Concern
for Consequences.
Two sets of correlations were computed. First, zero-order correlations were computed
between Reactive and Proactive Aggression and all predictor variables. Second, partial
correlations were computed separately for Reactive and Proactive Aggression with all predictor
variables while the opposing subtype of aggression was controlled statistically.
Zero-order correlations of Reactive and Proactive Aggression with predictors. Table 4
presents the correlations among all study variables. The first two columns present the
correlations for Reactive and Proactive Aggression with all of the predictor variables. Higher
levels of Reactive Aggression were associated with higher levels of Victimization (r = .14),
Hostile Attribution Bias (r = .13), Retaliation Beliefs (r = .26), Low Negative Self Evaluation (r
= .29), Aggressive Fantasy (r = .24), and Low Concern for Consequences (r = .18). Higher
levels of Proactive Aggression were associated with higher levels of Low Anger Control (r =
.18), Hostile Attribution Bias (r = .12), Retaliation Beliefs (r = .23), Low Negative Self
Evaluation (r = .27), Aggressive Fantasy (r = .19), and Low Concern for Consequences (r =
.15). Correlations also were computed for Reactive and Proactive Aggression with the Social-
Cognitive Composite. Both Reactive Aggression (r = .31) and Proactive Aggression (r = .26)
correlated significantly with the Social-Cognitive Composite, indicating that both types of
aggression were associated with aggression-related social cognitions.
To assess whether the zero-order correlations of each predictor with reactive versus
proactive aggression were significantly different from each other, t-tests were computed.
Because the correlations between the two subtypes of aggression and each study variable are
taken from the same sample, a formula had to be applied to the correlations to correct for the 64
shared variance due to the dependent samples. This formula is described in Statistical Methods
for Psychology – Third Edition (Howell, 1992). Among those variables that were correlated
significantly with both Reactive and Proactive Aggression, there were no significant differences
identified between those correlations.
Partial correlations between Reactive and Proactive Aggression with study variables.
Partial correlations were computed in which each subtype of aggression was correlated with the
predictor variables, while the opposing subtype of aggression was controlled (see Table 13).
Higher levels of Reactive Aggression were associated with higher levels of Victimization (r =
.19), Retaliation Beliefs (r = .14), Low Negative Self Evaluation (r = .15), Aggressive Fantasy (r
= .14), Low Concern for Consequences (r = .11), and the Social-Cognitive Composite (r = .17), whereas higher levels of Proactive Aggression were associated with lower levels of
Victimization (r = -.13), and higher levels of Low Anger Control (r = .13).
In summary, these results provide partial support for hypothesis 3. Specifically, as
expected, Victimization and Aggressive Fantasy correlated more strongly with Reactive
Aggression than Proactive Aggression. Contrary to expectations, however, Retaliation Beliefs,
Low Negative Self Evaluation, and Low Concern for Consequences correlated more strongly
with Reactive Aggression than Proactive whereas Low Anger Control correlated more strongly with Proactive Aggression than Reactive. Furthermore, Hostile Attribution Bias did not correlate with either variable though it was expected to correlate with Reactive and not Proactive
Aggression.
Hypothesis 4: Reactive and Proactive Aggression Will Have Distinct Profiles of Predictors
A pattern of results was expected based on previous research. Specifically, it was expected that Victimization, Hostile Attribution Bias, and Aggressive Fantasy would be 65
significant, unique predictors of Reactive Aggression, whereas Low Concern for Consequences,
Low Negative Self Evaluation and Retaliation Beliefs would be significant unique predictors for
Proactive Aggression.
Two sets of hierarchical regression analyses were computed to determine whether there
were unique predictors of Reactive and Proactive Aggression. In the first set of regression
analyses, each subtype of aggression was predicted separately after sex and school differences
were controlled statistically in the first step. In the second set of analyses, the opposing subtype
of aggression also was controlled statistically in the first step before entering the predictor
variables. Anger Control was not included as a predictor variable in these analyses because it
was measured in cohort 1 only and therefore would limit the sample size for the regression analyses. Results of these regression analyses can be found in Table 14.
Prediction of Reactive and Proactive Aggression without controlling for the opposing aggression subtype. Two regression equations were computed in which Reactive Aggression
and Proactive Aggression were predicted separately. In the first step of these analyses, sex and
school were entered in order to control for differences on these variables. In the second step,
Victimization, Hostile Attribution Bias, and the Social-Cognitive Composite were entered simultaneously. In predicting Reactive Aggression, the demographic variables entered in the first step accounted for a significant proportion of the variance (16%) in Reactive Aggression. In the second step, the predictor variables accounted for a significant increase of 8% of the variance. Among the predictors, Victimization (β = .15) and the Social-Cognitive Composite (β
= .22) were significant unique predictors of Reactive Aggression above and beyond the demographic variables. In predicting Proactive Aggression, the demographics in the first step predicted a significant proportion of the variance in Proactive Aggression (6%). In the second 66
step, the predictor variables accounted for a significant increase of 6% of the variance in
Proactive Aggression. Among the predictors, the Social-Cognitive Composite (β = .23) was a
significant unique predictor of Proactive Aggression above and beyond the demographic
variables.
Prediction of Reactive and Proactive Aggression with opposing aggression subtype
controlled. Two regression equations were computed in which Reactive and Proactive
Aggression were predicted separately. In the first step, sex, school, and the opposing subtype of aggression were entered, and in the second step, Victimization, Hostile Attribution Bias, and the
Social-Cognitive Composite were entered simultaneously. In predicting Reactive Aggression, the demographic variables and Proactive Aggression entered in the first step accounted for a significant proportion of the variance in Reactive Aggression (60%). In the second step, the predictor variables accounted for a significant increase of 2% of the variance. Among the predictors, Victimization (β = .14) and the Social-Cognitive Composite (β = .07) emerged as
significant unique predictors of Reactive Aggression above and beyond the demographics and
Proactive Aggression. In predicting Proactive Aggression, the demographic variables and
Reactive Aggression accounted for a significant proportion of the variance in Proactive
Aggression (56%). In the second step, the predictor variables accounted for a significant
increase of 1% of the variance. Among the predictors, only Victimization (β = -.09) emerged as
a significant unique predictor of Proactive Aggression above and beyond demographic variables
and Reactive Aggression.
Identifying classification variables for extreme groups. Extreme groups, including
nonaggressive, reactive aggressive, and pervasive aggressive groups, were identified in the
sample, as described earlier (see Tables 10 and 11). A discriminant function analysis was 67
computed to determine which of the predictors contributed best to the classification of
participants into each of these groups.
Because group sizes were unbalanced, the size of each group was used in the calculation
of prior probabilities for these analyses. Box’s M test (Box’s M = 26.87, p = .01) indicated that
the homogeneity of covariance assumption was violated for the current analysis. Therefore, the
discriminant function analyses were computed using both within-groups and separate-groups
covariance matrices and results across these analyses were identical. Therefore, the results based
on the within-groups covariance matrix were used.
Two discriminant functions emerged, but only the first was statistically significant. Both
functions will be presented, however, because the second function might be of some theoretical
interest. The first function (χ2(6) = 33.91, p < .01) accounted for 92% of the variance in
classification into the groups and was comprised primarily of the Social-Cognitive Composite
(discriminant function coefficient = .87) as well as Hostile Attribution Bias (discriminant
function coefficient =.28) and Victimization (discriminant function coefficient =.23). The
canonical correlation for the first function (r = .36) suggests that this function has a moderate
correlation with group assignment. The second function (χ2(2) = 2.77, p = .25) accounted for 8% of the variance in classification into the groups and was comprised of Victimization
(discriminant function coefficient =.96). The canonical correlation for the second function (r =
.11) suggests that this function has a low correlation with group assignment. Together, these discriminant functions successfully classified 52.2% of the cases in the current sample.
The first function, which was characterized most strongly by the Social-Cognitive
Composite, was most associated with the pervasive aggressive group (M = .54), was negatively associated with the nonaggressive group (M = -.36) and was weakly and negatively associated 68 with the reactive aggressive group (M = -.09). The pattern which emerged for the second function, which was characterized by Victimization, suggests that it was associated most strongly with the reactive aggressive group (M = .17) and was weakly and negatively associated with the nonaggressive group (M = -.09) and the pervasive aggressive group (M = -.05).
In summary, these results partially support Hypothesis 4. Both the regression analyses and the pattern suggested by the discriminant function analysis support the expectation that
Victimization would be a stronger predictor of Reactive Aggression than Proactive Aggression.
The results are not as strong for the other predictors, however. The Social-Cognitive Composite was a significant predictor for Reactive and not Proactive Aggression, but the composition of the
Composite precludes interpretation of the contribution of its component variables. That is, the composite includes variables that were expected to predict both Reactive (i.e., Aggressive
Fantasy) and Proactive Aggression (i.e., Retaliation Beliefs, Low Negative Self Evaluation, Low
Concern for Consequences). No predictions were made regarding this composite, only its component variables. 69
DISCUSSION
The purpose of this study was to investigate social-cognitive predictors of reactive and proactive aggression in a diverse, urban, 5th grade sample. This section will review the four hypotheses proposed for this study, describe the extent to which each was supported, and explore potential explanations for the results. Implications of this research will be discussed as well as limitations and ideas for future research.
Hypothesis 1: Reactive and Proactive Aggression Will Have Low Prevalence in This Community
Sample
This hypothesis was supported in that both reactive and proactive aggression were found to have low prevalence in the current sample. Reactive aggression was more prevalent than proactive aggression in the total sample and in the gender subsamples. Specifically, the average frequency of reactive aggression was between “rarely” and “sometimes”, and the average frequency of proactive aggression was between “never” and “rarely.” This prevalence level is fairly consistent with prior research which found that on average children in grades 4 through 6 used reactive aggression between “rarely” and “sometimes” (M = 1.68, SD = .89) and proactive aggression slightly more than “rarely” (M = 1.28, SD = .60) (Poulin & Boivin, 2000).
In order of prevalence, the extreme groups identified in the current research were nonaggressive (27%), pervasive aggressive (21%), and reactive aggressive (20%). These prevalence rates differed significantly, with the nonaggressive group being significantly more prevalent than the two aggressive groups. It is possible that these numbers underrepresent the prevalence of reactive and proactive aggression. Proactive aggression is more likely to be under- estimated due to its covert nature. Despite this possibility, these prevalence rates for the pervasive and reactive aggression groups are higher than those identified by Dodge et al. (1997) 70
who used a cutoff of one standard deviation above the mean to identify extreme groups in a
community sample (i.e., pervasive 11%, reactive 7%). No exclusively proactive aggressive
children were identified in the current study. Therefore, although proactive aggression was present, it did not occur in the absence of reactive aggression. This finding is not fully consistent
with prior research which has identified exclusively proactive aggressive groups, such as Dodge
et al. (1997) who identified a proactive aggressive group that consisted of 3% of their community
sample. The use of a community sample and the elementary school age of the children in the
current study potentially accounts for this lack of exclusively proactive aggressive children.
Hypothesis 2: Reactive and Proactive Aggression Will be Correlated Moderately to Highly in the
Total Sample and Gender Sub-Samples
As expected, reactive and proactive aggression were highly correlated in the total sample
and gender subsamples (rs = .73 - .75). Similar correlations have been found in previous
research (e.g., .76 in Dodge & Coie, 1987). The high correlation between reactive and proactive aggression calls into question whether the subtypes are, in fact, highly distinct phenomena.
Adding credence to this conclusion is the lack of exclusively proactive aggressive children in the current study, as discussed above.
Hypothesis 3: Reactive and Proactive Aggression Will Have Distinct Correlation Profiles with the Predictor Variables
Partial support for Hypothesis 3 was found. Victimization, as expected, correlated positively with reactive aggression and negatively with proactive aggression. Similar associations have been found in previous research (e.g., Schwartz et al., 1998). Victimization
consistently has been linked with reactive and not proactive aggression (e.g., Camodeca et al.,
2002; Pelligrini, Bartini, & Brooks, 1999). The causal direction of the relation between 71
victimization and reactive aggression, however, cannot be determined by this study. That is, we
do not know whether children who are victimized are more likely to develop reactive aggression,
or if children who are reactively aggressive are more likely to become victimized, or both.
Long-term prospective studies beginning in pre-school could help to shed light on this question.
Either way, victimization experiences clearly distinguished between reactive and proactive aggression.
Furthermore, as expected, aggressive fantasy correlated positively with reactive aggression and not with proactive aggression. Prior research has not investigated aggressive fantasy in relation to reactive and proactive aggression. It was expected that aggressive fantasy would correlate positively with reactive aggression in part because in past research it has been shown to correlate with victimization. Theory suggests that children who engage in aggressive fantasy increase the number of aggressive scripts they have available to them and strengthen connections to these scripts, making them more accessible (Huesmann, 1998). Perhaps reactively aggressive children, who have been victimized, engage in higher levels of aggressive
fantasy as a result of their victimization experiences and they therefore have aggressive scripts
more available to them when faced with a provocation. Access to these scripts could contribute
to their aggressive behavior in these provocation situations. If aggressive fantasy is associated
with angry affect, angry affect in response to a provocation also could serve as an internal cue for
the aggressive scripts generated during fantasy. Further research is needed to better understand
the relation between reactive aggression and aggressive fantasy.
Inconsistent with expectations, in the correlation analyses, low anger control was
positively related to proactive and not reactive aggression. Previous research has shown that
physiological measures of anger and observer-rated angry nonverbal behavior have been 72 associated with reactive and not proactive aggression (Hubbard et al., 2002). However, in at least one study (Hubbard et al., 2002), children’s self reports of anger were not associated with reactive aggression but actually were positively correlated with teacher-rated proactive aggression as found in the current study. Potentially, therefore, this inconsistent finding might be a function of the self-report measurement of children’s perceived anger control. Children who enact proactive aggression might perceive themselves as angry, possibly using their behavior as an indicator of anger rather than the physiological cues adults might use (e.g., increased heart rate). Using their behavior as a cue, proactively aggressive children would report low anger control even though they might not experience difficulty controlling their angry affect.
Another potential explanation is that reactively aggressive children are less aware of their affect in general and therefore have a less accurate perception of their anger control. If this were the case, reactively aggressive children’s self-reported anger control would not be consistent with their actual affect and would therefore not be an accurate assessment of their anger control.
Also inconsistent with expectations, several variables that were expected to correlate with proactive aggression (i.e., retaliation beliefs, low negative self evaluation, and low concern for consequences) were found to be related more strongly to reactive aggression. These variables have not been studied in previous research. The current results suggest that children who are reactively aggressive are more likely to have beliefs that support retaliatory aggressive behavior, are less likely to believe that they will feel bad after they use retaliatory aggression, and are less likely to feel concern that they will experience negative consequences following aggressive behavior. Children who are proactively aggressive, on the other hand, do not harbor such beliefs and expectations to the same extent. It is possible that the retaliatory nature of the vignettes used in the measurement of these social-cognitive variables contributed to this unexpected result. 73
With the exception of Aggressive Fantasy, the measures used in this study for the social- cognitive variables use vignettes that describe only retaliatory situations (e.g., “Pretend that a kid trips you in the lunchroom and you drop your tray. You’re thinking about pushing the kid. Some kids would be upset with themselves if they pushed the kid, but other kids would not be upset.
How would you feel about it?” is an item from the Negative Self Evaluation scale). Given that
children who are reactively aggressive are more likely to be victimized by their peers, these
retaliatory situations might hold more salience for them, which could potentially prime them
toward reporting higher levels of the social cognitions. Proactively aggressive children, on the
other hand, are less likely to experience victimization, making the vignettes less salient, therefore
contributing to a lower level of endorsement on the social-cognitive variables.
Hypothesis 4: Reactive and Proactive Aggression Will Have Distinct Profiles of Predictors
In the current study, reactive and proactive aggression had some distinct predictors, but
the hypothesized relations of reactive and proactive aggression with predictors were only
partially supported. As expected, victimization emerged as a unique predictor of reactive and
proactive aggression, but in opposite directions. Furthermore, the pattern of results suggested by
the discriminant function analysis also suggests that victimization is the best variable to classify children who are exclusively reactive aggressive. As discussed in the previous section, this finding is consistent with previous research.
Hostile attribution bias did not predict reactive aggression as expected. Other studies have failed to find an association between reactive aggression and hostile attribution bias measured using self-report surveys. Crick and Dodge (1996) and Dodge et al. (1997) failed to find a difference in self-reported hostile attribution bias across groups of reactively aggressive and proactively aggressive children. However, when videotaped vignettes were used in the 74
measurement of hostile attribution bias in the research conducted by Dodge and Coie (1987) as
well as Dodge, Price, Bacharowski, and Newman (1980), the expected difference in hostile
attribution bias across aggression subtypes was confirmed. It is possible that the videotaped
vignettes are more similar to real-life situations in which hostile attribution biases are enacted,
therefore allowing for the detection of the bias more easily than when written vignettes that children read are used. This measurement difference, then, might account for the lack of discrimination in predicting reactive versus proactive aggression by hostile attribution bias in past research as well as in the current study.
Three out of four of the variables that were included in the social-cognitive composite were expected to predict proactive and not reactive aggression. Based upon the composition of the composite, then, it would be expected to relate more to proactive than reactive aggression.
The current study found, however, that the composite uniquely predicted reactive and not proactive aggression. As discussed earlier, retaliation beliefs, low negative self evaluation, aggressive fantasy, and low concern for consequences have not previously been studied in relation to reactive and proactive aggression. In the current study, the social-cognitive composite was a marginally significant predictor of reactive aggression (p < .10) and did not significantly predict proactive aggression. The measurement of these constructs (i.e., using vignettes about retaliation situations) potentially contributed to these unexpected results. However, in the regression analyses, the beta values were the same for the social-cognitive composite in the prediction of both reactive and proactive aggression (i.e., β = .07), suggesting that the relation of the social-cognitive variables with reactive and proactive aggression is similar in magnitude.
Overall Conclusions 75
Overall, the current study provides at least partial support for the proposed hypotheses.
First, both reactive and proactive aggression were present at a low prevalence in the current
sample of diverse, urban, 5th grade children at a level that is consistent with that found in
previous research. It is important to keep in mind that the levels of reactive aggression and
proactive aggression identified in this sample could underestimate the “true” prevalence of these
behaviors, especially proactive aggression. Second, children who were proactively aggressive in
the current sample also were reactively aggressive. There were no exclusively proactively
aggressive children. Third, reactive and proactive aggression were highly correlated in the
current sample as has been found in other research. As discussed earlier, the discriminant
validity of reactive and proactive aggression is open to question based on this degree of
correlation between the variables. Connor and his colleagues suggested that the high correlation
between reactive and proactive aggression (.60 in their clinical sample) makes it necessary to use
caution in identifying subgroups among clinical samples. They suggest using convergent
evidence across multiple measures of reactive and proactive aggression in the identification of
reactive and proactive subgroups. To further aid in classification, longitudinal research with
“normal” as well as clinical samples that uses multiple measures can contribute to our understanding of reactive and proactive aggression. Also, victimization emerged as a key
distinguishing variable between reactive and proactive aggression in the current study. The
subtypes have opposite relations with victimization such that victimization predicts higher levels
of reactive aggression and lower levels of proactive aggression.
Notably, however, distinct correlation and prediction profiles emerged for reactive and
proactive aggression, which provides some supports for the discriminant validity of these
subtypes. The different associations identified for the subtypes point to different assessment and 76 treatment for reactive and proactive aggression. These finding suggest that it is important to consider the victimization experiences of aggressive children. Some researchers (e.g., Dodge &
Coie, 1987) have suggested that the best interventions for children who are proactively aggressive would target the environmental contingencies related to their aggressive behavior.
This study does support the conclusion that reactive and proactive aggression have important differences, other than peer victimization, that would have implications for their prevention and treatment.
Inconsistent with expectations, anger control related more to proactive aggression in the current sample. The measurement of this construct potentially plays a role in how this variable relates to the subtypes. Specifically, as noted earlier, different results have been found regarding the relation of reactive and proactive aggression with anger control depending on the form of measurement (Hubbard et al., 2002; self-report vs. physiological and observer-rated nonverbal behaviors). Furthermore, retaliation beliefs, low negative self evaluation, and low concern for consequences – all social-cognitive variables – unexpectedly correlated with reactive when proactive aggression was controlled but not the reverse. Because these variables previously have not been explored, the current study suggests that these social cognitions are possibly related more strongly to reactive aggression but additional research will be needed to support this conclusion. However, these results could also be influenced by the biased measurement of these social cognitions using only retaliatory vignettes. Further, it is also possible that there are social cognitions that are related to proactive aggression that were not assessed in the current study.
For example, previous research has shown that children who are proactively aggressive expect positive outcomes following aggression, such as achieving a goal (Crick & Dodge, 1996;
Smithmyer, Hubbard, & Simons, 2000). Perhaps there are differences across different types of 77 outcome expectancies in how they relate to proactive versus reactive aggression. Future research is needed to distinguish between different types of outcome expectancies in relation to reactive and proactive aggression. Such research can potentially lead to the identification of social cognitions that are predictive of proactive aggression.
Beyond outcome expectancies, few social cognitions have been identified as unique predictors of proactive aggression. Future research with proactively aggressive children which explores their thoughts and beliefs about aggression (e.g., interviews, focus groups) could potentially point to relevant social cognitions. Researchers (e.g., Dodge & Coie, 1987) also have suggested that environmental contingencies play an important role in influencing proactively aggressive children. Perhaps children’s perceptions of these contingencies are another potential area of exploration toward better understanding the social cognitions that underlie proactive aggression.
Limitations and Future Directions
The current study was limited by several factors including its correlational design, multicollinearity among the predictor variables, and measurement issues. First, the correlational design of the current study precludes insight into the causal relation among the study variables.
We cannot determine whether any of the predictors cause aggressive behavior or vice versa.
Future longitudinal research is needed to help determine whether social cognitions actually lead to aggressive behavior or vice versa. It is also possible that the relation between cognitions and behavior change over time. Huesmann and Guerra (1997) found that when children were younger (1st and 2nd grade) their behavior influenced their later cognitions, but when they were older (4th and 5th grade) the reverse was true. That is, cognitions predicted behavior only among older children. 78
The multicollinearity present among the social cognitive variables in the current study
complicates the picture obtained regarding the relation of these variables with reactive and proactive aggression. With the exception of hostile attribution bias (possibly due to
measurement issues discussed earlier), the social-cognitive variables were moderately to highly
intercorrelated, with correlations ranging from .38 to .58. The degree of correlation among the
remaining variables was high enough to warrant the use of a composite score to represent
retaliation beliefs, low negative self evaluation, aggressive fantasy, and low concern for consequences in the regression analyses. Because these variables were combined in a composite for the regression analyses, we were unable to parse out the contribution of each of the social
cognitions in the prediction of the subtypes. Multicollinearity among the social-cognitive
variables might well be a phenomenon in “real life.” The reality of this phenomenon, however,
does not diminish the importance of understanding each component social cognition.
Understanding the relation of each cognition to reactive and proactive aggression is a means
toward gaining a more comprehensive understanding of the social-cognitive information
processing associated with reactive and proactive aggression.
A major limitation of many studies that investigate reactive and proactive aggression is
the measurement of the constructs. The most common method of measurement is Dodge and
Coie’s (1987) 6-item teacher-report scale that was used in the present study. The teacher-report
nature of this scale is somewhat problematic due to the motivational and affective distinction
between reactive and proactive aggression. That is, teachers might have limited insight into the
internal states of children (i.e., motivation, affect) which makes it difficult for them to determine
if a child’s aggressive behavior is associated with anger (reactive aggression) or motivation
toward a goal (proactive aggression). Furthermore, the scale is limited in its scope in that it does 79
not identify explicitly different forms of aggression (i.e., physical, verbal, social). Narrowly
defining aggression as only physical or verbal limits how accurately we are capturing the
phenomenon of aggression among children. Recent research has shed light on the prevalence of
social aggression among children and adolescents (e.g., Crick & Grotpeter, 1995), but the current measurement of reactive and proactive aggression (i.e., Dodge & Coie’s 1987 scale) predates this
research and therefore misses an entire subset of aggressive behavior. We need to understand the
prevalence, topography, and correlates of reactive and proactive social aggression in order to
have a more complete understanding of aggression. Because such research is limited (Crain,
2002; Ostrov, 2004; Prinstein & Cillessen, 2003) we do not know much about the differences
across physical, verbal, and social types of reactive and proactive aggression. A more thorough
measurement of reactive and proactive aggression would include assessment of these different
forms of aggression. Finally, the scale is brief, assessing each subtype using only three items.
Although its reliability has been supported, a longer and more thorough scale for reactive and
proactive aggression might broaden the measurement of these constructs. Overall, in order to
move beyond some of these measurement issues, future research that investigates the relation of
social cognitions with reactive and proactive aggression should use multiple informants in the
assessment of reactive and proactive aggression and a more comprehensive scale for measuring
reactive and proactive aggression.
The measurement of social-cognitive variables is another important limitation of the
current study. First, hostile attribution bias was measured using vignettes that the children read rather than videotaped vignettes. Past research has found that the relation of hostile attribution
bias with reactive and proactive aggression was more distinct when videotaped versus survey
vignettes were used. Future research is needed to understand the impact of this measurement 80 issue. Additionally, the measurement of low negative self evaluation and low concern for consequences was limited because only retaliatory vignettes were used. This bias potentially contributed to the unexpected relation of the social-cognitive composite with reactive rather than proactive aggression. Future research should expand on this measurement by including non- retaliatory situations in the measurement of these variables.
Several findings in the current study were unexpected and future research is needed to attempt to replicate them. First, the positive relation of low anger control with proactive aggression in the current study was unexpected. Future research is needed to investigate the role that measurement played in this finding. Specifically, children’s self-perceptions of anger control need to be compared with other measures of anger and should be investigated in relation to reactive and proactive aggression. Different ages and populations (e.g., geographic, racial) should be investigated to determine whether self-perceived anger control varies based on any of these characteristics. As well, research with “normal” and clinical samples is needed to identify potential differences across these samples in the relation between self-rated anger control and the subtypes of aggression. Second, the relation of retaliation beliefs, low negative self evaluation, aggressive fantasy, and low concern for consequences with reactive and proactive aggression needs to be further investigated. Although the current study identified these variables as being more closely related to reactive than proactive aggression, replication of these results is needed.
Finally, longitudinal research is needed to investigate the potential causal relation between social cognitions and reactive and proactive aggression. Specifically, studies that assess whether social cognitions at one time point predict reactive and proactive aggressive behavior at a second time point or vice versa, will add clarity to our understanding of how these constructs both develop independently over time and influence each other over time. 81
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Table 1
Sample Characteristics
Variable Frequency % Valid M SD Range Cohort Fall ‘02 – Spring ’03 (1) 166 49% Fall ‘03 – Spring ’04 (2) 93 27% Spring ‘05 (3) 81 24% Total 340 100% Sex Cohort 1 166 100% Males 75 45% Females 91 55% Cohort 2 93 100% Males 39 42% Females 54 58% Cohort 3 80 99% Males 37 46% Females 43 53% Total 339 99.7% Males 151 44% Females 188 55% Race Cohort 1 148 89% Caucasian 72 43% African American 18 11% Hispanic 16 10% Other/Multiracial 42 25% Cohort 2 93 100% Caucasian 39 42% African American 25 27% Hispanic 12 13% Other/Multiracial 17 18% Cohort 3 80 99% Caucasian 32 40% African American 14 17% Hispanic 9 11% Other/Multiracial 15 31% Total 321 94% Caucasian 143 42% African American 57 17% Hispanic 37 11% Other/Multiracial 84 25% 89
Variable Frequency % Valid M SD Range Age Cohort 1 164 -- 10.67 .72 9 – 13 Cohort 2 93 -- 10.75 .73 9 – 12 Cohort 3 81 -- 10.89 .69 10 – 12 Total 338 -- 10.75 .72 9 – 13 School Cohort 1 166 49% School 1 67 40% School 2 43 26% School 3 56 44% Cohort 2 93 27% School 2 39 42% School 3 54 58% Cohort 3 81 24% School 2 36 44% School 3 45 56% Total 340 100% School 1 67 20% School 2 118 35% School 3 155 45%
90
Table 2
Changes in measures across cohorts
Variable Changes made to survey
Student Victimization Cohort 1 – Cohort 2:
- language of one item changed slightly but not
the meaning
Retaliation Beliefs Cohort 1 - Cohort 2:
- cut 3 items (verbal aggression questions)
- change language for two verbal retaliation
items (scream to yell)
Negative Self Evaluation Cohort 1 - Cohort 2:
- cut 2 items (afraid of hurting the other kid,
afraid of causing the other kid to lose friends)
- language of items and responses options
changed (e.g., “Very sure I would be upset
with myself” to “A lot”)
Caring About Consequences Cohort 1 - Cohort 2:
- cut 1 item (care about staying safe and
avoiding a fight);
- number of response options increased from
three to four
- language changed in scenario (shortened) 91
Variable Changes made to survey
Caring About Consequences Cohort 1 - Cohort 2 (continued):
(continued) - language changed in items (re-identified the
target in the question; changed “how much
would you care” to “would you care”)
- language changed in responses (e.g., “I would
not care at all” to “not at all”)
Cohort 2 - Cohort 3:
- added 3 items (teacher might not trust you,
parents might not trust you, might get picked
on by other kids)
- language of one item changed slightly but not
the meaning
Aggressive Fantasy Cohort 1 - Cohort 2:
- emphasis on incidence to frequency (“do you”
to “how often do you”)
- response option increased from 3 to 4
- language of response options (e.g., “no” to
“never” 92
Table 3
Means, Standard Deviations, and Ranges of Study Variables
Variable N M SD Range Reactive Aggression 340 1.37 1.25 0 – 4 Proactive Aggression 340 .49 .80 0 – 4 Low Anger Control 166 2.42 .78 1 – 4 Victimization 340 1.14 .82 0 – 3 Hostile Attribution Bias 338 .50 .22 0 – 1 Retaliation Beliefs 340 .97 .72 0 – 3 Low Negative Self Evaluation 338 1.40 .89 0 – 3 Aggressive Fantasy – standardized 339 .00 1.00 -1.97 – 2.69 Low Concern for Consequences – 339 .00 1.00 -1.13 – 2.28 standardized
Table 4 Intercorrelations Among Study Variables
Variable 1 2 3 4 5 6 7 8 9 1. Reactive Aggression -- 2. Proactive Aggression .74** -- 3. Victimization .14** .02 -- 4. Low Anger Control .13 .18* .02 -- 5. Hostile Attribution Bias .13* .12* .03 -.10 -- 6. Retaliation Beliefs .26** .23** .04 .36** .08 -- 7. Low Negative Self Evaluation .29** .27** -.02 .39** .22** .57** -- 8. Aggressive Fantasy .24** .19** .21** .39** .18** .53** .53** -- 9. Low Concern for Consequences .18** .15** -.08 .25** .14* .38** .58** .38** --
*p<.05. **p<.01.
93 94
Table 5
Factor Analysis with All Social-Cognitive Predictors
Variable Factor Factor loading loading Factor loading Hostile Attribution Bias .00 1.0 Retaliation Beliefs .73 .01 Low Negative Self Evaluation .76 .06 Aggressive Fantasy .82 -.14 Low Concern for Consequences .84 .08 % variance 51.26% 19.02% Eigenvalue 2.56 .95
Table 6 Analysis of Variance Test for Sex, Race, and School Differences for Study Variables
Sex Race School Variable λ; F (df); M(SD) λ; F (df); M(SD) λ; F (df); M(SD) Multivariate 2.96 (8, 291)** .95 (24, 844.59) 3.01 (16, 582)**
Between subjects Male Female Caucasian Af. Amer. Hispanic Other School 1 School 2 School 3 Reactive Aggression F (df) 7.60 (1, 298)** .83 (3, 298) 15.35 (2, 298)** M(SD) 1.39(.14) .89(.12) 1.21(.10) 1.10(.26) .91(.25) 1.33(.14) .38(.24) 1.27(.13) 1.76(.10) Proactive Aggression F (df) 3.85 (1, 298) † 1.11 (3, 298) 6.25 (2, 298)** M(SD) .55(.09) .31(.08) .33(.07) .34(.18) .53(.17) .51(.09) .06(.16) .54(.09) .68(.07) Victimization F (df) .13 (1, 298) 2.05 (3, 298) .52 (2, 298) M(SD) 1.06(.10) 1.10(.09) 1.26(.07) 1.01(.19) .84(.18) 1.20(.10) 1.06(.17) 1.15(.10) 1.02(.07) Hostile Attribution Bias F (df) .00 (1, 298) .76 (3, 298) .47 (2, 298) M(SD) .50(.03) .50(.02) .50(.02) .54(.05) .50(.05) .46(.03) .47(.05) .52(.03) .50(.02) Retaliation Beliefs F (df) 14.62 (1, 298)** .47 (3, 298) 2.52 (2, 298) † M(SD) 1.18(.08) .77(.07) .90(.06) 1.02(.15) .97(.15) 1.00(.08) .79(.14) 1.14(.08) .99(.06) Low Negative Self Evaluation F (df) 11.76 (1, 298)** .57 (3, 298) 2.69 (2, 298) † M(SD) 1.65(.11) 1.19(.09) 1.32(.07) 1.57(.20) 1.37(.18) 1.40(.10) 1.19(.18) 1.63(.10) 1.43(.08) Aggressive Fantasy – standardized F (df) 10.79 (1, 298)** .77 (3, 298) 1.30 (2, 298) M(SD) .25(.12) -.24(.10) .-.02(.08) .24(.22) -.22(.21) .02(.12) -.04(.20) .14(.11) -.09(.09) Low Concern for Consequences – standardized F (df) 4.46 (1, 298)* .12 (3, 298) .95 (2, 298) M(SD) .10(.12) -.22(.11) -.07(.09) -.03(.22) -.13(.21) .00(.12) -.23(.20) -.01(.11) .07(.09)
†p < .10. *p < .05. **p < .01. 95 96
Table 7
Analysis of Variance Test for Cohort Differences for Study Variables with Common Measurement
Cohort Variable λ; F (df) Multivariate 1.70 (8, 335) † Between subjects Reactive Aggression 1.15 (2, 335) Proactive Aggression .01 (2, 335) Victimization 1.79 (2, 335) Hostile Attribution Bias 2.07 (2, 335)
Table 8
Analysis of Variance Test for Time of Data Collection Differences for Study Variables
Data Collection Variable λ; F (df); M(SD) Multivariate .07 (8, 326)**
Between subjects Fall Spring Reactive Aggression F (df) 15.50 (1, 333)** M(SD) 1.12(1.32) 1.55(1.17) Proactive Aggression F (df) 2.51 (1, 333)* M(SD) .39(.80) .56,(79) Victimization F (df) .52 (1, 333) M(SD) 1.18(.82) 1.10(.82) Hostile Attribution Bias F (df) .00 (1, 333) M(SD) .50(.21) .49(.23) Retaliation Beliefs F (df) .32 (1, 333) M(SD) .93(.74) .99(.70) Low Negative Self Evaluation F (df) .57 (1, 333) M(SD) 1.35(.85) 1.43(.92) Aggressive Fantasy – standardized F (df) 5.00 (1, 333)* M(SD) .14(.97) -.10(1.01) Low Concern for Consequences – standardized F (df) .01 (1, 333) M(SD) -.02(1.00) .00(.99)
*p<.05. **p<.01.
Table 9
Descriptive Statistics for Reactive and Proactive Aggression
Percentiles M SD Variance t 25th 50th 75th Total sample Reactive Aggression 1.37 1.25 1.56 19.22** .00 1.00 2.33 Proactive Aggression .49 .80 .63 .00 .00 1.00 Boys Reactive Aggression 1.65 1.22 1.5 15.64** .67 1.67 2.33 Proactive Aggression .59 .86 .74 .00 .00 1.00 Girls Reactive Aggression 1.13 1.22 1.50 12.12** .00 .67 2.00 Proactive Aggression .40 .73 .54 .00 .00 .33 **p < .01. 97
Table 10
Descriptive Statistics for Extreme Groups
Range M SD N % Sample Reactive Proactive Reactive Proactive Reactive Proactive Nonaggressive 92 27% 0 – 0 0 – 0 .00 .00 .00 .00 Pervasive aggressive 71 21% 2.33 – 4 1 – 4 3.15 1.71 .57 .78 Reactive aggressive 67 20% 1 – 3.33 0 – 0 1.62 .00 .55 .00
98 99
Table 11
Demographics for Extreme Groups
N % M SD Range Age Nonaggressive 91 -- 10.55 .64 9 – 12 Pervasive aggressive 70 -- 11.00 .77 9 – 12 Reactive aggressive 67 -- 10.67 .73 9 – 12 Sex Nonaggressive Males 23 25% Females 69 75% Pervasive aggressive Males 36 51% Females 34 48% Reactive aggressive Males 39 58% Females 28 42% Race Nonaggressive Caucasian 38 41% African American 13 14% Hispanic 15 16% Other/Multiracial 21 23% Pervasive aggressive Caucasian 24 34% African American 17 24% Hispanic 11 16% Other/Multiracial 17 24% Reactive aggressive Caucasian 38 57% African American 11 16% Hispanic 3 5% Other/Multiracial 11 16% School Nonaggressive School 1 31 34% School 2 42 46% School 3 19 21% Pervasive aggressive School 1 1 1% School 2 23 32% School 3 47 66% Reactive aggressive School 1 13 19% School 2 14 21% School 3 40 60% 100
Table 12
Chi-Square Test for Extreme Group Differences in Sex, Race, School, and Cohort
Non-Aggressive Reactive Aggressive Pervasive Aggressive Sex Male 23.5% 39.8% 36.7% Female 52.7% 21.4% 26% Race Caucasian 38% 38% 24% African American 31.7% 26.8% 41.5% Hispanic 51.7% 10.3% 37.9% Other 42.9% 22.4% 34.7% School School 1 68.9% 28.9% 2.2% School 2 53.2% 17.7% 29.1% School 3 17.9% 37.7% 44.3% Cohort Cohort 1 45.2% 27% 27.8% Cohort 2 31.7% 28.6% 39.7% Cohort 3 38.5% 34.6% 26.9%
101
Table 13
Partial Correlations of Criterion Variables with Predictors Controlling for Opposing Subtype of Aggression
Variable Reactive Aggression Proactive Aggression t(df) Victimization .19** -.13* 9.25(335)** Low Anger Control -.01 .13† - Hostile Attribution Bias .05 .04 - Retaliation Beliefs .14* .05 - Low Negative Self Evaluation .15** .08 - Aggressive Fantasy .14* .02 - Low Concern for Consequences .11† .03 - Social-Cognitive Composite .17** .06
†p < .10. *p<.05. **p<.01.
Table 14
Hierarchical Regressions of Victimization and Social-Cognitive Variables Predicting Reactive and Proactive Aggression
Reactive Aggression Proactive Aggression Reactive Aggression Proactive Aggression (Proactive controlled) (Reactive controlled) Predictors ΔR2 β ΔR2 β ΔR2 β ΔR2 β Step 1: Controlled variables .16** .06** .60** .56** Sex .21** .12* .12** -.04 School .33** .22** .18** -.04 Proactive Aggression ------.69** -- -- Reactive Aggression ------.77** F value for step 30.69 (2, 334) 11.27 (2, 334) 169.14 (3, 333) 141.62 (3, 333)
Step 2: Predictor variables .08** .06** .02** .01* Victimization .15** .03 .14** -.09* Hostile Attribution Bias .08 .08 .03 .02 Social-Cognitive Composite .22** .23** .07† .07 F value for step 12.17 (3, 331) 7.80 (3, 331) 7.24 (3, 330) 3.05 (3, 330)
Note. Sex coded as 0 = Female, 1 = Male. School codes increase with levels of aggression. All β values are standardized coefficients
†p < .10. *p < .05. **p < .01. 102 103
Table 15
Discriminant Functions for Extreme Groups
Function 1 Function 2 Discriminant function coefficients Victimization .23 .96 Hostile Attribution Bias .28 -.31 Social-Cognitive Composite .87 -.16 Eigenvalue .15 .01 % variance 92% 8% Chi-square (df) 33.91 (6)** 2.78 (2) Canonical correlation .36 .11 Functions at group means Nonaggressive group -.36 -.09 Reactive aggressive group -.09 .17 Pervasive aggressive group .55 -.05
Note. Prior probabilities were calculated taking group size into account.
% correctly classified: 52.2%.
104
APPENDIX A
Parent Consent Form
Dear Parents:
During the past few years, as part of our fifth grade curriculum, we have included a special section on managing problems with peers. This program consists of lessons focused on: improving student relationships with classmates, understanding one's own and others' emotions, building problem-solving skills, reducing aggressive behavior by managing anger, and encouraging positive behavior to avoid conflicts. This year, staff members from Bowling Green State University are helping us improve the program by including material from programs that have been successful across the country.
The program's 12 sessions are part of the classroom curriculum, and are carried out in the classroom during the school day once a week for 60 minutes by BGSU staff who developed the program. There are educational discussions, readings, and activities that have been adapted from similar educational programs. Children will also be asked to fill out a survey before and after the sessions to see if they learned and enjoyed the material. The teacher will also complete a brief survey about the children’s behavior. This research will help us evaluate how successful the program is. The survey will ask questions about children’s: beliefs about positive and aggressive behavior, own experiences with positive and aggressive behavior, and opinions about positive ways to solve problems with peers.
On all surveys, children's names will be on a removable sticker that the students will remove and throw away once they receive their survey. From that time, only special code numbers will be used to indicate who completed the survey. The completed surveys will never have the names of the children on them. Students will be told NOT to write their names anywhere on the survey and will NOT be asked ANY PERSONAL INFORMATION ABOUT THEIR FAMILIES. We are interested in whether children as a group learn and enjoy the program's material.
We are excited about continuing this part of our curriculum. If for any reason you do not want your child to participate in either the program or the survey, please let us know by returning the attached form to your child’s teacher. Children who do not participate will be given other activities by their teachers. If you have any questions, please call the principal, XXX, at XXXXX Elementary School at (419) XXX-XXXX, or Eric Dubow at Bowling Green State University at (419) 372-2556. In addition, if you have any concerns about the program you may contact the Chair of the Human Subjects Review Board at BGSU at (419) 372-7716.
If you do not want your child to participate in this program, you must return the attached form to your child’s teacher by XX, October XX, 2003.
Sincerely, XXX XXXXX, Principal, XXX Elementary School 105
Please return this form only if you do not want your child to participate in the program or the survey.
I have read the letter about the program being conducted by staff from Bowling Green State
University at XXXXXX Elementary School.
______I do not want my child to participate in the program on managing problems with peers and finding peaceful ways to resolve conflicts.
______I do not want my child’s survey responses to be included in the research that will study the effectiveness of the program.
Child’s name (please print): ______
Child’s grade: ______
Child’s teacher:______
Signature of parent or legal guardian:______
Thank you again for your time.
106
APPENDIX B
Child Assent Form
Bowling Green State University is working with the Toledo Public Schools to find out things about how students get along with each other, and what they think about things like conflict and bullying that students do. We hope that the things we learn from you will help us to improve programs in your schools. We need YOUR help to learn about these things! Your opinions are VERY IMPORTANT to us. We would like you to fill out a survey so you can share your thoughts and opinions.
You DO NOT have to fill it out if you don’t want to. If you start, and then change your mind, you can stop. If you do fill it out, your responses will be PRIVATE and CONFIDENTIAL. This means that no one will be able to know what you wrote. So, you will not write your name on the survey anywhere. Just write your name on this sheet then tear it off and give it back to us. This sheet is your own permission form for doing the survey.
Then, we want you to take off the sticker that has your name on it and we will throw it away. That way your name will not be on the survey anywhere.
Please make a check mark in the space below that shows if you choose to fill out the survey:
After listening to the person from Bowling Green State University,
______I want
______I do not want
to fill out the survey.
Name: ______
107
APPENDIX C
Teacher Letter and Consent Form
[Insert date]
Dear teacher,
As you know, we are working on a project to assess and reduce rates of aggression through classroom based intervention. We have collected information from the students about their behaviors and thoughts relating to aggression. This is very valuable information, but may not give us a complete picture of classroom climate. We would also like to gain a better understanding of the teachers’ perspectives on these issues.
Therefore, we are asking you to complete this brief survey about your class. We will ask you to complete this survey again in a few months and at the end of the year so that we can discover whether things have changed in your classroom or if they have stayed about the same. It will also help us to determine what kinds of issues you have in your classroom so we can adjust our intervention to address those specific issues. This survey asks about general levels of aggressive behavior in your classroom. It does not ask you for information on individual students. This survey should take about 15-20 minutes for you to complete. You can give the completed survey back to the graduate student from Bowling Green State University who gave it to you.
We also ask that you complete a brief survey that reflects the behaviors of the individual students in your class. Each survey has a removable label with the student’s name. Please remove and throw away the label from each survey once you have completed it. If you start to complete a survey but need to stop in the middle of completing it, please keep the survey in a place where nobody can see it. We want to protect the privacy of the students by ensuring that nobody has access to partially-completed surveys that still have their name label attached. This survey takes approximately 3 minutes per student to complete. An addressed, stamped envelope is included for you to return this first set of surveys to us in the next week. We will also ask you to complete these surveys again in a few months and at the end of the year. At the end of the year, we will provide you with $100 to compensate for the extra time you took to complete these surveys.
Your responses on all of these surveys will be completely confidential. We will not share individual responses with any other teachers or with your school administration. Using a special code number, we will match the survey you complete now to the others you complete to examine change over time. We will also match your responses for individual students to their individual surveys using a special code. School-level results will be presented to school administrators. 108
Your completion of this survey will help us to improve the programming that we are able to offer to your school.
If you have any questions or concerns about this project, you can contact either Eric Dubow, Ph.D. (419) 372-2556 or Dara Musher-Eizenman, Ph.D. (419)-372-2948, or the Chair of the Human Subjects Review Board at BGSU (419) 372-7716.
Your participation will be greatly appreciated!
Sincerely, Eric Dubow Dara Musher-Eizenman Department of Psychology, BGSU
If you agree to complete the survey, please sign below. Tear off this page and return it along with your completed survey. You can keep the letter for your records.
I agree to participate in the survey on student aggression. ______(sign here) 109
APPENDIX D
Victimization: Cohort 1
These next questions ask about things you might have SEEN, HEARD, or EXPERIENCED during the last school year. For each one, circle the answer that shows how often you have seen, heard, or experienced each thing.
How often have you been hit or pushed by someone at school?
Never (0) One time (1) A few times (2) A lot of times (3)
How often have other students said mean things to you at school?
Never (0) One time (1) A few times (2) A lot of times (3)
How often does it happen that other kids won’t let you join in what they’re doing?
Never (0) One time (1) A few times (2) A lot of times (3)
How often has it happened that no one would talk to you at school?
Never (0) One time (1) A few times (2) A lot of times (3)
How often have you had rumors spread about you at school?
Never (0) One time (1) A few times (2) A lot of times (3)
How often has someone threatened to hurt you at school?
Never (0) One time (1) A few times (2) A lot of times (3)
110
Victimization: Cohorts 2 & 3
These next questions ask about things that might have happened to you during this school year. For each one, circle the choice that tells how often it happened to you.
How often have you been hit or pushed by someone at school?
Never (0) One time (1) A few times (2) A lot of times (3)
How often have other students said mean things to you at school?
Never (0) One time (1) A few times (2) A lot of times (3)
How often has it happened, that other kids at school won’t let you join in what they’re doing?
Never (0) One time (1) A few times (2) A lot of times (3)
How often have you been ignored or avoided at school?
Never (0) One time (1) A few times (2) A lot of times (3)
How often have you had rumors spread about you at school?
Never (0) One time (1) A few times (2) A lot of times (3)
How often has someone at school threatened to hurt you?
Never (0) One time (1) A few times (2) A lot of times (3)
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APPENDIX E
Anger Control: Cohort 1
Circle the answer that says how often you do the following things, or how often each one is true for you.
I can stop myself from losing my temper.
Not at all (4) Sometimes (3) Most of the time (2) All the time (1)
I can control my angry feelings.
Not at all (4) Sometimes (3) Most of the time (2) All the time (1)
I can do things to calm down.
Not at all (4) Sometimes (3) Most of the time (2) All the time (1)
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APPENDIX F
Hostile Attribution Bias: Cohorts 1, 2, & 3
Now, I’m going to read a few situations that might happen at school. Some of these things might never have happened to you before, but we would like you to pretend that you are in the situation and then answer the questions about it.
Pretend that you are standing in the gym and you get hit hard in the back by a ball thrown by another kid. Why do you think this happened? CIRCLE ONLY ONE CHOICE.
a. On purpose. (0) b. By accident. (1)
Was the kid trying to be mean? CIRCLE ONLY ONE CHOICE.
a. Yes. (0) b. No. (1)
Pretend that you are walking down the hall on your way to class. A kid bumps into you and your books fall on the floor. Why do you think this happened? CIRCLE ONLY ONE CHOICE.
a. On purpose. (0) b. By accident. (1)
Was the kid trying to be mean? CIRCLE ONLY ONE CHOICE.
a. Yes. (0) b. No. (1)
Pretend that you are walking in the lunchroom and when you pass a group of kids, they start to laugh. Why do you think this happened? CIRCLE ONLY ONE CHOICE.
a. On purpose. (0) b. By accident. (1)
Were the kids trying to be mean? CIRCLE ONLY ONE CHOICE.
a. Yes. (0) b. No. (1)
Pretend that a friend of yours is having a party and everyone you know got an invitation in the mail except for you. Why do you think this happened? CIRCLE ONLY ONE CHOICE.
a. On purpose. (0) b. By accident. (1)
Was your friend trying to be mean? CIRCLE ONLY ONE CHOICE.
a. Yes. (0) b. No. (1) 113
APPENDIX G
Retaliation Beliefs: Cohort 1
The next questions ask you about whether you think certain behaviors are WRONG or are OK. Circle the answer that best describes what you think.
Pretend one kid says something bad to a second kid.
Do you think it’s OK for the second kid to scream at the first kid? It’s perfectly OK (4) It’s sort of OK (3) It’s sort of wrong (2) It’s really wrong (1)
Do you think it’s OK for the second kid to hit the first kid? It’s perfectly OK (4) It’s sort of OK (3) It’s sort of wrong (2) It’s really wrong (1)
Do you think it’s OK for the second kid to get others not to like the first kid? It’s perfectly OK (4) It’s sort of OK (3) It’s sort of wrong (2) It’s really wrong (1)
Pretend one kid hits a second kid.
Do you think it’s OK for the second kid to scream at the first kid? It’s perfectly OK (4) It’s sort of OK (3) It’s sort of wrong (2) It’s really wrong (1)
Do you think it’s OK for the second kid to hit the first kid back? It’s perfectly OK (4) It’s sort of OK (3) It’s sort of wrong (2) It’s really wrong (1)
Do you think it’s OK for the second kid to get others not to like the first kid? It’s perfectly OK (4) It’s sort of OK (3) It’s sort of wrong (2) It’s really wrong (1)
Pretend one kid gets others not to like a second kid.
Do you think it’s OK for the second kid to scream at the first kid? It’s perfectly OK (4) It’s sort of OK (3) It’s sort of wrong (2) It’s really wrong (1)
Do you think it’s OK for the second kid to hit the first kid? It’s perfectly OK (4) It’s sort of OK (3) It’s sort of wrong (2) It’s really wrong (1)
Do you think it’s OK for the second kid to get others not to like the first kid? It’s perfectly OK (4) It’s sort of OK (3) It’s sort of wrong (2) It’s really wrong (1) 114
Retaliation Beliefs: Cohort 2 &3
The next questions ask about whether you think certain behaviors are WRONG or are OK. Circle the choice that best describes what you think.
Pretend one kid hits a second kid. Do you think it’s OK for the second kid to yell at the first kid?
It’s perfectly OK (0) It’s sort of OK (1) It’s sort of wrong (2) It’s really wrong (3)
Do you think it’s OK for the second kid to hit the first kid back?
It’s perfectly OK (0) It’s sort of OK (1) It’s sort of wrong (2) It’s really wrong (3)
Do you think it’s OK for the second kid to get others not to like the first kid?
It’s perfectly OK (0) It’s sort of OK (1) It’s sort of wrong (2) It’s really wrong (3)
Pretend one kid gets others not to like a second kid. Do you think it’s OK for the second kid to yell at the first kid?
It’s perfectly OK (0) It’s sort of OK (1) It’s sort of wrong (2) It’s really wrong (3)
Do you think it’s OK for the second kid to hit the first kid?
It’s perfectly OK (0) It’s sort of OK (1) It’s sort of wrong (2) It’s really wrong (3)
Do you think it’s OK for the second kid to get others not to like the first kid?
It’s perfectly OK (0) It’s sort of OK (1) It’s sort of wrong (2) It’s really wrong (3) 115
APPENDIX H
Negative Self Evaluation: Cohort 1
Now, I’m going to read you a few situations that might happen at school. Some of these things may never have happened to you before, but we would like you to pretend that you are in the situation and then answer the questions about it.
Pretend that a kid trips you in the lunchroom and you drop your tray. You’re thinking about pushing the kid.
Some kids would be upset with themselves if they pushed the kid, but other kids would not be upset. How would you feel about it?
a. Very sure I b. Pretty sure I c. Pretty sure I d. Very sure I WOULD be upset WOULD be upset WOULD NOT be WOULD NOT be with myself. (1) with myself. (2) upset with myself. (3) upset with myself. (4)
Some kids would feel guilty if they pushed the kid, but other kids would not feel guilty. How would you feel about it?
a. Very sure I b. Pretty sure I c. Pretty sure I d. Very sure I WOULD feel guilty. WOULD feel guilty. WOULD NOT feel WOULD NOT feel (1) (2) guilty (3) guilty. (4)
If you push the kid, the kid’s feelings might be hurt. Some kids would be afraid of hurting the kid’s feelings, but other kids would not be afraid. How would you feel about it?
a. Very sure I b. Pretty sure I c. Pretty sure I d. Very sure I WOULD be afraid of WOULD be afraid of WOULD NOT be WOULD NOT be hurting the kid’s hurting the kid’s afraid of hurting the afraid of hurting the feelings. (1) feelings. (2) kid’s feelings. (3) kid’s feelings. (4)
If you pushed the kid, the kid might get really hurt. Some kids would be afraid of hurting the kid, but other kids would not be afraid. How would you feel about it?
a. Very sure I b. Pretty sure I c. Pretty sure I d. Very sure I WOULD be afraid of WOULD be afraid of WOULD NOT be WOULD NOT be hurting the kid. (1) hurting the kid. (2) afraid of hurting the afraid of hurting the kid. (3) kid. (4)
116
Pretend that a kid says something mean to you. You’re thinking about spreading a rumor about the kid.
Some kids would be upset with themselves if they spread a rumor, but other kids would not be upset. How would you feel about it?
a. Very sure I b. Pretty sure I c. Pretty sure I d. Very sure I WOULD be upset WOULD be upset WOULD NOT be WOULD NOT be with myself. (1) with myself. (2) upset with myself. (3) upset with myself. (4)
Some kids would feel guilty if they spread a rumor, but other kids would not feel guilty. How would you feel about it?
a. Very sure I b. Pretty sure I c. Pretty sure I d. Very sure I WOULD feel guilty. WOULD feel guilty. WOULD NOT feel WOULD NOT feel (1) (2) guilty (3) guilty. (4)
If you spread a rumor, the kid’s feelings might be hurt. Some kids would be afraid of hurting the kid’s feelings, but other kids would not be afraid. How would you feel about it?
a. Very sure I b. Pretty sure I c. Pretty sure I d. Very sure I WOULD be afraid of WOULD be afraid of WOULD NOT be WOULD NOT be hurting the kid’s hurting the kid’s afraid of hurting the afraid of hurting the feelings. (1) feelings. (2) kid’s feelings. (3) kid’s feelings. (4)
If you spread a rumor, the kid might lose some friends. Some kids would be afraid of causing this, but other kids would not be afraid. How would you feel about it?
a. Very sure I b. Pretty sure I c. Pretty sure I d. Very sure I WOULD be afraid of WOULD be afraid of WOULD NOT be WOULD NOT be hurting the kid. (1) hurting the kid. (2) afraid of hurting the afraid of hurting the kid. (3) kid. (4) 117
Negative Self Evaluation: Cohorts 2 & 3
Now, I am going to read a few situations that might happen between you and another person. This situation might never have happened to you before, but we would like you to pretend that you are in this situation and then answer the questions about it.
Pretend that a kid trips you in the lunchroom and you drop your tray. You’re thinking about pushing the kid. Some kids would be upset with themselves if they pushed the kid, but other kids would not. Would you be upset with yourself if you pushed the kid?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
Some kids would feel guilty if they pushed the kid, but other kids would not. Would you feel guilty if you pushed the kid?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
If you push the kid, the kid’s feelings might be hurt. Some kids would be afraid of hurting the kid’s feelings, but other kids would not. Would you be afraid of hurting the kid’s feelings if you pushed them?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
Pretend that a kid says something mean to you. You’re thinking about spreading a rumor about the kid.
Some kids would be upset with themselves if they spread a rumor, but other kids would not. Would you be upset with yourself if you spread a rumor?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
Some kids would feel guilty if they spread a rumor, but other kids would not. Would you feel guilty if you spread a rumor?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
If you spread a rumor, the kid’s feelings might be hurt. Some kids would be afraid of hurting the kid’s feelings, but other kids would not. Would you be afraid of hurting the kid’s feelings if you spread a rumor?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
118
APPENDIX I
Concern for Consequences: Cohort 1
Now, I am going to read you a situation that might happen between you and another person. This situation may never have happened to you before, but we would like you to pretend that you are in this situation and then answer the questions about it.
Pretend that another kid picks on you and starts to push you around. You have to decide what to do. You want to get even with the kid but there are some things you want to decide about first. Some kids care about these things, but other kids don’t. HOW MUCH WOULD YOU CARE IF THESE THINGS HAPPENED when you decide if you are going to get even with the kid?
If you do something to get even, you might get into trouble at school. How much would you care if this happened?
I would I would I would NOT CARE AT ALL (3) CARE A LITTLE BIT (2) REALLY CARE! (1)
If you do something to get even, you might get into trouble at home. How much would you care if this happened?
I would I would I would NOT CARE AT ALL (3) CARE A LITTLE BIT (2) REALLY CARE! (1)
Some kids might want to look strong and not back down in front of their friends in this situation. How much would you care about looking strong and not backing down?
I would I would I would NOT CARE AT ALL (1) CARE A LITTLE BIT (2) REALLY CARE! (3)
Some kids might want to stay safe and avoid a fight in this situation. How much would you care about staying safe and avoiding a fight?
I would I would I would NOT CARE AT ALL (3) CARE A LITTLE BIT (2) REALLY CARE! (1)
119
Concern for Consequences: Cohort 2
Now, I am going to read a few situations that might happen between you and another person. This situation might never have happened to you before, but we would like you to pretend that you are in this situation and then answer the questions about it.
Pretend that a kid picks on you and starts to push you around. You have to decide what to do. You want to get even with the kid but there are some things you want to decide about first.
If you do something to get even with the kid who is pushing you around, you might get into trouble at school. Would you care if this happened?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
If you do something to get even with the kid who is pushing you around, you might get into trouble at home. Would you care if this happened?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
Some kids might want to look strong and not back down in front of their friends when they get pushed around. Would you care about looking strong and not backing down?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
120
Concern for Consequences: Cohort 3
Now, I am going to read a few situations that might happen between you and another person. This situation might never have happened to you before, but we would like you to pretend that you are in this situation and then answer the questions about it.
Pretend that a kid picks on you and starts to push you around. You have to decide what to do. You want to get even with the kid but there are some things you want to decide about first.
If you do something to get even with the kid who is pushing you around, you might get into trouble at school. Would you care if this happened?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
If you do something to get even with the kid who is pushing you around, your teacher might not trust you. Would you care if this happened?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
If you do something to get even with the kid who is pushing you around, you might get into trouble at home. Would you care if this happened?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
If you do something to get even with the kid who is pushing you around, your parents might not trust you. Would you care if this happened?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
If you don’t do something to get even with the kid who is pushing you around, other kids might think you are weak. Would you care if this happened?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
If you don’t do something to get even with the kid who is pushing you around, you might get picked on by other kids. Would you care if this happened?
Not at all (0) A little (1) Quite a bit (2) A lot (3)
121
APPENDIX J
Aggressive Fantasy: Cohort 1
Circle the answer that says how much you think each sentence is true, or how often you think about or do the things in the sentence.
Do you think about getting other kids to not like a certain kid that you don’t like?
No (0) Sometimes (1) A lot (2)
Do you play games where you pretend to fight with somebody?
No (0) Sometimes (1) A lot (2)
Do you think about hitting or hurting somebody that you don’t like?
No (0) Sometimes (1) A lot (2)
Do you play games where you pretend to use a gun or other weapon on somebody?
No (0) Sometimes (1) A lot (2)
Do you think about having a party and inviting everyone except one kid that you don’t like?
No (0) Sometimes (1) A lot (2)
When you get mad, sometimes, do you daydream about the things you would like to do to the person you are mad at – like hitting, damaging their property, or getting them into trouble?
No (0) Sometimes (1) A lot (2)
When you’re playing, do you sometimes act out scenes from TV shows or movies where kids fight each other?
No (0) Sometimes (1) A lot (2)
122
Aggressive Fantasy: Cohorts 2 & 3
Circle the choice that tells how often you do each thing in the sentence.
How often do you play games where you pretend to fight with somebody?
Never (0) Hardly Ever (1) Sometimes (2) A lot (3)
How often do you think about getting other kids not to like a certain kid that you don’t like?
Never (0) Hardly Ever (1) Sometimes (2) A lot (3)
How often do you think about hitting or hurting somebody that you don’t like?
Never (0) Hardly Ever (1) Sometimes (2) A lot (3)
How often do you play games where you pretend to use a gun or other weapon on somebody?
Never (0) Hardly Ever (1) Sometimes (2) A lot (3)
How often do you think about having a party and inviting everyone except one kid that you don’t like?
Never (0) Hardly Ever (1) Sometimes (2) A lot (3)
When you get mad, how often do you daydream about the things you would like to do to the person you are mad at – like hitting, damaging their property, or getting them into trouble?
Never (0) Hardly Ever (1) Sometimes (2) A lot (3)
When you’re playing, how often do you act out scenes from TV shows or movies where kids fight each other?
Never (0) Hardly Ever (1) Sometimes (2) A lot (3)
123
APPENDIX K
Reactive and Proactive Aggression: Cohorts 1, 2, & 3
Mark the box that indicates HOW OFTEN this student exhibits the behavior below.
(0) (1) (2) (3) (4) When the child is teased or threatened, he or Almost Never Rarely Sometimes Usually she gets angry easily. Always The child claims that other children are to Almost blame in a fight and feels that they started Never Rarely Sometimes Usually Always the trouble. When a peer accidentally hurts the child (such as bumping into him or her), this child Almost Never Rarely Sometimes Usually assumes that the peer meant to do it, and Always overreacts with anger/fighting. This child gets other kids to gang up on a peer Almost Never Rarely Sometimes Usually that he or she does not like. Always This child uses physical force (or threatens to Almost Never Rarely Sometimes Usually use force) in order to dominate other kids. Always This child threatens or bullies others in order Almost Never Rarely Sometimes Usually to get his or her way. Always