Relational Aggression: a Review and Conceptualization

RELATIONAL AGGRESSION: A REVIEW AND CONCEPTUALIZATION

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

Kristine Hirai Saket, M.A.

* * * * *

The Ohio State University 2005

Dissertation Committee: Approved by Professor Michael Vasey, Adviser

Professor Steven Beck ______Adviser Professor John Gibbs Psychology Graduate Program

ABSTRACT

Relational aggression is a pivotal concept associated with the trend toward increasing attention toward aggression in girls. Specifically, acts of relationship manipulation are hypothesized to be especially salient for girls, who are expected to covet social acceptance and inclusion more than boys. Past studies have supported a distinction between relational and physical forms of aggression and identified unique correlates of relational aggression. However, characteristics of relational aggression have been largely examined in isolation, thereby providing only indirect information about the nomological network of relational aggression.

The present study adopted a construct validation approach to replicating and expanding upon existing research. Two hundred twenty-four children, aged 11 to 14, completed measures of relational, reactive, and proactive aggression as well as various other behaviors. One parent and one teacher per child completed parallel measures.

At the manifest variable level, hierarchical regression analyses, which were conducted separately for child, parent, and teacher reports, varied across informants.

Overall, these analyses suggested that relational aggression is both uniquely reactive and uniquely proactive in nature. Consistent with previous research, relational aggression was associated with such harmful correlates as social and internalizing problems. In

ii many cases, such associations were mediated by either reactive or proactive aggression.

Moreover, although there were generally no gender differences in mean levels of relational aggression, there was some suggestion that the implications of relational aggression may differ for boys and girls.

At the latent variable level, a multitrait-multimethod approach was used to model associations between relevant constructs. Overall, these analyses provided consistent support for the convergent validity of all relevant constructs. However, there was less evidence for the discriminant validity of relational, reactive, and proactive aggression, suggesting that the distinguishing features of these hypothesized aggression subtypes may become obscured when combining multiple informants’ perspectives. Method effects were identified for parent and child reports.

Differences between results at the manifest and latent variable levels highlighted potential limitations of combining multiple reports. As direct tests of fundamental assumptions of a multi-informant approach were unavailable, it is suggested that the unique manifest variable results may offer more promising directions for future research.

iii

Dedicated to my family for their loving support.

ACKNOWLEDGMENTS

I thank my advisor, Dr. Michael Vasey, for his insightful feedback and intellectual support at all stages of this project. I am also especially grateful to him for continually challenging me to think about conceptual and methodological issues from new perspectives and for encouraging me to “stretch” and grow in my research endeavors. I truly appreciate and value the mentorship and support he has given me throughout my graduate training.

I would also like to thank the other members of my dissertation committee, Drs.

Beck and Gibbs, for their thoughtful comments and support not only with this project, but with the other major activities of my graduate training, as well.

I thank Becky Hazen and Anya Ho for their extensive time and efforts in data collection and entry. I am also especially grateful to Becky Hazen for being a devoted

”writing buddy” and invaluable source of support throughout the entire process.

Finally, I am eternally grateful to my husband, Ramin, for his constant love, encouragement, and support. Thank you for always believing in me and inspiring me to achieve my goals.

v VITA

December 1, 1977……………….Born – Torrance, California

2000……………………………..B.A. Psychology, with Highest Honors University of California, Berkeley

2002……………………………..M.A. Psychology The Ohio State University

2000-2001……………………….Dean’s Graduate Enrichment Fellow 2003-2004 The Ohio State University

2001-2003………………………Graduate Teaching and Administrative Associate, The Ohio State University

2001-2003……………………… Practicum Student, Psychological Services Center: The Ohio State University

2002-2003………………………. Advanced Practicum Extern, Children’s Hospital Child Guidance Center Columbus, Ohio

2003-2004……………………….. Advanced Practicum Extern, The Children’s Health Council Palo Alto, California

2004-2005……………………….Clinical Child Psychology Intern, Stanford University Child and Adolescent Psychiatry / The Children’s Health Council Palo Alto, California

FIELDS OF STUDY

Major Field: Psychology

vi TABLE OF CONTENTS

Page Abstract …………………………………………………………………………… ii

Dedication ………………………………………………………………………… iv

Acknowledgments ……………………………………………………………….... v

Vita ………………………………………………………………………………... vi

List of Tables ……………………………………………………………………… x

List of Figures …………………………………………………………………….. xi

Chapters:

1. Introduction………………………………………………………………... 1

Reactive and Proactive Subtypes of Aggression………………….. 8 Relational Aggression and the Reactive / Proactive Distinction….. 18 Current Study……………………………………………………… 20 Hypotheses………………………………………………………… 22

2. Methods …………………………………………………………………… 25

Participants ………………………………………………………... 25 Participant Recruitment……………………………………. 25 Informed Consent Procedures……………………………... 26 Procedures ………………………………………………………… 27 Measures ………………………………………………………….. 28 Child Self-Report Measures ……………………………… 28 Parent Measures .………………………………………….. 34 Teacher Measures ………………………………………… 37

3. Results …………………………………………………………………….. 42

Preliminary Analyses …………………………………………….. 42 Missing Data..…………………………………………….. 42

vii Descriptive Statistics……………………………………….. 43 Validity Checks…………………………………………………….. 43 Variable Correlations…………………………………….… 43 Calculation of Composite Variables……………………….. 44 Tests of Differences Associated with Demographic Variables…………………………………………………… 46 Primary Analyses …………………………………………………. 47 Relational Aggression and RA / PA………………………. 48 Relational Aggression Associations with RA Correlates…. 49 Relational Aggression Associations with PA Correlates…. 50 Mediation Analyses - RA…………………………………. 51 Mediation Analyses – PA…………………………………. 54 Follow-Up Analyses………………………………………. 56 Multitrait-Multimethod Analyses…………………………………. 56 Tests of Differences in Structural Models for Boys and Girls……………………………………………………….. 58 Model Fit………………………………………………….. 59 Tests of Convergent Validity……………………………… 60 Tests of Discriminant Validity……………………………. 61 Tests of Method Effects…………………………………… 63

4. Discussion ………………………………………………………………… 65

Hierarchical Regression Analysis Results ………………………… 65 MTMM Results.…………………………………………………… 68 Relational Aggression and RA/PA Subtypes of Aggression………. 70 Gender Differences in Relational Aggression……………………... 71 Child Social Problems……………………………………... 74 Internalizing Problems…………………………………….. 80 Convergent Validity……………………………………………….. 81 Discriminant Validity……………………………………………… 83 Method Effects…………………………………………………….. 86 Limitations………………………………………………………… 86 Implications……………………………………………………….. 89

References …………………………………………………………………………. 95

Appendices ………………………………………………………………………... 105

A: Tables …………………………………………………………………... 105

B: Figures ………………………………………………………………….. 130

viii

C: In-Class Study Description…………………………………………… 142

D: Parent Letter ..………………………………………………………… 144

E: Consent Form.………………………………………………………... 147

F: Teacher Letter………………………………………………………… 149

ix LIST OF TABLES

Table Page

1 Demographic variable information ……………………………………….… 106

2 Descriptive statistics …………………... ………………………………….. 107

3 Correlations between study variables …………………………….………… 110

4 Component-Composite correlations ……………………………………….. 116

5 Hierarchical regression analysis results for RA and PA in predicting

RelationalAggression ……………………………………………………….. 119

6 Hierarchical regression analysis results for RA correlates ………………….. 121

7 Hierarchical regression analysis results for PA correlates ………………….. 123

8 Tests of mediation (RA) …………………………………………………….. 125

9 Tests of mediation (PA) …………………………………………………….. 127

LIST OF FIGURES

Figure Page

1 Relational Aggression x Gender interaction in predicting Child-Rated Social Problems …………………………………………………………….. 131

2 Relational Aggression x Gender interaction in predicting Child-Rated Internalizing Problems ……………………………………………………… 132

3 Relational Aggression x Gender interaction in predicting Teacher-Rated Oppositional Behaviors ……………………………………………………. 133

4 Multitrait-Multimethod Matrix model for Boys …………………………… 134

5 Multitrait-Multimethod Matrix model for Girls ……………………………. 139

xi CHAPTER 1

INTRODUCTION

Relatively recent studies and statistics challenge classic conceptualizations of aggression (that tend to characterize aggression as being more relevant to boys) by providing clear evidence that, not only do girls also demonstrate aggressive behavior, but they may actually be increasing in their levels of aggression. Although adolescent boys continue to greatly outnumber adolescent girls in violent crime offenses, (with prevalence ratios of 3:1 to 12:1, depending on the type of offense reported; Borduin & Schaeffer,

1998), in the United States the overall crime rate for adolescent girls has increased at a much higher rate than for any other segment of the population (Hennington, Hughes,

Cavell, & Thompson, 1998). For example, whereas arrests for violent crimes among boys under the age of 18 in the United States increased by 67% in the period between

1985 and 1994, arrests for violent crimes among girls in the same age group increased by

125% (Hennington et al., 1998). These statistics clearly indicate that girls exhibit aggressive behaviors, and research on aggression among youth has similarly reflected an appreciation of these patterns. In particular, “relational aggression” represents one pivotal concept linked to the relatively recent trend of increasing attention toward the

1 types of behaviors that may be more characteristic of aggression among girls (Crick &

Grotpeter, 1995).

As defined by Crick and her colleagues, relational aggression refers to behaviors intended to harm others through either actual or threatened damage to relationships or feelings of acceptance and inclusion in friendship groups (Crick, Werner, Casas, O’Brien,

Nelson, Grotpeter, & Markon, 1999; Crick, 1996; Crick, Bigbee, & Howes, 1996; Crick

& Grotpeter, 1995). Whereas overt aggression involves physically harming others, relational aggression involves harming others through intentional manipulation of, or damage to, their relationships with others. Examples of relationally aggressive behaviors include: using social exclusion as a form of retaliation, threatening to end a friendship unless the other individual complies with a request, or using the “silent treatment” as a means of coercion or punishment (Crick & Grotpeter, 1995).

The concept of relational aggression is based on the hypothesis that girls are likely to favor non-physical methods of harming others. That is, Crick and Grotpeter

(1995) asserted that due to the ways in which aggression had been studied up to that point, including a sole focus on physical manifestations of aggression as well as the overrepresentation of purely male samples, it was highly likely that aggressive behavior in girls was not being adequately considered. Further, they noted that even when girls were included in such studies, the results largely indicated that comparatively fewer aggressive behaviors were evident in girls.

From this perspective, a consideration of relational forms of aggression represents an attempt to more adequately capture and identify a form of aggression expected to be more common among girls. Specifically, girls are hypothesized to be more likely than

2 types of behaviors that may be more characteristic of aggression among girls (Crick &

Grotpeter, 1995).

Nelson, Grotpeter, & Markon, 1999; Crick, 1996; Crick, Bigbee, & Howes, 1996; Crick

The concept of relational aggression is based on the hypothesis that girls are likely to favor non-physical methods of harming others. That is, Crick and Grotpeter

3 boys to engage in behaviors aimed at harming relationships rather than in overt aggression because such behaviors are likely to be more effective in girls’ peer groups

(Crick & Grotpeter, 1995). A fundamental premise of this proposition is that behaviors that are most likely to be harmful to others are those that impede the realization of valued goals. Therefore, girls’ comparatively greater emphasis on social interactions, popularity, and security within their social groups is likely to make damage to their relationships more salient, whereas boys’ greater emphasis on instrumental and dominance-oriented goals is likely to make overtly aggressive behaviors more salient among them (Crick,

1996).

Empirical support for the hypothesized gender differences in relational aggression has been mixed. On the one hand, there is evidence supporting the suggestion that girls are more relationally aggressive than boys. For example, whereas boys typically outnumber girls when only overt aggression is considered, evidence suggests that among extremely aggressive children, approximately equal numbers of aggressive boys and girls can be identified when relational as well as overt forms of aggression are assessed (Crick,

1995). Many studies have documented gender differences in relational aggression, with results suggesting that girls are more relationally aggressive than boys (Crick, 1997;

Crick & Grotpeter, 1995; French, Jansen, & Pidada, 2002). There is evidence that children perceive girls to be more relationally aggressive toward both same and opposite- gender peers than boys (Crick, Bigbee, & Howes, 1996) and girls also report more distress associated with relationally aggressive types of behaviors than boys (Crick,

1995). On the other hand, the expected gender differences have not been found across all studies. For example, Rys and Bear (1997) found no gender difference in relational

4 aggression, and in three other studies, boys obtained higher ratings than girls in both overt and relational aggression (Tomada & Schneider, 1997; Hennington, Hughes,

Cavell, & Thompson, 1998; David & Kistner, 2000). These inconsistencies in the literature are likely due to differences across studies in the ways in which relational aggression is conceptualized and examined, and at this point no firm conclusions can be drawn about gender differences in relational aggression. It should also be noted that while the studies cited above examined relational aggression in middle-childhood-aged samples, studies conducted with later adolescent or early adulthood samples have been somewhat more consistent in demonstrating that females tend to be more relationally aggressive than boys (Casas & Crick, 1997; Crick et al., 1999).

Notwithstanding the somewhat inconsistent findings with respect to hypothesized gender differences, the majority of empirical investigations of relational aggression have yielded consistent evidence to support the construct validity of relational aggression. At a fundamental level, numerous studies support the validity of the distinction between relational and overt forms of aggression. Although the two types of aggression are correlated (as would be expected) factor analyses of various measures as well as examinations of criterion validity both support their distinction (Crick, 1996; Crick &

Grotpeter, 1995; Grotpeter & Crick, 1996; McNeilly-Choque et al., 1996; Rys & Bear,

1997). Further, the majority of children who exhibit extreme levels of aggression demonstrate either solely overt or solely relational forms of aggression, and not both

(Crick & Grotpeter, 1995).

Relationally aggressive children are also distinguishable from overtly, or physically, aggressive children with respect to a variety of correlates. For example, a

5 difference in friendship patterns between relationally and physically aggressive children has been documented. Specifically, whereas physically aggressive children report that they and their friends act aggressively toward others outside of their friendship groups, relationally aggressive children report high levels of aggression directed toward their own friends. That is, relationally aggressive children report a tendency to direct their aggression toward individuals within their own friendship circles, whereas overtly aggressive children tend to direct their aggression to individuals outside their friendship groups (Grotpeter & Crick, 1996). Further, whereas relationally aggressive children report high levels of intimacy with their friends, overtly aggressive children report valuing companionship rather than self-disclosure and closeness. However, it should be noted that these particular differences may also reflect a difference between the two types of aggression in the value placed on relational goals (Grotpeter & Crick, 1996). Finally, investigations of social information-processing patterns likewise provide support for the distinction between relational and overt aggression. For example, although hostile attribution biases have been identified among both overtly and relationally aggressive children (Grotpeter, Crick, & Bigbee, 1996; Crick, 1995), these biases are distinct in that relationally aggressive children have been found to exhibit hostile attribution biases for relationally-relevant situations, whereas attribution biases in overtly aggressive children are associated with instrumental provocation situations (Crick, 1995).

The harmful correlates of relational aggression for both relationally aggressive children and their victims have been documented across several studies. Children cite relational aggression as the most common hurtful behavior among girls’ groups (Crick,

Bigbee, & Howes, 1996) and evidence indicates that relational aggression adds uniquely

6 to the prediction of future social maladjustment (Crick, 1996). Relationally aggressive children report significantly higher levels of depression, isolation, and loneliness than their non-relationally aggressive peers (Crick & Grotpeter, 1995) and relational aggression is significantly related to peer rejection for both boys and girls (Crick, Casas,

& Mosher, 1997). In one college-aged sample, relational aggression was associated with antisocial and borderline personality features, and, in women, bulimic symptoms (Werner

& Crick, 1999). Being the target of relational aggression is also associated with detrimental implications, as studies have demonstrated that relational victimization is associated with concurrent adjustment problems, depressive symptoms, and delinquent behavior (Crick, Casas, & Nelson, 2002) and is predictive of future peer rejection (Crick et al., 2001). Considered together, existing evidence clearly suggests that relational aggression has broad negative implications for individuals’ overall psychological adjustment and functioning.

A review of the literature indicates that relational aggression represents a theoretically-based and empirically-supported subtype of aggression that can be reliably distinguished from overt forms of aggression. Also, despite the lack of consistent findings to support the expected trend toward greater levels of relational aggression among girls, the concept of relational aggression has greatly advanced the study of aggression by drawing attention toward the need to consider varying forms of aggression.

Thus far, existing studies of relational aggression have: demonstrated evidence for its distinction from overt aggression in samples ranging from preschool through adulthood, documented its harmful implications for both aggressors and victims, provided initial support for its relevance in specific cultures outside of the United States (Hart, Nelson,

Robinson, Olsen, & McNeilly-Choque, 1998; Tomada & Schneider, 1997; Owens, 1996;

Lagerspetz, Bjorkvist, & Peltonen, 1988), and established the framework for continuing investigation of this potentially important form of aggressive behavior (Crick et al.,

1999). However, despite these noteworthy contributions, the majority of research on relational aggression to date has focused on establishing a distinction between relational and overt forms of aggression, with relatively fewer studies examining relational aggression in the context of other established theories of aggression. Specifically, another empirically-supported and reliable distinction between types of aggression - reactive versus proactive aggression - has only been implicitly considered in the context of theories of relational aggression. An attempt to assess the associations between relational aggression and both proactive and reactive aggression offers a promising approach to further elucidation of the concept of relational aggression.

Reactive and Proactive Subtypes of Aggression

The distinction between reactive and proactive types of aggressive behavior is both theoretically-based and empirically-supported. Whereas reactive aggression (RA), based upon the frustration-aggression model outlined by Dollard and colleagues (Dollard,

Doob, Miller, Mowrer, & Sears, 1939), involves an angry, defensive response to frustration or provocation, proactive aggression (PA), based upon Bandura’s (1973) social learning theory conceptualization of aggression, involves a deliberate behavior that is goal-driven (Crick & Dodge, 1996).

From this perspective, reactive or retaliatory aggressive behavior is viewed as a defensive response to perceptions of threat. Threat perceptions are conceptualized as

“pushing” the individual to react aggressively. Reactive aggression is viewed as resulting from errors and biases in interpreting environmental information, such as the perception that one has been provoked by another individual with hostile intent. This type of response is conceptualized as serving the function of addressing the immediate perceived threat rather than advancing one’s goals. Reactive aggression is theorized to be triggered by feelings of intense anger, with the expectation that reactively aggressive individuals strike out toward peers in an “out-of-control” manner, without considering the consequences of their behaviors (Crick & Dodge, 1996). It should be noted, however, that an emphasis on the automatic (as opposed to controlled) nature of information- processing (for example, impulsivity and uncontrollability) that is assumed to be uniquely characteristic of reactive aggression can, in many instances, be misleading (Bushman &

Anderson, 2001). That is, whereas many acts of reactive aggression may occur in the

“heat of the moment”, a reliance on the immediacy or automaticity of such acts to classify aggressive behaviors can be problematic. From this perspective, a focus on the desire to attain justice, get revenge, or otherwise repair perceived damage to one’s self- image would seem to be the essential feature of reactive aggression.

In contrast, proactive aggression is viewed as occurring outside of the context of immediate provocation and as being influenced by beliefs about the outcomes of such behaviors. Beliefs about the expected outcome are hypothesized to “pull” the individual to act aggressively (Dodge & Coie, 1987). Proactively aggressive individuals are expected to view aggression as an effective and reasonable way to obtain social goals, with evidence suggesting that they are less likely to show concern for relationship-

9 enhancing goals during social interactions and are more likely to focus on self-enhancing goals (Crick & Dodge, 1996).

The social information-processing (SIP) theory of aggression, outlined by Dodge and Crick (1990; 1986) provides the theoretical cognitive framework for the distinction between reactive and proactive subtypes of aggression. According to the reformulated version of this model, children’s behavioral responses are a function of six processing steps: (1) encoding of external and internal cues, (2) interpretation and mental representation of these cues, (3) clarification or selection of a goal, (4) response access or construction, (5) response decision, and (6) behavioral enactment. Skillful processing across the entire sequence of steps is hypothesized to lead to socially competent responding, whereas biased or inept processing is hypothesized to lead to aggressive or socially incompetent responding (Crick & Dodge, 1994; Dodge & Crick, 1990).

Specifically, deficits in the first two steps of the model are associated with reactive aggression, while proactive aggression is linked with deficits in the later steps. It should be noted that although this model is comprised of a series of sequential steps, it is acknowledged that multiple social information-processing activities can occur at the same time and that relations between processing at the different steps are probably nonlinear

(Crick & Dodge, 1994).

Biases or inept processing at the first two steps of the SIP model are hypothesized to be associated with reactive aggression. At the first step of this processing sequence, relevant information from the environment is encoded through a process of selective attention. Such social information might include facial expressions, verbal comments, or body language. It should be noted that although this model implies that reactive

10 aggression results from heightened attention to threat cues, a recent study raises the possibility that refinement in this step of the model may be appropriate. That is, results from one sample of middle-school-aged children indicate that reactive aggression may actually be uniquely associated with suppressed, rather than heightened, attention to cues of rejection, ridicule, or failure (Schippell, Vasey, Cravens-Brown, & Bretveld, 2003).

However, the authors of this study acknowledge that these results are not inconsistent with the possibility of an earlier, more automatic bias toward such cues at earlier stages of processing. Further, they offer the suggestion that suppression of attention toward cues of social threat may also have the effect of interfering with the encoding of other relevant social cues at this stage of processing.

Once relevant social cues are encoded, they are represented in long-term memory and given meaning. In the case of social interactions, this often requires the skills of intention cue detection and social cue reading to guide the interpretation of another individual’s intentions.

Proactive aggression is hypothesized to be associated with deficits in the latter stages of this processing sequence. That is, after having interpreted the situation, children are hypothesized to select a goal or desired outcome for the situation, with the expectation that children’s existing goal orientation tendencies as well as the immediate social situation influence these goals. At the fourth step of this sequence, the individual either accesses one or more possible behavioral responses from long-term memory or creates a new strategy. Associative networks and other access rules are hypothesized to influence the access of behavioral responses. At this step, the access of competent strategies in response to specific stimuli is hypothesized to be associated with competent

11 social behavior, whereas a bias toward accessing aggressive responses is expected to precede inappropriate aggressive behavior. At the next step of processing, the individual chooses a response from among the possibilities accessed or constructed. The model does not specify whether this process involves a simultaneous or a sequential consideration of all responses. Individual differences are likewise expected in the characteristic ways of evaluating strategies, outcomes, and personal efficacy for successful enactment of strategies. The last stage of this sequence involves enactment of the selected behavioral response, in which the individual’s behavior is guided by protocols and scripts in the process of translating the chosen response into verbal and motor behaviors.

Numerous studies have provided results to support the theoretical claims and practical relevance of a SIP approach to conceptualizing aggression (Dodge, Laird,

Lochman, & Zelli, 2002; Crick & Dodge, 1994). At the outset of a review of this literature, it is important to note that the majority of these findings are correlational and that some of the observed correlates of aggression may be consequences, rather than causes, of being labeled an aggressive child (Dodge, Lochman, Harnish, Bates, &

Pettit,1997; Crick & Dodge, 1994; Price & Dodge, 1989). Several studies have provided evidence of a hostile attribution bias, or a tendency to attribute hostile intent to ambiguous situations, among aggressive children (Orobio de Castro, Veerman, Koopes,

Bosch, & Monshouwer, 2002; Dodge & Tomlin, 1987; Dodge et al., 1986; Dodge &

Frame, 1982; Dodge, 1980). Evidence also indicates that aggressive children of various ages are relatively deficient in their quality of generated responses, their ability to generate non-aggresive responses, their appraisal of types of outcomes likely to ensue,

12 and the degree of confidence that they express in their ability to perform each response

(Dodge & Crick, 1990; Dodge et al., 1986). There is also evidence suggesting that the on-line behavioral judgements about aggression that are associated with certain response decision processes tend to maintain externalizing behavior problems during adolescence whereas the tendency to view aggressive responses as sociomorally favorable is strongly positively correlated with externalizing outcomes (Fontaine, Burks, & Dodge, 2002).

Lochman and Dodge (1994) report that attributional biases, problem-solving deficiencies, and positive outcome evaluations of aggressive responses are all characteristic of aggressive children, with the degree of these cognitive biases directly related to the severity of aggression.

In addition to the general support for the SIP theory of aggression outlined above, there is also a notable amount of specific evidence to support the distinction between reactive and proactive forms of aggression. That is, despite the substantial correlations between the reactive and proactive subtypes of aggression reported in existing studies

(Dodge et al., 1997 calculated an approximate average correlation of r = .60 in non- referred samples), there is notable empirical support for their uniqueness. Reactive and proactive aggression are each associated with distinct, theory-based cognitive, behavioral, and peer status correlates, and the expected relations among these variables have been documented across various studies. For example, reliable distinctions can be made between these subtypes based on observations of even young children’s peer interactions, with reactive aggression including such behaviors as anger expressions and reactive hostility, and proactive aggression including such behaviors as bullying, teasing, and name-calling (Dodge, Lochman, Harnish, Bates, & Pettit, 1997). Reactive and proactive

13 aggression also appear to have distinct correlates. Reactive aggression has been shown to be uniquely associated with hostile attribution tendencies (Hubbard, Dodge, Cillesen,

Coie, & Schwartz, 2001; Crick & Dodge, 1996; Dodge, 1991; Dodge et al., 1997; Dodge,

Price, Bachorowski, & Newman, 1990; Dodge & Coie, 1987) frequent victimization by peers (Schwartz, Dodge, Coie, Cillesen, Lemerise, & Bateman, 1998), and heightened skin conductance reactivity in response to anger provocation (Hubbard et al., 2002), whereas proactive aggression is associated with positive outcome expectancies for aggression (Crick & Dodge, 1996; Crick & Dodge, 1989; Hubbard et al., 2001; Dodge et al.,1997) and frequent displays of assertive social behavior (Coie, Cillesen, Dodge,

Hubbard, Schwartz, Lemerise, & Bateman, 1999). Reactive aggression, similarly to

Attention-Deficit / Hyperactivity Disorder (ADHD), is more strongly correlated with impulsivity and attention problems than proactive aggression (Dodge, et al.,1997).

However, despite the association between reactive aggression and symptoms of

Attention-Deficit / Hyperactivity Disorder (ADHD), there is also evidence to indicate that a distinction can be made between the two. For example, reactive aggression has been found to be uniquely associated with a tendency to suppress attention toward social-threat cues, even after controlling for inattention and hyperactivity-impulsivity (Schippell et al.,

2003). Evidence has supported the expectation that response evaluation biases, or expectancies that aggression leads to positive instrumental or instrumental outcomes, are not associated with reactive aggression (Crick & Dodge, 1996; Crick & Dodge, 1989). In one sample of boys, proactive aggression uniquely predicted adolescent delinquency, and the relation between proactive aggression and delinquency was actually weakened in the presence of high levels of reactive aggression (Vitaro, Gendreau, Tremblay, & Oligny,

1998). Whereas reactive aggression has been found to be associated with social rejection, negative peer evaluations, absence of leadership, lack of cooperation with peers, starting fights, getting angry, and overall poor functioning (Day, Bream, & Pal,

1992; Waschbuch, Willoughby, & Pelham, 1998; Price & Dodge, 1989), proactive aggression has been found to be somewhat positively related to peer status, a sense of humor, and evaluations of leadership (Price & Dodge, 1989). For example, in their study of five- and six- year old boys, Price and Dodge (1989) found that once the variance due to reactive aggression was removed from teacher ratings of social rejection, ratings of proactive aggression were actually found to be associated with peer acceptance.

Reactive and proactive forms of aggression also appear to be distinguishable based upon levels of callous-unemotional (CU) traits. CU traits are conceptualized as attributes characterized by a lack of empathy and guilt, as well as shallow and superficial emotions (Frick 2000; 1995; Barry, Frick, DeShazo, McCoy, Ellis, & Loney, 2000).

Children who both exhibit conduct problems and receive high scores on measures of CU traits demonstrate a stronger family history of antisocial disorders, more contact with police, and higher intelligence test scores than non-CU conduct problem children (Frick,

1995). Children with high levels of CU traits show features typically associated with psychopathy, such as a lack of fearfulness and a reward-dominant response style and appear to be less distressed by their problems (Barry et al., 2000; O’Brien & Frick, 1996).

Youth characterized by these traits tend to have low levels of behavioral inhibition, a temperamental dimension that may contribute to poor internalization of remorse and compassion (Frick, 1998; Caputo, Frick, & Brodsky, 1999). Among adult criminal offenders, there is a substantial amount of evidence to support the distinction between

15 instrumental, goal-directed aggressive acts and reactive acts of aggression committed out of hostility in response to a perceived provocation or threat (Cornell, Warren, Hawk,

Stafford, Oram, & Pine, 1996). These general personality traits are hypothesized to be associated more closely with proactive aggressive behavior (Cleckley, 1976; Hare, 1981;

Patrick & Zempolich, 1998), with evidence indicating that instrumental violent offenders score higher on measures of psychopathic traits than reactive offenders (Cornell et al.,

1996). For example, adjudicated psychopaths are more likely to demonstrate the use of goal-oriented aggression and intimidation (Serin, 1991) and relative to non-psychopaths, psychopaths are more likely to victimize strangers in order to achieve material rewards and to report less emotional arousal surrounding their crimes (Williamson, Hare, &

Wong, 1987; Patrick & Zempolich, 1998). Among youth, CU traits are similarly theorized to be characteristic of individuals who are instrumental in their aggression

(Frick, 1998; Hare, 1981; Patrick & Zempolich, 1998; Loper, Hoffschmidt, & Ash,

2001). Specifically, there is evidence to suggest that, at least in one sample of incarcerated juvenile offenders, instrumental, goal-oriented violence is uniquely associated with a lack of guilt and remorse, whereas reactive violence is less uniquely predictive of such lack of empathy. In one sample of incarcerated male adolescents, proactive aggression was uniquely related to a greater tendency to expect positive outcomes for aggressive acts, suggesting that proactive aggression may be associated with a lack of empathy or regret (Smithmeyer, Hubbard, & Simons, 2000).

With respect to teacher reports of aggression, there is evidence to suggest that reactively aggressive children are viewed by teachers as having more social skills deficits

(Day, Bream, & Pal, 1992). As assessed by Dodge and Coie’s (1987) teacher-rating

16 instrument, the distinction between these types of aggression is supported by the internal consistency of the two scales, significant correlations between teacher and experimenter observations of behavior, and the stability of these subtypes of aggression over discrete periods of observation. Confirmatory factor analyses of Dodge and Coie’s teacher measure have indicated that a 2-factor model is more appropriate than a single-factor model, even though the two latent factors are substantially correlated (Poulin & Boivin,

2000; Dodge & Coie, 1987). Results from a factor analysis of an expanded version of a teacher rating measure for aggression also supported the distinction between these two forms of aggression (Brown, Atkins, Osborne, & Milnamow, 1996). Results using this measure indicated that teacher ratings of reactive aggression were uniquely related to in- school detentions, supporting the expectation that reactively aggressive acts are more associated with disruptive behavior problems in school.

Finally, neurocognitive models of aggression also offer support for the distinction between reactive and proactive aggression. From this perspective, reactive aggression is conceptualized as being associated with impairment in brain regions associated with executive functioning (i.e., prefrontal regions) (Barratt, 1994; Krakowski, Czobor, &

Carpenter, 1997), whereas proactive aggression is conceptualized as being associated with impairments in neurophysiological systems related to the processing of fear-related information and sensitivity to cues of punishment (Hare, 1978; Patrick, Cuthbert, &

Lang, 1994; Loney, Frick, Clements, Ellis, & Kerlin, 2003). For example, reductions in prefrontal cortex activity have been found in reactively, but not proactively, aggressive murderers (Raine, Buchsbaum, Stanley, Lottenberg, Agel, & Stoddard, 1994). With respect to proactive forms of aggression, signs of amygdala and related circuitry

17 dysfunction have been identified in individuals with psychopathic traits. Such features have been hypothesized to underlie deficits in emotional processing that, ultimately, lead to psychopaths’ failure to experience remorse or concern for their victims (Blair, 2002).

Relational Aggression and the Reactive / Proactive Distinction

Initial investigations of relational aggression with respect to information- processing models of aggression have yielded tentative support for the notion that, as currently commonly conceptualized and assessed, relational aggression may be more reactive than proactive in nature. For example, there is evidence to suggest that relationally aggressive children interpret ambiguous slights as intentional, maliciously- motivated acts (Crick, 1995). For such children, relational aggression may represent a retaliatory response to perceived hostile threat or harm (Crick, 1995). Similarly, there is evidence to suggest that relational aggression may be distinct from proactive aggression with respect to certain correlates. For example, information-processing styles characteristic of proactive aggression have not been found to be characteristic of relationally aggressive children. Specifically, relationally aggressive children do not report response decision biases characteristic of proactive acts of aggression, possibly implying that relational aggression is not proactive in nature (Crick & Werner, 1998).

However, an alternative explanation is that, as assessed in this particular study (a peer nomination measure), proactive elements of aggression were not adequately captured or that the responses merely reflected socially desirable responding (i.e., underreporting)

(Crick & Werner, 1998). Relational aggression also differs from proactive aggression with respect to the target of aggression. Whereas proactively aggressive individuals

18 typically direct their aggression toward individuals who are outside of their friendship groups, documented patterns indicate that relationally aggressive children direct their aggressive behaviors toward their own friends (Poulin & Boivin, 2000).

As outlined above, existing investigations of relational aggression appear to suggest that relational aggression is largely reactive in nature. Alternatively, it is also possible that existing methods of assessment merely emphasize the reactive aspects of relational aggression and fail to capture the proactive elements of such behavior. For example, Underwood, Galen and Paquette (2001) assert that most research on relational aggression has focused on reactive forms of these behaviors rather than on the more instrumental nature of such acts. From this perspective, different motives may underlie relationally aggressive behaviors, such as manipulating others’ relationships for instrumental reasons, or even for its purely “entertainment value,” and such proactively- oriented motives may be inadequately tapped by existing measures of relational aggression. Further, given the delayed nature of many relationally aggressive acts, relational aggression might logically be expected to demonstrate a certain amount of overlap with proactive aggression. That is, relationally aggressive acts frequently occur in the absence of an immediate perception of threat (for example, the act of spreading rumors about another individual often occurs outside of the presence of that individual), and from this perspective might be viewed as more proactive than reactive in nature.

On a related note, Crick and colleagues have suggested that reactive and proactive subtypes of relational aggression are likely to exist (Crick et al., 1999). Based on this dichotomy, reactive relationally aggressive behaviors might include such acts as spreading vicious rumors or excluding an individual from social groups in response to a

19 perceived threat or as a form of retaliation. Proactive relationally aggressive acts might include: using friendship as a tool of manipulation (“You can’t be my friend unless you help me with my homework”) or threatening to share personal information with others as a means of control over an individual (Crick et al., 1999, p.79). Further, to the extent that relationally aggressive acts are relatively covert in nature, it would seem logical to expect to find an association between proactive aggression (which has been linked with covert aspects of aggression) and relational aggression. To date, the possibility of subtypes has been restricted to speculation with no attempts to empirically investigate such a possibility. Given that reactive and proactive subtypes of relational aggression are hypothesized to exist, attempts to understand the association between relational aggression and the reactive / proactive dimension may be particularly relevant.

Current Study

Social information-processing approaches to investigations of relational aggression have yielded important information about cognitive processing patterns associated with relational aggression. For example, there is evidence that relationally aggressive preadolescent girls: attribute hostile intent to others, select social goals emphasizing revenge and exclusivity of relationships, and report positive outcome expectations for relationally aggressive behaviors (Crain, 2002). There is also evidence that relationally aggressive children demonstrate hostile attribution biases and report greater distress for relational provocation situations (Crick, Grotpeter, & Bigbee, 2002;

Crick, 1995). (It should be noted that no longitudinal studies of relational aggression have been reported in the literature and therefore there is no evidence to date that these

20 biased processing patterns are causally related to the development of relational aggression). However, although various specific information-processing patterns have been linked with relational aggression, the question of how relational aggression is best conceptualized along a reactive / proactive dimension has only been indirectly addressed.

That is, speculations about the reactive / proactive nature of relational aggression have largely been based upon the identification of information-processing patterns in relationally aggressive children that are characteristic of either reactive or proactive aggression. While these studies provide important insight into the nature of relational aggression as currently conceptualized, a logical next step involves a construct-driven, empirical investigation of the specific associations between relational aggression and measures of both reactive and proactive aggression as well as their respective correlates.

Data collected from a sample of middle-school-aged boys and girls were utilized to address the study objectives. Such a sample was viewed as particularly appropriate for an investigation of the nature of relational aggression for several reasons. First, in comparing age differences within their sample of third through sixth graders, Crick and

Ladd (1990) found that older children emphasize relational goals relative to instrumental goals more frequently than younger children. Further, relational aggression is displayed frequently by children between the ages of 11 and 14 (Crick et al., 1999), which is the typical age-range of middle school students. Also, given that middle-school-aged children spend increasing amounts of time with their peers and peer support becomes increasingly important during this period, it is logical to presume that relational aggression becomes especially effective and salient at this age (Parker, Rubin, Price, &

DeRosier, 1995; Prinstein, Boergers, & Vernberg, 2001). At the same time, increases in

21 physical development also functionally serve to make physical forms of aggression more dangerous and associated with far greater implications (Cairns, Cairns, Neckerman,

Ferguson, & Gariepy, 1989). Finally, increasingly sophisticated cognitive abilities and increasing levels of self-disclosure with development are likely to make relational aggression a potent, effective form of aggression among this age group (Prinstein,

Boergers, & Vernberg, 2001).

Hypotheses

The primary aim of this study was to investigate the construct validity of relational aggression. This objective was pursued through two main approaches. First, an attempt was made to investigate the specific associations between relational aggression and both reactive and proactive aggression. Within this framework, two primary questions were addressed: First, what are the associations between relational aggression and both reactive and proactive aggression? Second, how does relational aggression relate to variables demonstrated in prior research to correlate with either reactive or proactive aggression?

It was hypothesized that reactive aggression would account for a significant amount of the variance in a measure of relational aggression after controlling for proactive aggression. Similarly, proactive aggression was also expected to be uniquely associated with relational aggression (i.e., after controlling for reactive aggression). To this end, relational aggression was expected to demonstrate predicted associations with the empirically-established correlates of both reactive and proactive aggression, respectively. Specifically, to the extent that relational aggression is reactive in nature, it

22 was expected to correlate with: inattention / impulsivity, social problems, and internalizing problems. To the extent that relational aggression is proactive in nature, it was expected to correlate with CU traits and delinquent behavior. Further, any associations between relational aggression and identified correlates were hypothesized to be at least partially mediated by either reactive or proactive aggression. Finally, because existing theory and research suggests that girls and boys differ in their levels of relevant forms of aggression, significant aggression x gender interactions (RA x Gender, PA x

Gender, and Relational Aggression x Gender) were also expected for all of the models outlined above.

A multitrait-multimethod (MTMM) approach was used during the second phase of data analysis to further examine the construct validity of relational aggression.

Whereas regression analyses were appropriate for examining the associations between measures of the constructs of interest, the latent variable modeling techniques used in analyzing the MTMM expanded upon these associations between measured variables to provide an estimation of the associations between the trait constructs themselves. In this phase of analysis, child, parent, and teacher reports of various aspects of children’s behaviors were considered. Within this framework, evidence of construct validity relies on the demonstration that measures of one construct relate to measures of other constructs in theoretically-consistent ways. Specifically, a MTMM allows for the examination of both convergent and discriminant validity of trait measures in order to establish the validity of the constructs the measures purport to assess (Campbell & Fiske,

1959). This approach can also be used to compare method effects, or the associations between ratings by different informants. It was expected that the various forms of

23 aggression (relational, reactive, and proactive) would be correlated with each other. It was also expected that the specific nature of the associations between the constructs of interest (e.g., correlation coefficient magnitudes) would be different for boys and girls.

However, because this was largely an exploratory approach to investigating the associations between the constructs of interest, no specific a priori hypotheses were made with respect to the nature of these gender differences.

CHAPTER 2

METHODS

Participants

Participant Recruitment

The sample for this study was comprised of participants recruited for Dr. Michael

Vasey’s larger investigation of aggression. Participants were recruited from middle schools in the Columbus, Ohio area through visits to individual school sites. Brief presentations describing the study were given in each classroom (See Appendix C for script) and letters detailing the central features of the research were sent home with students (Appendix D). As a precaution, letters were also mailed home to parents.

Overall, 3,027 letters were sent home with students at seven different schools. As a result of these recruitment efforts, 224 families agreed to participate, reflecting a response rate of 7.4%. The response rate for one particular school was higher than the response rate for the other schools (17.4% averaged over the two-year period). Ninety students from this school (comprising 40.6% of the sample) chose to participate in the study. Participants from this specific school had a higher mean socioeconomic status index (t = 8.934, p =

.003) than the rest of the sample. However, they did not differ from the rest of the sample with respect to any other demographic variables. As an incentive for

participation, participants received $40.

Of the 224 participants in the sample, 88 were in the sixth grade, 77 were in the seventh grade, 55 were in the eighth grade, and 3 were in the ninth grade. (Data for the three participants in the ninth grade were collected during the summer after they had completed the eighth grade). 58.9% of the sample was comprised of boys, and participants ranged in age from eleven to fifteen years old, with a mean age of 12.9 years.

As calculated by the Hollingshead Four Factor Index of Social Status, the socioeconomic status (SES) of participating families was within the second stratum (medium business, minor professional, technical range; mean = 44.4, standard deviation = 12.1)

(Hollingshead, 1975). The sample was predominantly Caucasian (89.3%), with the remaining participants identifying themselves as African-American (1.4%), Asian

(1.8%), Native American (0.5%), and “Other” (5.9%). Table 1 provides a summary of the demographic variable information for the sample.

Informed Consent Procedures

Participants were scheduled for sessions at which both the child and one parent were present. At the beginning of the session, a description of the study and data collection procedures was read aloud by the experimenter. Both the child and parent were informed that their consent was voluntary and that they were free to withdraw from the study at any point without penalty. After agreeing to participate, the child, parent, and experimenter signed the consent form (See Appendix E for Consent Form).

Participants were also given a copy of the consent form for their records.

Procedures:

Parents of children who were interested in participating in the study called the research laboratory to schedule an appointment for data collection. Experimental sessions were either conducted at the participant’s home or in the Psychology Department clinic at the Ohio State University, based on the participant’s preference. In both cases, one clinical child psychology doctoral student and one undergraduate research assistant were present. The parent began completing the questionnaires at the beginning of the session, while the child completed the initial experimental tasks (related to the larger study) before beginning the child questionnaires. Specifically, these preliminary activities involved a series of computer-based activities that typically required approximately 45 minutes to complete. The parent and child completed their questionnaires in separate rooms. An experimenter was available to answer questions throughout the entire session. Additionally, because Harter’s Self-Perception Profile for

Children (1985) requires a two-step response, children were given specific instructions about how to complete it before they began to complete that particular questionnaire.

Experimental sessions typically lasted between two and two and one-half hours.

In order to compare multiple reports of the child’s behaviors, one of the child’s teachers was also asked to complete some questionnaires. Parents provided the name of one of their child’s teachers whom they wanted us to contact regarding their child’s participation in the study. Designated teachers were mailed a brief packet of questionnaires along with a letter explaining the study and indicating that both the parent and child had provided consent for that teacher to complete questionnaires about the child

(See Appendix F for Teacher Letter). To provide incentive for completing and returning the questionnaires, teachers were compensated $25.

Although the majority of teachers completed and returned their questionnaires, 19 out of the 224 teacher packets (8.5%) were not returned. This subset of 19 participants for whom teacher data were not available did not differ from the remainder of the sample in any demographic variables.

Measures:

The measures used in this study were administered in random order within a larger set of measures not relevant for the purposes of the current analyses.

Child Self-Report Measures

Children’s Social Behavior Scale (CSBS)

The Children’s Social Behavior Scale (CSBS) is a 13-item measure parallel in content to Crick’s (1996) teacher rating measure of relational aggression (Children’s Peer

Relations Scale). The items for the CSBS were rephrased to adapt them to a self-report format. However, although the Children’s Peer Relations Scale consists of 3 scales

(relational aggression, overt aggression, and prosocial behavior), the CSBS only contains the corresponding items that pertain to relational aggression (e.g., “I spread rumors or gossip about others”) and prosocial behavior (“I say supportive things to others”). The

CSBS also contains one item assessing the degree to which the child is accepted by same- gender peers, and another item assessing the degree to which the child is accepted by

28 opposite-gender peers. The response scale for each of the items ranges from 1 (never true) to 5 (almost always true). In the current study, Cronbach’s alpha for the seven items that correspond to the relational aggression subscale was .71 and Cronbach’s alpha for the four items of the prosocial behaviors subscale was .75.

Child Ratings of Aggression (CRA)

The Child Ratings of Aggression (CRA) is a 28-item measure adapted to a self- report format from Brown, Atkins, Osborne, and Milnamow’s (1996) Revised Teacher

Rating Scale for Reactive and Proactive Aggression. The items in this measure assess: proactive aggression, reactive aggression, covert antisocial, and prosocial behavior.

Possible responses to the items range from 0 (Never) to 2 (Very Often). For the purposes of this study, only the reactive aggression (e.g., “I get mad when I don’t get my own way”) and proactive aggression (e.g., “I have hurt others to win a game or contest”) subscales will be considered. In the current study, Cronbach’s alpha was .70 for the six items that comprise the reactive aggression subscale on the teacher version and .79 for the ten items that correspond to the proactive aggression subscale.

Youth Self–Report (YSR) (Achenbach, 1991)

The Youth Self-Report (YSR) is a 112-item self-report measure used as a broad screening instrument of behavioral problems in children ages eleven to eighteen.

Children rate the items on a scale from (0) not true to (2) often true for the past six months. The YSR has broad internalizing and externalizing factors, as well as eight specific syndrome scales. For the purposes of this study, only the Anxious / Depressed,

Social Problems, Attention Problems, and Delinquent Behavior subscales will be considered. The YSR syndrome scales have an average one-week test-retest reliability of r = .65 for a sample of 11 to 14-year-olds. Reported internal consistency coefficients for the subscales range from α = .68 to .91 (Achenbach, 1991). In the current study,

Cronbach’s alpha was .84 for the Anxious / Depressed subscale, .70 for the Social

Problems subscale, .76 for the Attention Problems subscale, and .65 for the Delinquent

Behaviors subscale.

Antisocial Process Screening Device – Youth version (APSD-Y) (Frick & Hare,

2001; Frick, O’Brien, Wootton, & McBurnett, 1994)

The Antisocial Process Screening Device (APSD) (formerly known as the

Psychopathy Screening Device) is a 20-item self-report measure adapted from its original format as a measure of parent and teacher reports of children’s antisocial behaviors. A self-report measure is viewed as especially important in the assessment of these types of characteristics, which by their nature involve covert and affective aspects of behavior that may be less readily apparent to observers (Loney, Frick, Clements, Ellis, & Kerlin,

2003). In a comparison between other-rated (staff members at a juvenile residential facility) and self-reports on the APSD-Y, self-reports were found to be uniquely predictive of both a diagnosis of conduct disorder as well as severity of conduct problems

(Green & Youngstrom, 2002). However, self-reports were also found to be less severe and to demonstrate less consistency among scale items.

The items on this instrument assess behaviors and characteristics related to interpersonal (i.e., narcissism; “brags a lot about abilities”), affective (i.e., callous-

30 unemotional personality features; “does not show emotions”), and behavioral (i.e., impulsiveness; “acts without thinking”) dimensions. Responses on the ASPD-Y range from 0 (not at all true) to 2 (definitely true).

Although no factor analyses of the self-report version of the APSD have been published to date, recent analyses suggest that a three-factor model of the APSD may be more appropriate for both non- clinic-referred children and adults (Frick, Bodin, & Barry,

2000; Lima, Butler, & Loney, 2002). In the current study, Cronbach’s alpha was .64 for the items corresponding to the narcissism subscale, .48 for the impulsivity subscale items, and .31 for the CU subscale items.

Self-Perception Profile for Children (Harter, 1985) (SPPC)

The Self-Perception Profile for Children (SPPC) is a 36-item measure assessing children’s perceptions of competence across five domains: social acceptance, behavioral conduct, scholastic competence, athletic competence, and physical appearance. There is also an index of global self-worth, for a total of six subscales with six items each. For the purposes of this study, only the social acceptance subscale was considered.

Each item on this measure is related to a specific skill, with both a positive description as well as its negative counterpart. The items require a two-step response, with children first deciding which statement is more descriptive of them, and then indicating whether the statement is “really true” or “sort of true for me.” Reported three- month test-retest reliabilities range from r = .70 to .87 and internal consistency estimates of the subscales range from α = .71 to .86 (Harter, 1982). Cronbach’s alpha was .83 for the social competence scale in the current study.

Effortful Control Scale – Child Version (EC-C; Lonigan, 1998; Lonigan, Phillips,

& Hooe, 2001)

The Effortful Control Scale assesses children’s levels of self-reported effortful control. For each of the 24 items, children are required to rate on a scale from 1 (“not at all like me”) to 5 (“very much like me”) how descriptive the various items are for them.

Behaviors that are descriptive of both high and low levels of effortful control are included. This measure has two factors, Persistence / Distractibility (e.g., “I have a hard time concentrating on my work because I’m always thinking about other things”) and

Impulsivity (e.g., I can easily stop an activity when told to do so”). The current version of this measure contains 12 items for each factor. For the purposes of the current analyses, only the Persistence / Distractibility factor was considered.

The original items for this measure were adapted from various temperament and personality inventories, and initial scale development was based on the results of a sample of 600 children from fourth to eleventh grade. From an original item pool of 46 items believed to relate to effortful control, 12 items with the highest loadings for each factor were identified for inclusion in the measure. The Persistence / Distractibility factor has a previously reported Cronbach’s alpha of .84. In the current study, Cronbach’s alpha was .85.

Revised Children’s Manifest Anxiety Scale (RCMAS-R; Reynolds & Richmond,

1978)

The Revised Children’s Manifest Anxiety Scale (RCMAS) is a 37-item measure which requires children to indicate on a four-point scale how much each statement is true

for them, from “always” to “never”. It measures both the frequency and intensity of such

anxiety symptoms as worry, oversensitivity, physiological reactivity, and concentration

problems. The RCMAS can be used with children as young as eight years old, and it is

significantly correlated with other self-report and diagnostic measures of anxiety

(Reynolds & Richmond, 1978). The scale consists of numerous factors, but for the

purposes of this study, only the total anxiety score and Lie Scale will be considered. The

nine-item Lie Scale assesses children’s tendency to present themselves favorably (e.g., “I

am always nice to everyone”). With a national standardization sample of nearly 5,000

Caucasian and African American children ages 6 – 19, reliability coefficients ranged

from α = .78 to .85 for Total Anxiety scores. Lie Scale alpha estimates for children ages

11 –14 range from α = .78 to .82. Test-retest correlations calculated for the

standardization sample were r = .88 after a period of 1 week and r = .77 for a 5- week interval. Cronbach’s alpha was .91 for the total score and .63 for the Lie Scale in the current study.

Children’s Depression Inventory (Kovacs, 1992)

The Children’s Depression Inventory (CDI) is a 27-item inventory that assesses depression-related behaviors. The scale is designed for use with children aged seven to seventeen years old, and it includes a broad range of depressive symptoms such as disturbed mood, hedonic capacity, vegetative functions, self-evaluation, and interpersonal behaviors. Each item consists of three choices, ranging from 0 to 2, and children are asked to indicate the degree to which each statement describes him or her for the past two weeks.

Reported internal consistency coefficients for the CDI range from α = .71 (with pediatric outpatients) to .89 (with clinic-referred youth) across various samples, and the measure has been demonstrated to have both explanatory and predictive validity (Kovacs,

1992). Overall, the measure has been shown to have an acceptable level of stability, but since the CDI is designed to measure a state rather than a trait, interpretations of test- retest data are problematic. The CDI correlates with other measures of childhood depression, such as the Depression Scale of the Child Behavior Checklist Youth Self

Report, the Reynolds Adolescent Depression Scale, the Hamilton Rating Scale for

Depression, and the Depression Scale of the Child Assessment Schedule. Cronbach’s alpha for the total score in the current study was .84.

Parent Measures

Children’s Social Behavior Scale – Parent Form (CSBS-P)

Crick’s (1996) teacher rating instrument was adapted into a parent-report measure for this study. The CSBS-P consists of three subscales: a relational aggression subscale, an overt aggression subscale, and a prosocial behavior subscale. It also contains two items assessing same-gender and opposite-gender acceptance, respectively. For this measure, the items that comprise the overt aggression subscale were omitted. Cronbach’s alpha was .80 for the items that comprise both the relational aggression and prosocial behavior scales in the current study.

Parent Ratings of Aggression (PRA)

The Parent Ratings of Aggression measure consists of 28 items that are adapted into a parent-report version from the teacher rating scale (Teacher Ratings of Aggression) developed by Brown, Atkins, Osborne, & Milnamow (1996). The teacher version of this measure assesses proactive aggression, reactive aggression, and prosocial behavior (see below for a full description of the original measure). Parents are asked to rate the frequency of each behavior on a 3-point scale. In the current study, Cronbach’s alpha was .82 for the reactive aggression items and .79 for the proactive aggression items.

Child Behavior Checklist (CBCL, Achenbach, 1991)

The CBCL is a 118-item parent-report instrument designed to evaluate emotional and behavioral problems in children, ages six to eighteen. The CBCL has eight syndrome scales: withdrawn, somatic complaints, anxious/depressed, social problems, thought problems, attention problems, delinquent behavior, and aggressive behavior. There are also two broadband scales of internalizing and externalizing behaviors, as well as a total problems scale. Parents are asked to rate each item on a three-point scale ranging from

“not true” to “very true” for the most recent six-month period. For the purposes of this study, only the anxious/depressed, social problems, and attention problems syndrome scales were considered. Reported alphas with a sample of children aged twelve to eighteen are as follows: Anxious/Depressed scale α = .86 for boys and .88 for girls,

Social Problems scale α = .76 for boys and .76 for girls, and Attention Problems scale α =

.83 for boys and .84 for girls. In the current sample, internal consistency coefficients

35 were: Anxious/Depressed scale α = .86, Social Problems scale α = .72, and Attention

Problems scale α= .81.

Antisocial Process Screening Device – Parent Version (APSD-P), (Frick & Hare,

2001)

The Antisocial Processes Screening Device (APSD-P) (formerly known as the

Psychopathy Screening Device) is a 20-item teacher rating measure developed by Frick and his colleagues. The items of the APSD assess interpersonal, affective, and behavioral dimensions associated with the construct of psychopathy as assessed by Hare’s (1991)

Psychopathy Checklist- Revised. Parents are asked to rate each item on a scale ranging from 0 (not at all true) to 2 (definitely true). Frick and colleagues (1994) originally reported a two-factor solution of the measure, with calculated alphas for a combined sample of both parent and teacher versions being .82 for an impulsive / conduct problems

(ICP) subscale and .68 for a CU subscale. However, more recent data indicate that a three-factor solution may be more appropriate (Frick, Bodin, & Barry, 2000; Lima,

Butler, & Loney, 2002). Specifically, the separation of the former ICP subscale into narcissism and impulsivity dimensions has been found to yield stronger divergent correlations with DSM-based diagnostic criteria (specifically, the narcissism subscale is more strongly associated with ODD criteria, while the impulsivity subscale is more strongly associated with the criteria for ADHD). Applying a three-factor structure to a combination of teacher and parent data yielded the following coefficients: α = .84 for the narcissism subscale, α = .74 for the impulsivity subscale, and α = .76 for the CU subscale

(Frick, Bodin, & Barry, 2000). In the current study, Cronbach’s alpha was .72 for the narcissism subscale, .68 for the impulsivity scale and .62 for the CU scale.

Parent Rating Scale (Harter, 1985) (PRS)

The Parent Rating Scale is a 15-item measure that assesses parents’ perceptions of their children’s competence across various domains. Each item requires a two-part response. First parents are asked to indicate which of two statements is more descriptive of their child, and then to indicate whether it is “sort of true” or “really true.” The five subscales of this measure are parallel to the competence subscales of the Self-Perception for Children, and, as with the child version, only the social competence subscale was considered. Cole et al. (1996) reported that the subscales of the PRS manifested moderately high levels of internal consistency. Cronbach’s alphas ranged from .82 to .89 except for Physical Appearance, which was .59. Cole et al. (1996) also reported that the

PRS subscales demonstrated relatively strong test-retest reliability estimates, ranging from r = .60 to .80 over a 4-month interval. In the current study, Cronbach’s alpha was

.88 for the social competence subscale.

Teacher Measures

Children’s Social Behavior Scale – Teacher Form (CSBS-T, Crick, 1996).

The Children’s Social Behavior Scale – Teacher Form (CSBS-T) is a teacher- rating measure of children’s social behavior. The CSBS-T consists of three subscales: a relational aggression subscale, an overt aggression subscale, and a prosocial behavior

subscale, as well as two items assessing same-gender and opposite-gender acceptance,

respectively. In the present study, the items of the overt aggression subscale were

omitted from the measure. Teachers rate each item on a scale of 1 (never true) to 5

(almost always true). Teachers’ reports of relational aggression have been shown to be

internally consistent (α = .94 in one sample and .95 in another sample), and the reported

correlation between the relational aggression and overt aggression subscale is r = .55

(Crick, 1996; Rys & Bear, 1997). In the current study, Cronbach’s alpha was .90 for both the relational aggression and prosocial behaviors subscales.

A major concern associated with the use of teacher rating instruments of relational aggression is the possibility that teachers may not be privy to relevant information about children’s social interactions. For example, in a meta-analytic comparison of agreement between peer and teacher assessments of aggression, the calculated average correlation was .42 (Achenbach, McDonaughy, & Howell, 1987). However, a comparison between teacher and peer-nominated reports of relational aggression in a sample of 3rd – 6th

graders revealed a correlation of r = .57 for boys and r = .63 for girls, suggesting that

although the two sets of reporters provide unique information, teacher reports are

substantially reflective of peer perceptions’ of children’s relational aggression and may

serve as an adequate substitute for peer nomination methods (Crick, 1996).

Teacher Ratings of Aggression (TRA) (Brown, Atkins, Osborne, & Milnamow,

1996)

The Revised Teacher Rating Scale is a 28-item measure consisting of items that

assess proactive aggression (e.g., “deliberately plays mean tricks on other students”),

38 reactive aggression (e.g., “blames others when he/she gets into trouble”), and prosocial behavior (“volunteers to help classmates in class or on the playground”). The content for these items was selected based on a review of existing scales. Teachers rate the frequency of each behavior on a 3-point scale ranging from 0 (never) to 2 (very often). In addition to the hostile aspect of proactively aggressive behaviors assessed by Dodge and

Coie’s (1987) teacher measure, the TRA includes items related to instrumental and covert antisocial aspects of proactive aggression. Both of the aggression scales have been demonstrated to be internally consistent, with the reported alpha for the six-item reactive aggression scale = .92 and the reported alpha for the ten-item proactive aggression scale

= .94. The two scales were moderately correlated with each other (r = .70). In the current study, Cronbach’s alpha was .87 for the reactive aggression subscale and .86 for the proactive aggression subscale. The correlation between reactive and proactive aggression was r = .65 in the current sample.

Conners’ Teacher Rating Scale - Revised (Conners, 2000)

The Conners’ Teacher Rating Scale is designed for use as a broad screening device to assess psychopathology and other problem behaviors in children ages three to seventeen. The Conners’ consists of eleven subscales that tap problem behaviors across a range of domains. However, only the Social Problems subscale, the Oppositional subscale, the Anxious / Shy subscale, and the Restless-Impulsive index were considered in this study. Teachers respond to the items on this measure by indicating which of four choices, ranging from “Not True at All” to “Very Much True”, is most descriptive of the child’s behaviors.

The Social Problems subscale consists of five items that assess perceptions of social acceptance, competence, and self-esteem. The Social Problems subscale has been shown to have a six-week test-retest reliability of r = .61. Among children nine to eleven years old, Cronbach’s alpha for boys was .93 and .91 for girls, and among twelve to fourteen year-olds, the reliability coefficient for boys was α = .91 and α = .93 for girls

(Conners, Sitarenios, Parker, Epstein, 1998). In this sample, Cronbach’s alpha was .91.

The Oppositional subscale consists of six items that assess behaviors related to breaking the rules and being defiant of authority. The Oppositional subscale has demonstrated a 6-week test-retest reliability of r = .88. Among children nine to eleven years old, Cronbach’s alpha was .93 for boys and .91 for girls, and among twelve to fourteen year-olds, Cronbach’s alpha was .94 for both boys and girls. In the current study, Cronbach’s alpha was .90.

The Anxious / Shy subscale consists of 6 items that assess a tendency to demonstrate anxious or withdrawn behaviors. Reported six-week test-retest reliability is r = .86. Cronbach’s alphas for boys are .82 (nine- to eleven-year-olds) and .83 (twelve- to fourteen-year-olds), and the corresponding alphas for girls are .78 and .79. Cronbach’s alpha for the Anxious / Shy subscale was .75 in the current study.

The Restless-Impulsive index consists of six items. Reported six-week test-retest reliability for this index is r = .80, and reported Cronbach’s alphas for boys are .88 (ages nine to eleven) and .85 (ages twelve to fourteen), and .89 and .88 for girls. Cronbach’s alpha was .89 in the current study.

Antisocial Processes Screening Device – Teacher version (APSD-T) (Frick &

Hare, 2001; Frick, O’Brien, Wootton, & McBurnett, 1994)

The Antisocial Processes Screening Device (APSD-T) (formerly the Psychopathy

Screening Device) is a 20-item teacher rating measure developed by Frick and his colleagues. The items of the APSD-T are identical in content to the parent version of this measure. Teachers are asked to rate each item on a scale ranging from 0 (not at all true) to 2 (definitely true). As with the child and parent versions used in this study, a three- factor solution was applied to the measure. Reported alphas using a three-factor solution have ranged from .70 to .84 for the narcissism subscale, .66 to .74 for the impulsivity subscale, and .58 to .76 for the CU subscale. In the current study, Cronbach’s alpha was

.83 for the narcissism subscale, .67 for the impulsivity subscale, and .73 for the CU subscale.

Teacher Rating Scale (Harter, 1985)

The Teacher Rating Scale is a 15-item measure of teachers’ appraisals of students’ competence. For each item, teachers are first asked to indicate which of two statements is more descriptive of the child, and then to indicate whether it is “sort of true” or “really true.” The subscales of this measure are parallel to the competence subscales of the Self-Perception Profile for Children. Cole, Martin, Powers, and Truglio (1996) reported that the subscales manifested high levels of internal consistency (Cronbach’s alphas from .93 to .97) and good test-retest reliability (r = .67 - .73 over a four-month interval). As with the child version, only the social acceptance subscale was considered in the present study. Cronbach’s alpha was .93 for this subscale in the current study.

CHAPTER 3

RESULTS

Preliminary Analyses

Missing Data

For the purposes of all regression analyses, participants with missing values on

10% or more of the items on a given measure were excluded from analyses involving that measure. However, in order to maximize power wherever possible, participants were excluded only from analyses involving the variables for which they had missing data.

Although this lead to slight differences in the numbers of participants included across regression analyses, the differences were minimal and did not pose a threat to the interpretability of results across regression models.

For the purposes of latent variable modeling analyses, missing values were estimated by using the Expectation-Maximization (EM) algorithm available in LISREL

8.50 (Joreskog & Sorbom, 1996). This procedure, which leads to maximally unbiased estimates of missing values, utilizes information contained in cases both with and without missing data to compute a maximum-likelihood covariance matrix from which missing values are estimated.

Descriptive Statistics

The means, standard deviations, and minimum and maximum values for the variables included in this study are presented in Table 2 for the males and females in this sample. Pearson Product Moment correlation coefficients between study variables are presented in Table 3.

Validity Checks

Variable Correlations

The Pearson Product Moment correlation coefficients between variables in this particular sample were compared to values previously reported in the literature in order to determine the extent to which this sample resembled other samples. For example, prosocial behavior should be negatively correlated with displays of aggressive behavior, and this relation has been documented in previous studies. In the current sample, as expected, prosocial behavior (as rated by child, parent, and teacher) was negatively correlated with corresponding reporter ratings of: relational, reactive, and proactive aggression (-.475 < r <-.244; p <.001). Relational aggression has been found in previous studies to be positively associated with both peer rejection (i.e., social problems) and depressed or withdrawn behaviors, and this pattern of associations was likewise found in this sample across all child, parent, and teacher ratings (.174 < r < .393; p <.05). Finally, a review of other expected correlations yielded similarly consistent results. For example, reports of anxiety and depression have been shown to overlap to a considerable degree, and in particular, CDI scores have been reported to significantly correlate with RCMAS

43 scores (Kovacs, 1992). For example, Joiner and Lonigan (2000) reported a correlation of r = .68 in a sample of youth psychiatric inpatients. In the current sample, the correlation between the CDI and the RCMAS was r = .615, p < .01. Overall, an examination of the patterns of associations within the entire data set indicated that relations between variables were consistent with those reported in other studies.

Reactive aggression (RA) and proactive aggression (PA) have been found to be highly correlated in previous studies. In order to determine if this sample was consistent with other samples in that respect, the correlation coefficients between RA and PA were examined for child, parent, and teachers in this sample. In all cases, as expected, the associations were both positive and significant (.580 < r < .652; p < .001). Due to this significant amount of overlap in variance between the two variables, all regression analyses examining the effects of RA controlled for PA, and all analyses examining the effects of PA controlled for RA.

Calculation of Composite Variables

A review of the Pearson Product Moment correlation coefficients between child, parent, and teacher variables indicated that agreement across reports of children’s behavior was generally low. For example, the correlations between parent and teacher reports of children’s relational, reactive, and proactive aggression were all less than r =

.30 (r = .210, .251, and .265, respectively). As a result, all regression analyses were calculated separately for child, parent, and teacher.

In cases in which more than one measure was available for the same construct, composite scores were created separately by reporter in order to maximize their validity

(Cook & Campbell, 1979; Rushton, Brainerd, & Pressley, 1983). The creation of composite variables also has the advantage of maximizing the error degrees of freedom available in regression analyses. Composite scores were created by standardizing the raw scores of all component variables and then calculating the means across measures of each construct. It should be noted that in determining the composition of composite scores, component variables that had a correlation of less than r = .60 with the composite score were not included in the calculation of a composite score. As a result of this criterion, parallel composite scores for child, parent, and teacher were not available in every case.

Overall, six composite variables were calculated. A Social Problems composite score was created for child, parent, and teacher reports. Social problems were represented by the average of the Social Problems subscale scores (from the YSR, CBCL, or CTRS), and reversed Social Competence scores (as measured by the SPPC, PRS, or

TRS). Cronbach’s alphas for the child, parent, and teacher Social Problems composite scores were .63, .75, and .90, respectively. A Child Internalizing composite score was calculated by averaging the standardized scores on the YSR Anxious/Depressed scale, the

CDI, and the RCMAS (α = .85). A score representing Child Inattention composite score was calculated by averaging the z-scores on the YSR Attention Problems subscale and the reversed EC – Persistence / Distractibility scale (α = .79). A teacher composite score representing Inattention problems was calculated by averaging the standardized scores from the CTRS subscales of both Restless / Impulsivity and Attention Problems (α = .86).

The correlations between component variables and their respective composite scores are presented in Table 4.

Tests of Differences Associated with Demographic Variables

In order to identify potential patterns of differences in variable scores associated with demographic variables, one-way analyses of variance were calculated for the demographic variables of gender, race, grade, age, socioeconomic status (SES), and school. The results revealed significant effects for Gender, SES, and School in predicting relevant study variables. Because there was a significant association between School and

SES in this study, hierarchical regression models were tested to determine whether

School was uniquely predictive of the observed variance, after controlling for SES.

Models were tested for all variables for which significant differences in child, parent, or teacher reports were found to be associated with SES or School. In these analyses, the dependent variable was the variable of interest (e.g., Relational Aggression), with SES entered as a predictor at Step 1 and School added as a predictor at Step 2. In all cases,

School did not account for a significant amount of the variance in a given variable beyond that already accounted for by SES. As a result, for the purposes of all subsequent regression analyses, only the effect associated with SES was controlled for.

Given that gender differences in aggression have been previously reported, the specific differences in mean levels of Relational Aggression, RA, and PA were examined. Specifically, both child and teacher reports of PA were relatively higher for boys (t = 6.97 and 6.66, respectively; p = .009 and .001). Effect sizes for these differences were in the low range, (d = .370 and .375, respectively). In contrast, girls received higher parent ratings of Relational Aggression than boys (t = 4.18, p = .042).

The effect size for the gender difference in parent reports of Relational Aggression was also relatively small (d = .138). Given these gender differences in reported levels of

46 aggression subtypes and because the different forms of aggressive behavior were expected to be associated with different implications for boys and girls, gender interaction terms were calculated and included in all subsequent analyses. The inclusion of interaction terms in the regression models also allowed for the construction of graphs depicting the specific nature of the effects.

Primary Analyses

In order to reduce the potential for problems of multicollinearity in the regression analyses, all of the variables were standardized prior to their inclusion in the model. The main effect variables were also standardized prior to computing all interaction terms, consistent with the recommendations of Aiken and West (1991). Due to concerns about strong correlations between Relational Aggression, RA, PA, and relevant correlates, collinearity diagnostics were examined for all analyses and multicollinearity was not identified as a problem in any case.

Various diagnostic tests were performed for all regression analyses. Normal- probability plots were examined and none were found to be indicative of a violation of the assumption of normality of errors. Plots comparing the standardized residuals against the standardized predicted values were examined and there were no indications of violations of the assumptions of either homoscedasticity or linearity in any case. Cook’s distance was calculated for all regression analyses and, although outliers were identified in some cases, influential data points were not identified. That is, removing the various outlier(s) from a given regression model did not change the interpretation of the model.

Because there was no specific evidence of unusual circumstances (e.g., measurement error) associated with these particular cases, outliers were included in all analyses.

Relational Aggression and RA / PA

In order to test the hypothesis that relational aggression is both uniquely reactive and uniquely proactive in nature, hierarchical regression analyses were conducted. In these models, after controlling for Gender and SES at Step 1, RA and PA (Step 2) and then an RA x Gender interaction term and a PA x Gender interaction term (Step 3) were added as predictors of Relational Aggression. Simultaneously entering both RA and PA

(as well as their respective gender interaction terms) into the regression models allowed for an examination of the relative contribution of all of the variables in accounting for variance in relational aggression. This basic model was calculated separately for child, parent, and teacher reports, and results are presented in Table 5.

In all cases, the addition of RA and PA at Step 2 resulted in a significant increase in the amount of variance in Relational Aggression accounted for, even after controlling for gender and SES (child ∆R2 = .289, parent ∆R2 = .221, teacher ∆R2 = .384; all p-values

< .01). Specifically, RA was a significant predictor across all three models (B = .196,

.285, and .257, respectively; all p-values < .01). Similarly, PA was also uniquely predictive of Relational Aggression for child (B = .411, p < .01), parent (B = .235, p <

.01) and teacher (B = .454, p < .01) reports. The addition of the RA and PA gender interaction terms at the final step of the model did not significantly increase the proportion of variance accounted for in any of the models.

Relational Aggression Associations with RA Correlates

The associations between Relational Aggression and established correlates of RA

were similarly examined using hierarchical regression analyses. In these analyses, after

controlling for PA, Relational Aggression and then a Relational Aggression x Gender

interaction term were added to a model predicting various correlates of RA. Individual

correlates of RA (Inattention, Social Problems, Internalizing Problems) were tested in

separate models. Overall, only the Child Inattention Composite (∆R2 = .028, p = .004) and the Parent Anxious / Depressed (∆R2 = .053, p = .000) variables were significantly

associated with Relational Aggression after controlling for PA (Step 3). With respect to

the addition of the Relational Aggression x Gender interaction term entered at the fourth

step of the model, the interaction was only significant for the Child Social Problems

Composite (∆R2 = .017, p = .038). The Child Internalizing Composite interaction was

associated with ∆R2 = .011, p = .095.

As illustrated in Figure 1, whereas higher levels of relational aggression were

associated with an increase in levels of self-reported social problems for boys, the same

was not true for girls. That is, girls who reported higher levels of relational aggression

actually demonstrated a tendency to report lower levels of social problems than girls who

reported low levels of relational aggression. With respect to the Relational Aggression x

Gender interaction in predicting Child Internalizing problems, the graph in Figure 2

illustrates that whereas higher levels of relational aggression were associated with an

increase in self-reported internalizing problems for girls, reporting high levels of

relational aggression among boys was actually associated with a tendency to report lower

levels of internalizing problems. The results of the significant regression models

predicting RA correlates (at either Step 3 or 4) are presented in Table 6.

Relational Aggression Associations with PA Correlates

Associations between Relational Aggression and established correlates of PA

(Delinquency and CU traits) were tested in a manner parallel to the tests of the RA

correlates. For child, parent, and teacher reports, Relational Aggression was

significantly correlated with Delinquency / Oppositional Behavior after controlling for

RA at Step 3. (For child reports, ∆R2 = .060, p = .000; for parent reports, ∆R2 = .017, p =

.019; and for teacher reports, ∆R2 = .039, p = .000). However, Relational Aggression

was only significantly correlated with CU Traits for child and parent reports (∆R2 = .026, p = .017 and ∆R2 = .027, p = .010, respectively) at Step 3. For the teacher model, ∆R2 =

.013, p = .067. With respect to a test of the Relational Aggression x Gender interaction

terms, the interaction was only significant for Teacher Oppositional (∆R2 = .011, p =

.022). The results of these analyses are presented in Table 7. A graph of the Relational

Aggression x Gender interaction for Teacher Oppositional is presented in Figure 3. As

Figure 3 illustrates, higher levels of teacher-reported Relational Aggression were associated with lower levels of teacher-rated Oppositional Problems for boys relative to girls. In essence, girls who received high ratings of relational aggression also received higher scores on ratings of oppositional problems than did boys with comparably elevated ratings of relational aggression.

Mediation Analyses – RA

For the cases in which Relational Aggression was significantly associated with

RA correlates, it was hypothesized that this association would be mediated by RA. In order to test this hypothesis, mediation models were tested for these variables (Child

Inattention Composite and Parent Anxious Depressed). In addition, because earlier analyses had identified a significant Relational Aggression x Gender interaction for the

Child Social Problems Composite, mediation models were tested separately for boys and girls in these cases in order to more specifically examine the nature of the potential mediating role of RA x Gender. That is, to the extent that RA was expected to mediate the association between relational aggression and the Child Social Problems Composite, it was further expected that given the gender interaction effects identified for Child Social

Problems, the mediating effect of RA in this case would be different for boys and girls.

For example, RA may be a stronger mediator of the association between Relational

Aggression and the Child Social Problems Composite for either boys or girls. Because the Relational Aggression x Gender interaction was approaching significance in the Child

Internalizing Composite, separate models for boys and girls were also tested in this case.

As outlined by Baron and Kenny (1986), a fundamental test of a mediational model involves four steps. The effects of Gender, SES, and PA were controlled for at tests of each step. The basic model is depicted below in Figure 3.1.

2 3

Relational 1 RA Correlate Aggression

Figure 3.1. Mediation of the association between Relational Aggression and RA Correlate by RA

At the first step, Relational Aggression must be significantly correlated with the

RA correlate, after controlling for PA. As described above, only those variables for which this relation was significant were tested for mediation. Second, Relational

Aggression must be significantly predictive of RA. This relation was examined in the initial partial correlations calculated above for child, parent, and teacher reports, and the results indicated that even after controlling for PA, RA still incrementally contributed to the prediction of Relational Aggression for each of the variables tested. Third, after controlling for PA and Relational Aggression, RA (when added into the base model) must significantly contribute to the variance accounted for in the RA correlate. When

RA was added into a model already including PA and Relational Aggression, the effect of RA was significant in predicting each of the RA correlates considered (for Child

Inattention ∆R2 = .056, p = .000 and for Parent Anxious / Depressed ∆R2 = .031, p =

.003). Fourth, the association between Relational Aggression and the RA correlate must

be reduced by the addition of RA into the model. However, the correlation between

Relational Aggression and the RA correlates of Child Inattention composite and Parent

Anxious / Depressed remained significant (B = .117, p = .051 and B = .209, p = .002,

respectively), suggesting that RA may, at best, only partially mediate the associations

between Relational Aggression and these RA correlates. The results of these analyses are

presented in Table 8.

Given that the Relational Aggression x Gender interaction term was significant in

predicting Child Social Problems, separate mediation models were tested for boys and

girls. When RA was added into a model already containing PA and Relational

Aggression for boys, the effect of RA in predicting Child Social Problems was significant

(∆R2 = .051, p = .006). Further, the addition of RA to the model rendered the effect of

Relational Aggression nonsignificant (B = .077, p = .362), suggesting that RA is a full mediator of the association between Relational Aggression and Child Social Problems for boys. Full mediation was also supported for the association between girls’ self-reports of

Relational Aggression and Child Social Problems. Specifically, at the final step of the girls’ model, RA was a significant predictor of Child Social Problems (∆R2 = .059, p =

.027) and the effect of Relational Aggression became non-significant (B = -.065, p =

.510). Taken together, these results suggest that RA is a full mediator of the association between Relational Aggression and Child Social Problems for both boys and girls.

Tests of the mediating role of RA in the association between Relational

Aggression and Child Internalizing Problems revealed different patterns with respect to

the mediating role of RA for boys and girls. Specifically, RA contributed significantly to

the variance accounted for in Child Internalizing problems when added to the base model

for boys (∆R2 = .048, p = .005), and the effect of Relational Aggression became non-

significant (B = .060, p = .492), suggesting that RA is a full mediator of this association for boys. In contrast, the addition of RA to the base model for girls was not associated with an increase in the amount of variance accounted for (∆R2 = .003, p = .643). Further,

Relational Aggression was not a significant predictor of Child Internalizing problems for

girls (B = -.075, p = .466), suggesting that Relational Aggression is not associated with

Child Internalizing problems for girls. The results of these analyses are presented in

Table 8.

Mediation Analyses - PA

Mediation models were likewise tested for those cases in which Relational

Aggression was found to be significantly correlated with established PA correlates (Child

and Parent CU Traits, Child and Parent Delinquency, and Teacher Oppositional).

Although the correlation between Relational Aggression and Teacher CU Traits did not

achieve statistical significance (∆R2 = .013, p = .067), this model was also tested. These

models were tested in the same way as the tests of RA mediation outlined above.

As described previously, the first path in these models was tested by an

examination of the unique associations between Relational Aggression and PA correlates

(which were all statistically significant except for Teacher CU Traits, which was only

marginally significant). For the tests of the second step, the initial partial correlations

calculated in the basic model indicated that even after controlling for RA, PA was still

uniquely predictive of Relational Aggression for child, parent, and teacher reports.

Tests of the third step, or the addition of PA into a model already including

Relational Aggression and RA, indicated that the effect of PA contributed significantly to

the prediction of all of the variables tested (for Child Delinquency, ∆R2 = .119, p = .000; for Child CU Traits, ∆R2 = .022, p = .022; for Parent Delinquency, ∆R2 = .137, p = .000;

for Parent CU Traits, ∆R2 = .036, p = .003; for Teacher Oppositional, ∆R2 = .065, p =

.000, and for Teacher CU Traits, ∆R2 = .059, p = .000). An examination of the

correlations between Relational Aggression and the respective PA correlate at the third

step of each model was suggestive of full mediation for the Child CU Traits (B = .112, p

= .156), Parent Delinquency (B = .063, p = .271) and Teacher CU Traits (B = .027, p =

.741) models, and partial mediation for the Child Delinquency, Parent CU Traits (B =

.138, p = .054) and Teacher Oppositional models (B = .128, p = .042; B = .121, p = .037).

That is, the addition of PA to all of the models tested resulted in a decrease in the association between Relational Aggression and the PA correlates in all cases. The results of these analyses are presented in Table 9.

Given that the Relational Aggression x Gender interaction term was significant in the Teacher Oppositional model, a PA mediation model was tested separately for boys and girls. For boys, the addition of PA into the base model resulted in a significant increase in the amount of variance accounted for in Teacher Oppositional (∆R2 = .105, p

= .000). At this step, the effect of Relational Aggression became non-significant (B = -

.006, p = .939), suggesting that PA is a full mediator of the association between

Relational Aggression and Teacher Oppositional. In contrast, although for girls PA was

also significant when added to the base model (∆R2= .038, p = .001), the effect of

Relational Aggression remained significant (B = .163, p = .050). This suggests that PA is

only a partial mediator of the association between Relational Aggression and Teacher

Oppositional for girls. The results of the tests of these models are presented in Table 9.

Follow-Up Analyses

In order to test for the potential effects of reporting biases related to social

desirability concerns, all child and parent regression analyses were repeated entering

RCMAS Lie Scale scores at the first step. Controlling for Lie Scale scores changed the

results of the models for Child and Parent CU traits, as well as Parent Delinquency.

Specifically, the correlation between Relational Aggression and each of these PA

correlates was non-significant when added to a model already including Child or Parent

Lie Scale scores (for Child CU traits, ∆R2 = .008, p = .184; for Parent CU traits, ∆R2 =

.014, p = .060, and for Parent Delinquency, ∆R2 = .008, p = .103). In all of these models,

the Child or Parent Lie Scale score was a significant predictor of the variable of interest

(for Child CU traits, t = -3.64, p = .000; for Parent CU traits, t = -2.57, p = .011, for

Parent Delinquency, t = -3.15, p = .002).

Multitrait-Multimethod Matrix Analyses

Whereas regression analyses are appropriate for examining relations between

manifest variables, structural equation modeling (SEM) approaches are more appropriate

for investigations of latent constructs (Kenny, 1979). That is, whereas the first phase of

analysis was restricted to an examination of relations between measured variables, the

56 second phase of data analysis attempted to model the associations between relational aggression, RA, and PA at the construct level.

A multitrait-multimethod (MTMM) matrix approach was used to evaluate the construct validity of relational aggression. Consistent with the recommendations of

Kenny and Kashy (1992), the reports of children, parents and teachers were analyzed using a correlated uniqueness model for multitrait-multimethod data. In this confirmatory factor analytic approach, no method factors are created, and trait factors are permitted to be correlated across measures of the same method. The trait factors are allowed to correlate with each other, and the factor loadings are allowed to vary. Using this model, method effects are inferred by the degree of covariation between the trait factors (specifically, a trait is viewed as being comprised of error plus a method factor).

A correlated uniqueness model is considered to be advantageous because method effects are allowed to be unidimensional (that is, they are permitted to vary across measures using the same method). For example, this would allow for a case in which a parent underestimates his child’s level of aggression and overestimates his child’s level of inattention.

A correlated uniqueness model is identified with a minimum of two traits and three methods (Kenny & Kashy, 1992). In this study, three methods (child self-report, parent report, and teacher report) were considered. The primary trait factors examined included: Relational Aggression, RA, and PA. In order to test the nature of the associations between these three aggression trait constructs and other relevant constructs, the trait factors of: Social Problems, Internalizing Problems, Inattention, Delinquency, and CU Traits were all added to the core model. However, due to statistical power

57 limitations, each of these additional constructs was added to the base model separately

(i.e., the resulting models included Relational Aggression, RA, PA, and one additional trait factor). As a result, there were five models tested for both boys and girls: Social

Problems, Internalizing Problems, Inattention, Delinquency, and CU Traits.

Convergent and discriminant validity as well as method effects were all tested through the process of comparing specified hierarchically nested models, as described by

Widaman (1985). In this approach, a comparison of the fit between two nested models is explored by evaluating the chi-square difference between the two models. If the difference is significant, the null hypothesis is rejected and there is evidence to support the notion that one model provides a better fit to the data than the other model. In all cases, model fit was evaluated based on standard model fit indices, such as chi-square test of fit and root mean square error or approximation (RMSEA). Overall, standard guidelines for the evaluation of model fit generally suggest that values of RMSEA below

.08 are “acceptable”, with values below .05 or .06 considered to be indicative of “good fit” (Hu & Bentler, 1999; Browne & Cudeck, 1993).

Test of Differences in Structural Models for Boys and Girls

In order to test the hypothesis that the relations between the constructs of interest were different for boys and girls, models in which parameter values were estimated separately by gender were tested. In the first model, factor loadings were permitted to vary across the models for boys and girls (X2 = 87.71, df = 60, p = .011, RMSEA = .064), and in the second (nested) model, factor loading values were constrained to be equal for both boys and girls (X2 = 244.03, df = 108, p = .000, RMSEA = .107). The significant

58 decrease in model fit when the model for boys and the model for girls were constrained to

2 be equal (X difference = 156.32, df = 48; p < .01) suggested that factor loading values were not the same for boys and girls. As a result, all subsequent analyses were considered separately for boys and girls.

Model Fit

Plausible solutions were available for almost all of the trait models specified.

However, plausible solutions could not be estimated for either the girls’ Internalizing or

CU Traits models. Therefore, in order to gain a rough estimate of the trends in these models, the data from the entire sample (both boys and girls) were combined and convergent validity, discriminant validity, and method effects were estimated from that model. Comparisons between these combined models and the boys’ models allowed for a rough estimate of the trends for girls in those cases.

Both the boys’ and girls’ models for Delinquency also yielded impossible solutions. Specifically, in both models, the trait correlations between Relational

Aggression and Delinquency were estimated at greater than r = 1.0. This suggests that the discriminant validity of Relational Aggression is very poor and that a model which does not attempt to distinguish between Relational Aggression and Delinquency may more appropriately reflect the nature of the associations between the variables of interest.

Due to the implausibility of this model, the models for both boys and girls were re- calculated with the specification of only three factors instead of four factors.

Specifically, the Delinquency measures (for child, parent, and teacher) were specified to load onto the Relational Aggression factor.

Overall, when no restrictions were placed on the basic models for both boys and girls, the model fit (as evaluated by generally accepted guidelines for RMSEA values described above) was either “good” or “acceptable”. Specifically, the RMSEA values for the boys’ models ranged from .039 (Delinquency model) to .083 (CU Traits) and the

RMSEA values for the girls’ models ranged from .017 (Social Problems) to .091

(Inattention). The models for Boys and Girls are depicted in Figures 4 and 5. These models, in which all parameters were permitted to be estimated freely, represented the base comparison models for all subsequent tests for boys and girls.

Tests of Convergent Validity

Convergent validity was assessed by examining the significance of the factor loadings across all three reporters (i.e., methods). That is, because all factor loadings in this model are comprised of both trait and method variance, non-significant factor loadings (i.e., evidence of low trait variance) implies that method variance is accounting for more influence on factor scores than the actual construct of interest (Kenny & Kashy,

1992). In contrast, significant factor loadings are suggestive of a greater relative contribution of trait variance to observed trait scores.

Overall, there was consistent support for the convergent validity of all of the trait factors considered in both the boys’ and girls’ models. Specifically, across virtually all of the models for both boys and girls, the factor loadings for child, parent, and teacher report variables were all significant (all p-values < .01, p < .05). In a relatively small number of cases, some isolated factor loadings for both RA and Relational Aggression were non- significant. Specifically, in the boys’ models, there were non-significant factor loadings

for Child RA (z = 1.615, p = .106) for the CU Traits model, Teacher RA (z = 1.744, p =

.081) for the Delinquency model, and Child Relational Aggression (z = 1.745, p = .081) for the Delinquency model. For the girls’ models, the factor loadings for both Child

Relational Aggression (z = 1.340, p = .180) for the Delinquency model and Teacher

Relational Aggression (z = 1.495, p = .135) for the Social Problems model were non-

significant. Although these non-significant factor loadings are largely isolated, they

provide some slight suggestion that method variance may be accounting for relatively

more variance in both Relational Aggression and RA scores than trait variance (i.e.,

evidence of low convergent validity).

Tests of Discriminant Validity

In order to assess the discriminant validity of the constructs of interest, a series of

models was tested in which various correlations between constructs were constrained to

have fixed perfect (r =1) correlations between trait factors. In essence, such a model is

designed to test the hypothesis that a single trait factor fits the data as well as having two

separate, but correlated, trait factors. That is, because a perfect correlation between two

latent variables implies an inability to empirically distinguish between the two constructs,

if such a model were to fit the data as well as a model in which the correlations between

trait factors were allowed to be freely estimated, it would suggest that there is little

support for discriminant validity among the latent constructs (Widaman, 1985). In testing

the discriminant validity of the constructs, correlations between pairs of constructs were

tested in separate models in order to determine which paths (i.e., correlations) were

affecting model fit. For each model comparison, a X2 difference test with 1 degree of

freedom was tested for the constrained model versus the basic model. A significant

increase in X2 associated with a model that was constrained to have a perfect correlation

between two trait factors was suggestive of worse model fit. That is, a significant value

for the X2 difference test implies that a model in which separate factors are created for the

two traits represents a better fit to the data (i.e., evidence of discriminant validity).

For the tests of the discriminant validity of the constructs of interest for boys,

there was support for the distinction between all trait constructs except for Relational

Aggression and PA. That is, tests of the difference when correlations between relevant

2 constructs were constrained to r = 1 yielded significant X difference values for the distinctions between RA and PA, Relational Aggression and RA, and Relational

Aggression and the additional construct (e.g., Social Problems). In contrast, a test of the difference in model fit when the correlation between the trait constructs of Relational

Aggression and PA was constrained to r = 1 only resulted in a significant increase in X2

2 for the Inattention model (X difference = 7.86, p < .05). For all other models, the model fit

did not substantially increase when Relational Aggression and PA were constrained to be

equal, suggesting that there is little evidence for a distinction between these two

constructs among boys.

For the tests of discriminant validity for girls, there was only support for the

2 discriminant validity of Relational Aggression and Social Problems (X difference = 5.13, p =

.02). Across all other models, there was no support for the discriminant validity of the

trait constructs. Specifically, there was no support for the discriminant validity of RA

and PA, or for Relational Aggression and: RA, PA, or Inattention. Finally, as described

above, because it was not possible to estimate plausible models for either girls

Internalizing or CU Traits, models utilizing data from the whole sample were also tested.

Likewise, in both combined models, there was not support for the discriminant validity

between Relational Aggression and either RA, PA, or CU Traits. This change in trend

from support for the distinction between trait constructs when tested in a model of solely

boys and lack of support for such a distinction when girls are also included in the

analyses further supports the observed trend toward less of a distinction between the

constructs among boys than girls. It should be noted that although there was support for

a distinction between Relational Aggression and Internalizing Problems in the combined

2 model (X difference = 8.23, p = .00), it is not possible to determine whether this pattern would be evident in a sample of just girls.

Finally, it should be noted that the need to permit the Delinquency measures to

load onto the same factor as the Relational Aggression measures in order to converge on

plausible solutions for both boys’ and girls’ models suggests that there is no support for

the distinction between Relational Aggression and Delinquency.

Tests of Method Effects

In order to test for method effects, models in which all correlations between

measured variables using the same method (child, parent, or teacher reports) were

constrained to r = 0 were compared to the base model (in which the correlations were estimated freely). In each case, if X2 increased when the correlations were fixed to 0, there was support for a significant correlation between measures by the same reporter

(that is, there was evidence to suggest that measures by the same reporter tended to be

correlated with each other). Method effects were tested separately for boys and girls,

except for the two models using data from both boys and girls (Internalizing Problems

2 and CU Traits). For all analyses, X difference statistics with 6 degrees of freedom were calculated for child, teacher, and parent reports of boys’ and girls’ behaviors.

For the boys’ models, there was consistent evidence of method effects for parent

2 2 reports (for Social Problems model, X difference = 22.88, for Internalizing model, X difference

2 2 = 20.41, for Inattention model, X difference = 26.82, for CU Traits model, X difference =

2 20.57, for Delinquency 3-factor model, X difference = 35.65; all p-values < .05). This

indicated that the model fit became significantly worse when correlations between parent

measures were constrained to zero. There was much less consistent evidence for method

effects for either child or teacher reports. Specifically, child method effects were only

2 2 apparent in the Social Problems (X difference = 16.73) and Inattention (X difference = 31.04)

2 models. Teacher method effects were only evident in the Delinquency model (X difference

= 29.16).

For the girls’ models, there was consistent evidence for method effects for both parent reports and child self-reports. In all models, the X2 difference values were significant when correlations between measures by either parents or children were constrained to zero. In contrast, there was no evidence of a method effect associated with teacher reports across any of the models tested.

CHAPTER 4

DISCUSSION

The primary aim of this study was to investigate the construct validity of relational aggression. This goal was addressed through two main approaches: hierarchical regression analyses and latent variable modeling (i.e., MTMM). Each approach allowed for a unique framing of the fundamental research questions and, taken together, the results from both methods contributed to a preliminary framework for conceptualizing relational aggression. For the most part, the present results were predicted and/or supported by existing theory and research. Broadly, there was evidence that relational aggression may be both proactive and reactive in nature. Further, the data indicated that relational aggression may be associated with different implications for boys and girls.

Hierarchical Regression Analysis Results

The hierarchical regression analyses allowed for examinations of shared variance among measures of subtypes of aggression and other relevant behaviors and characteristics (e.g., social problems, CU traits). At a fundamental level, child, parent,

and teacher reports supported the hypothesis that both reactive and proactive aggression

are uniquely associated with relational aggression.

Relational aggression was associated with the reactive aggression correlates of

child-reported inattention and parent-reported anxious depressed behaviors. There was a

significant Relational Aggression x Gender interaction in predicting child social

problems, indicating that whereas higher levels of relational aggression were associated

with higher levels of self-reported social problems for boys, the same trend was not

apparent for girls. Instead, girls who reported higher levels of relational aggression

actually tended to report relatively lower levels of social problems than girls with low (-1

SD) levels of relational aggression. The (marginally significant) Relational Aggression x

Gender interaction in predicting child internalizing problems was suggestive of a

tendency for relational aggression to be associated with internalizing problems for girls,

but not for boys. Rather, boys who reported high levels of relational aggression actually

tended to report lower levels of internalizing problems.

With respect to proactive aggression correlates, relational aggression was

significantly correlated with all three informants’ ratings of delinquency / oppositional

problems, as well as child and parent reports of CU traits. The correlation between

teacher-reported relational aggression and CU traits was marginally significant. There was also a Relational Aggression x Gender interaction in predicting teacher-rated

oppositional behaviors. Specifically, higher levels of teacher-reported relational

aggression were associated with lower ratings of oppositional problems for boys relative

to girls. Follow-up analyses indicated that the correlation between Relational Aggression

and the PA correlates of: Child and Parent CU traits and Parent Delinquency was not

66 significant after controlling for social desirability. This suggests that social desirability is a stronger predictor of child and parent endorsements of CU traits or delinquent behaviors than relational aggression.

Mediation models were tested for cases in which relational aggression was associated with established correlates of either reactive or proactive aggression (e.g., social problems, CU traits). Tests of these models largely supported the hypothesis that the observed associations were mediated by either reactive or proactive aggression.

Specifically, these models demonstrated that the associations between relational aggression and other relevant variables were attributable to associations between relational aggression and either reactive or proactive aggression. That is, as reported by children, parents, and teachers, relational aggression was accounting for essentially the same portion of variance in other relevant variables of interest as both reactive and proactive aggression.

The use of hierarchical regression analyses provided a powerful method for analyzing the nature of the associations between measures of the relevant constructs.

However, these analyses only provided information about shared variance between child, parent, and teacher measures when considered separately by informant. Further, it was not possible to directly test whether parallel measures were assessing the same construct.

For example, it was not possible to determine whether the relational aggression measure was tapping into the same construct across all three sets of reporters. In addition, it is important to note that regression results may reflect rater bias (although this may be less likely to be true for the observed interactions). That is, correlations between related constructs may have been inflated because the same rater’s scores were compared for all

67 constructs of interest. However, direct tests of potential method effects were available within the MTMM approach. The MTMM analyses also allowed for additional, more global tests of the associations between relational aggression, reactive aggression, and proactive aggression at the construct level.

MTMM Results

The MTMM approach allowed for statistical analysis of the convergent and discriminant validity of the relevant trait constructs as well as direct tests of potential method effects associated with informants’ reports. The estimation of separate models for boys and girls allowed for a delineation of gender differences in the nature of associations between trait factors. Due to statistical power limitations, separate models were estimated for each of the additional relevant trait factors. That is, the trait factors of: relational aggression, reactive aggression, and proactive aggression were included in different models with one additional trait factor selected for inclusion in the structural models. As a result, five models were tested for both boys and girls: Social Problems,

Internalizing Problems, Inattention, Delinquency, and CU Traits. For the most part, the indices of fit for both the boys’ and girls’ models suggested that the hypothesized structural models (i.e., distinct, but correlated, trait factors as assessed by children, parents, and teachers) were consistent with the observed data.

Children’s, parents’, and teachers’ reports demonstrated consistent evidence for the convergence of ratings of: proactive aggression, social problems, internalizing problems, inattention, delinquency, and CU traits across all boys’ and girls’ models. That is, all three sets of informants tended to be in agreement about children’s levels of these

68 behaviors. In contrast, there was slightly less support for the agreement between reporters’ ratings of both relational aggression and reactive aggression. Specifically, a small number of factor loadings for Relational Aggression and RA were non-significant across some of the boys and girls’ models.

Tests of discriminant validity yielded largely contrasting results for girls and boys. Specifically, whereas for boys there was consistent evidence for the discriminant validity of the majority of constructs of interest, for girls, there was only support for the distinction between relational aggression and both social problems and internalizing problems. That is, while there was support for the distinction between all relevant constructs except for relational aggression and proactive aggression in the boys’ models, there was no support for the discriminability of relational aggression and: reactive aggression, proactive aggression, inattention, or CU traits in the girls’ models. This suggests that the constructs of interest were too similar to be distinguished among girls.

Finally, the complete inability to estimate plausible solutions for models specifying delinquency as a separate factor from relational aggression for either boys or girls provides especially strong evidence that these constructs, as currently assessed, are too similar to be distinguished.

Method effects were identified for parent reports in both boys’ and girls’ models.

These results suggest that when reporting about their children’s behaviors, parents demonstrate a tendency to respond to measures in the same way (for example, to rate their child as generally aggressive across all aggression items and subscales, regardless of the specific content of the items). There was also some evidence across the boys’ and girls’ models for method effects associated with child self-reports, suggesting that

children may also demonstrate a reporting bias when responding to items about their own

behaviors.

Relational Aggression and RA / PA Subtypes of Aggression

As hypothesized, both reactive and proactive aggression appeared to be uniquely associated with child, parent, and teacher reports of relational aggression. The apparent mediating roles of both reactive and proactive aggression in accounting for associations between relational aggression and measures of other relevant constructs provide additional information about the overlap between relational aggression and the constructs of both reactive aggression and proactive aggression. That is, the data suggest that the associations between relational aggression and such relevant variables as social problems, delinquency, or CU traits can be accounted for by the associations between relational aggression and either reactive or proactive aggression.

The pattern of results outlined above is also consistent with the hypothesized presence of relational aggression subtypes. That is, the evidence of shared variance between measures of relational aggression and both reactive and proactive aggression implies that relational aggression may reflect both reactively and proactively aggressive characteristics. From this perspective, the “reactive subtype” of aggression would presumably account for the shared variance with measures of reactive aggression, and the

“proactive subtype” of relational aggression would presumably account for the shared variance with proactive aggression measures. For example, if it were the case that some relationally aggressive children are more reactive in their aggressive behaviors while other relationally aggressive children utilize relationally aggressive behavior in more

70 proactively aggressive ways, the associated pattern of data might resemble the present results. However, it is important to emphasize that the present data are merely consistent with the possibility that this pattern of shared variance reflects distinct types of children.

Such a hypothesis was not tested in this study and therefore further investigation is necessary to determine whether there is direct evidence to support the existence of relational aggression subtypes.

Gender Differences in Relational Aggression

Apparently, the nature of relational aggression as well as the patterns of its associated implications is different for boys and girls. Specifically, gender differences were apparent in both the hierarchical regression and latent variable modeling approaches to data analysis. However, in considering the implications of such gender differences across data analytic approaches, it should be noted that because it was not possible to estimate plausible solutions for either the girls’ internalizing problems or CU traits models, the results from the MTMM analyses should be interpreted cautiously. It is also important to emphasize that whereas the hierarchical regression analyses reflect patterns of shared variance between measured variables, the MTMM analyses reflect attempts to model associations between trait constructs. From this perspective, fundamental differences between the data analytic approaches must be considered when attempting to draw conclusions about the results as a whole.

Before addressing potential gender differences in aggression, it is worth noting that reported levels of relational aggression were actually largely similar for boys and girls in the present study. That is, although girls are theorized to exhibit higher levels of

71 relational aggression than boys, in this study only parents rated girls higher than boys on relational aggression. No gender differences in mean levels of relational aggression were apparent for either child self-reports or teacher reports of relational aggression. Further, proactive aggression was the only other aggression subtype considered in this study that demonstrated gender differences. Specifically, both boys and their teachers reported higher levels of proactive aggression than girls and their teachers.

Nonetheless, despite the indication that boys and girls demonstrate similar levels of aggression on average, there were several noteworthy gender differences in the specific patterns of associations between trait constructs. For example, whereas the tests of discriminant validity across models suggested that relational aggression is largely independent from reactive aggression for boys, relational aggression, reactive aggression, and proactive aggression all appear to be associated with each other for girls. However, as noted above, the results of the MTMM analyses are based on the patterns of covariance across child, parent, and teacher measures for all subtypes of aggression.

Therefore, in this approach, trait constructs reflect the common portion of shared variance across all three reporters. As a result, although evidence may suggest that at the construct level there is a distinction between relational aggression and reactive aggression for boys, this does not negate the possibility of shared variance between individual measures of these constructs (i.e., from the perspective of just one informant). For example, tests of

RA mediation were supported for boys, indicating that there is, in fact, shared variance between boys’ reports of relational and reactive aggression.

At a global level, there are several potential explanations for these observed differences in the nature of the associations between trait constructs for boys and girls.

For example, evidence of apparent discreteness between most forms of aggression for boys may reflect fundamental differences in standards or expectations for behavior.

Perhaps because aggressive behaviors in boys are viewed as somewhat “typical” (e.g.,

“boys will be boys”), ratings of boys’ aggressive behaviors are viewed as less global and pervasive. Specifically, a boy may be more readily viewed as exhibiting just one, isolated form of aggression without necessarily being perceived as globally aggressive.

In contrast, aggressive behaviors in girls in one domain may be perceived as likely to be indicative of manifestation of aggression across a wider variety of domains.

Similarly, another potential explanation for this trend toward a greater overlap among all forms of aggression for girls relative to boys is that girls who are aggressive may be both more extreme and diverse in their aggression. This perspective is reflected in the “group resistance” hypothesis. Specifically, this hypothesis is based on the position that because males are more likely to be socialized toward aggressive behaviors than females, an aggressive female is more likely to be viewed as deviant, whereas an aggressive male is likely to be viewed as conforming (Sellin, 1938). From this perspective, because females are bombarded with stronger societal messages discouraging aggressive behaviors, females who “resist” such influences and nonetheless exhibit aggressive behaviors are more likely to be manifesting aggressive behaviors as the result of some individual psychopathology (Sellin, 1938). It follows that girls who are relatively more severe in their aggressive behaviors (and presumably have higher levels of underlying psychopathology) might also be expected to demonstrate a broader scope of problems overall. For example, comorbidity rates of such problems as

73 depression and anxiety in clinical samples are often much higher than those in non- referred populations (Cole, Truglio, Peeke, & Lachlan, 1997).

On a related note, some researchers have suggested that the DSM criteria for diagnosing Conduct Disorder may be less sensitive to the identification of antisocial behaviors in females than males (Zoccolillo, Tremblay, & Vitaro, 1996; Zoccolillo,

1993). Specifically, it has been suggested that changes to the number or type of required symptoms for diagnosing Conduct Disorder in girls may make the criteria more sensitive to the identification of girls with problem behaviors (Zoccolillo, 1993). For example, females are more likely to exhibit covert forms of aggression, such as shoplifting, which are logically more difficult to detect than the more overt forms of aggression often exhibited by males (Delligatti, Akin-Little, & Little, 2003). It follows that to the extent that a female demonstrates more overt forms of aggression, such a female may be more likely to have extreme levels of psychopathology (Delligatti et al., 2003). Similarly, this perspective also implies that girls must exhibit more severe forms of aggression (relative to other girls) in order to be identified as aggressive when compared to the spectrum of behaviors for children overall (i.e., both boys and girls).

Child Social Problems

It is logical to expect relational aggression to be associated with overall peer rejection for both boys and girls. For example, existing research indicates that relationally aggressive boys and girls report distress and unhappiness about their relationships, even after taking levels of physical aggression into account (Crick &

Grotpeter, 1995). On the one hand, relationally aggressive acts may contribute to social

74 problems by limiting children’s access to peer relationships. Because of its harmful nature, relational aggression is likely to be perceived negatively by children. Children who exhibit such behaviors are therefore likely to be viewed unfavorably by their peers.

Likewise, it is plausible that social rejection promotes the use of relational aggression. A socially rejected child may resort to the use of relational aggression to retaliate for a lack of success or exclusion from other social groups (Crick & Grotpeter). In either case, relational aggression is largely expected to be associated with peer problems for both boys and girls.

However, the results from the hierarchical regression analyses suggest that relational aggression may be associated with greater peer problems for boys than for girls, at least as reported by children themselves. Such a pattern is consistent with previous findings which suggest that relational aggression may be associated with more extreme and diverse social, emotional, and adjustment problems for boys than girls

(Crick et al., 1999; Crick, 1997; Crick & Grotpeter, 1995). Boys with high levels of relational aggression are less likely to have a mutual friend and have fewer friendships overall than girls with high levels of relational aggression (Crick, 1997). Further, children who engage in gender non-normative forms of aggression (i.e., physical aggression for girls, relational aggression for boys) have been found to be more socially and emotionally maladjusted than children who engage in normative forms of aggression

(Crick, 1997).

Gender atypical aggressive behavior may be associated with detrimental outcomes for children for several reasons. Physical aggression has been reported to be more common among boys, with relational aggression being more common among girls

(Crick, Bigbee, & Howes, 1996; Crick et al., 1996), and boys have been shown to be more accepting of physical aggression than girls (Huesman, Guerra, Zelli, & Miller,

1992). From this perspective, gender “unexpected” behavior is likely to lead to more social rejection and negative perceptions by others than is behavior that is more gender normative (Crick, 1997). For example, relational aggression in girls (although it may be relatively frequent or extreme) may be generally more accepted than the same behavior in boys. The associated negative reactions from others in response to gender atypical behaviors are likely to contribute to increases in psychological adjustment problems and poorer overall functioning (e.g., these children may be ostracized or rejected by their peers because of their “unusual” behaviors).

Another factor that may contribute to the greater association between relational aggression and social problems for boys than girls is the possibility that relationally aggressive boys are more likely to also exhibit overt or physical forms of aggression. For example, in one sample of third and sixth grade children, boys who were high in relational aggression also demonstrated high levels of overt aggression, whereas many girls (largely in the sixth grade) were found to demonstrate high levels of relational aggression in the absence of notable levels of overt aggression (Rys & Bear, 1997).

Given that relationally aggressive boys may be more likely than relationally aggressive girls to also be physically aggressive, it is reasonable to expect that such boys would therefore be at greater risk for rejection by their peers.

Thus far, potential explanations for why relationally aggressive boys may experience more social problems than relationally aggressive girls have focused solely on the greater likelihood of detrimental effects for boys relative to girls. However, it is also

76 possible to identify plausible reasons why relationally aggressive girls may actually be more successful in their social relationships than relationally aggressive boys. On the one hand, studies have demonstrated that whereas girls, in particular, perceive overtly aggressive behaviors to be disturbing, they are more accepting of verbal aggression as a method for resolving peer conflicts (Rys & Bear, 1997). This suggests that girls may be less distressed by the demonstration of relational aggression in other girls and therefore may be less likely to reject a relationally aggressive girl. Such a possibility is supported by existing research. For example, in their sample of third and sixth grade boys and girls,

Rys and Bear (1997) reported that the percentage of relationally aggressive girls who had one or more reciprocal friends did not differ from the percentage of nonaggressive girls who had one or more reciprocal friends. Apparently, relationally aggressive girls are accepted by at least some of their peers.

In addition, many of the traits required to make a child successful at being relationally aggressive are the same traits that are associated with elevated social influence and leverage for girls. For example, relationally aggressive children are presumably effective at controlling the acceptance or exclusion of peers into their friendship groups. Exclusivity is one hallmark characteristic of relationally aggressive children’s friendships, and such exclusivity implies the existence of a strong sense of cohesion and unity within the friendship group. Further, the ability to sway friends’ opinions or direct their behaviors (for example, by convincing one’s friends to snub another peer) logically requires a certain degree of acceptance by a particular group of friends. That is, relationally aggressive children apparently have the social approval and acceptance of at least some other peers (Crick & Grotpeter, 1995; Rys & Bear, 1997).

Finally, relationally aggressive children’s friendships do not appear to be generally negative in nature. For example, friendships of relationally aggressive children do not appear to differ from those of their non-aggressive peers in such positive aspects of friendship as validation, caring, companionship or recreation (Grotpeter & Crick, 1996).

Even the friendship qualities such as intimacy or self-disclosure that appear to differentiate relationally aggressive children from non-aggressive children’s friendships are traditionally regarded as positive attributes of a friendship. Although self-disclosure is also associated with the ability to ultimately harm friends (e.g., by divulging their secrets to others or exercising control over another individual by threatening to divulge such information), such a characteristic implies, at least at the surface level, the existence of a strong friendship bond.

In particular, the characteristics outlined above are expected to be associated with social success more for girls than for boys because of fundamental gender differences in social goals. For example, the concept of relational aggression stemmed from the expectation that girls are more likely to covet popularity and security within social groups, whereas boys are more likely to emphasize instrumentality and dominance- oriented goals (Crick, 1996). To the extent that relational aggression reflects skill in establishing strong friendship allegiances and social status security (as opposed, for example, to being reflective of physical dominance), relational aggression is therefore more likely to be associated with perceptions of greater social success for girls than for boys. In sum, given the characteristics associated with relational aggression, it may not be surprising that relational aggression is not associated with ratings of social rejection or friendship problems for girls.

It should also be emphasized that the distinction between success in dyadic / intimate friendship circles versus success in terms of overall sociometric status is important when attempting to draw conclusions about social prowess. For example,

Crick and Grotpeter (1995) found that children with a “controversial” sociometric status

(i.e., accepted by some peers, rejected by other peers) received the highest relational aggression scores. This suggests that although relationally aggressive girls may be accepted by some peers, (e.g., they may have some reciprocal friendships), they may also be disliked by other peers. It has been suggested that “concealed competencies” of such aggressive children might allow for their success with some peers (Cairns, Cairns,

Neckerman, & Gest, 1988). For example, such prosocial behaviors as helpfulness or interpersonal sensitivity of relationally aggressive children may allow them to be successful in some friendship contexts (Rys & Bear, 1997). From this perspective, it is possible that relationally aggressive girls in this sample did not rate themselves as having high levels of social problems because they are actually socially competent and accepted by at least some of their peers. Further, clarifying the scope of social rejection (i.e., rejected by only some peers versus rejected by most peers) also offers one way in which to reconcile the suggestion that relational aggression may be associated with perceptions of both social acceptance and social rejection.

Finally, it is important to note that the gender interaction for relational aggression and social problems was limited to self-reports. From this perspective, the findings from the MTMM analyses that the trait constructs of relational aggression and social problems are correlated for both boys and girls can be reconciled by acknowledging that the

MTMM results reflect the combined perspectives of children, parents, and teachers.

Internalizing Problems

The indication in the present study that relational aggression is associated with internalizing problems for girls but not for boys may reflect the confounding effect of fundamental gender differences in levels of internalizing problems. For example, in one sample of second and third grade students, girls (but not boys) who received high peer ratings on a measure of relational aggression were also rated by their peers as being sad, shy, or withdrawn (Henington et al., 1998). The authors of this study suggested that these findings might have been a reflection of the greater likelihood that physical aggression, which is less strongly associated with internalizing problems, is also occurring in boys.

Similarly, the manifestation of some underlying psychopathology (associated on the one hand with the demonstration of aggressive behavior) may be more likely to be manifested in internalizing problems for girls (Zahn-Waxler, 1993). For example, there is support for a developmental sequence in which girls with early conduct problems develop depression at later ages (Loeber & Keenan, 1994, Zoccolillo, 1992; Moffit et al.,

2001). Internalizing problems are typically more prevalent among girls than boys among middle-school-aged children. This difference may reflect either differences in gender- role socialization influences or other characteristics of cognitive or emotional development (Keenan & Shaw, 1997). From this perspective, the manifestation of psychopathology in girls may simply be more likely to be expressed in internalizing problems for girls than for boys.

Convergent Validity

Tests of convergent validity in the present study suggest that children, parents, and teachers offer similar perspectives on the behaviors considered in this study. Only relational aggression and reactive aggression demonstrated somewhat inconsistent support for convergent validity. In the case of these two constructs, it should be noted that the low levels of agreement between reporters are not unique to this sample. For example, low correlations between teacher and adolescent ratings of problem behavior are typical (Conners, 2000; Crick et al., 1999).

The suggestion of a lack of convergent validity for both relational aggression and reactive aggression was limited to a relatively small number of non-significant factor loadings and should be interpreted with extreme caution. Nonetheless, given that non- significant factor loadings across models were only observed for relational and reactive aggression, it is possible that these particular constructs are associated with relatively lower levels of convergent validity. Although the potentially low convergent validity of relational and reactive aggression may reflect unreliability of the measures and/or informants considered in this study, it may also reflect a variety of other causes (Garber

& Strassberg, 1991). For example, children’s behavior may differ across situations in which relevant informants are observing children (e.g., school vs. home) (Achenbach et al., 1987). Specifically, a child might be expected to be more likely to exhibit relationally aggressive behaviors at school than at home. Different reporters may also vary in their standards or norms for comparison of children’s behaviors. For example, teachers are likely to have a more extensive knowledge base of normative behaviors for

81 boys and girls than parents. On the other hand, parents may have a better sense of a child’s behaviors across a broader range of contexts or situations because they have had more intimate contact with a particular child, over a considerably greater length of time

(Garber & Strassberg, 1991). Informants also differ in the types of information about children to which they have access. For example, teachers are more likely to have detailed knowledge of children’s academic abilities and behaviors in a classroom setting, whereas parents are more likely to be better informants about children’s daily living habits, such as sleeping or eating (Garber & Strassberg, 1991).

In contrast to reports of relational and reactive aggression, child, parent, and teacher reports of proactive aggression (as well as the other constructs of interest) demonstrated reasonable degrees of convergence. That is, all three reporters’ scores demonstrated a tendency to be in agreement. One potential explanation for the convergence on measures of more proactive forms of aggressive behavior is that these types of behaviors may be more readily apparent to parents and teachers. Adults and children may be more likely to agree upon measures of traits that are demonstrated behaviorally (Phillips, Lonigan, Driscoll, & Hooe, 2002), and a review of the items comprising each of the subscales considered in this study provides support for such a hypothesis. Specifically, the items assessing proactive aggression are largely comprised of behavioral descriptions (e.g., “threatens others”, “pick on kids smaller than him/her”,

“gets others to gang up on other children”). In contrast, the items comprising both the reactive aggression and relational aggression subscales are much less readily visible and/or require much more extensive knowledge of a child’s behavior and the particular context to accurately assess (e.g., “when angry at someone, tries to get other people to

stop hanging out with him/her or stop liking him/her”, “tries to get other kids to dislike

certain kids by making up stories about them”, “gets mad for no good reason”). From

this perspective, fundamental differences between various forms of aggressive behavior

and how they are assessed may render some subtypes relatively easier to evaluate than

other subtypes.

Discriminant Validity

The results of this study suggest that there is a great deal of overlap between the constructs of relational aggression, reactive aggression, and proactive aggression for girls. For boys, there is only a lack of support for the distinction between relational aggression and proactive aggression. One possible interpretation of these results is that the aggression subtypes considered in this study do not represent distinct constructs for girls. However, it should be noted that the identification of unique correlates of each aggression subtype challenges such a perspective. Another possibility is that children, parents, and teachers cannot reliably differentiate between behaviors associated with each of these aggression subtypes. Finally, it is possible that when aggregated across reporters, the common “construct” does not adequately capture the theoretical distinction between subtypes. That is, what is common across all reporters may be some broad type of aggression, with the accompanying possibility that more fine-tuned distinctions between the actual forms of aggressive behavior become “averaged out” when considered across reporters.

At the manifest variable level, the potential measurement issues that may be problematic for convergent validity may also complicate attempts to reliably distinguish

83 between these aggression subtypes. For example, correlations between reactive and proactive aggression are higher when the same reporters are used to assess aggressive behavior (Camodeca, Goosens, Terwogt, & Schuengel, 2002). This suggests that characteristics of the measurement process (e.g., having the same informant respond to all items) may artificially inflate the correlations between measures of different constructs.

Dodge and Coie (1987) have suggested that the typically high correlations between reactive and proactive aggression frequently observed across studies may reflect measurement bias. For example, they note that when rating children’s behaviors, teachers often do not have access to all of the necessary contextual information to make informed ratings of the nature of an observed behavior. That is, teachers are often not privy to information about events leading up to a particular aggressive act, and they may therefore have a tendency to combine all aggressive acts into one larger, encompassing category (Dodge & Coie. 1987). From this perspective, the high correlations between measures of reactive and proactive aggression may be representative of a paucity of information necessary to reliably classify behaviors rather than of a lack of distinction between the constructs.

Likewise, the same potential problems associated with ratings of reactive and proactive aggression are likely to be especially problematic for relational aggression.

While classifying aggressive acts as either reactive or proactive in nature requires a certain amount of background information about events leading up to such an act, relational aggression is difficult to assess for this reason as well as many others. First, relationally aggressive acts are typically much more difficult to observe than physical

84 forms of aggression. For example, such behaviors as spreading rumors or excluding a peer from a friendship group are often more covert in nature and therefore less detectable to outside observers. In addition, whereas proactive and especially reactive aggressive behaviors tend to take place in the presence of the victim, relationally aggressive behaviors often involve complex interactions with other peers that occur behind the victim’s back (Crick et al., 1999). For example, whereas physical aggression must occur in the context of direct confrontation with the victim, hurtful behavior in the form of relationally aggressive acts can occur outside of the direct presence of that individual, such as over the telephone or via electronic mail (Underwood, Galen, & Paquette, 2001).

Further, relationally aggressive behaviors can occur over extended periods of time (e.g., days or even weeks), while both reactively and proactively aggressive behaviors tend to occur in much briefer, discrete time periods. For example, although bullying or threats of physical fighting can continue for extended periods of time, the actual act of hitting or punching someone happens quickly and is immediately apparent to the victim

(Underwood, Galen, & Paquette, 2001). In contrast, relational aggression often assumes more subtle forms and the victim may not become aware of the harmful effects until a later point in time. In sum, numerous characteristics of relational aggression are likely to make identification of such behaviors relatively more difficult for observers than certain other forms of aggression.

On a related note, although multiple reports of relational aggression were considered in this study, one commonly used method, peer reports, was not included. It has been suggested that peers may have access to the more subtle forms of information about relationally aggressive acts that are not easily accessible by either parents or

85 teachers (Crick et al., 1999). However, it should also be noted that comparisons between peer and teacher reports of relational aggression in children aged nine to twelve have indicated that the predictive validity of both reports are comparable and that teachers may be able to provide comparable information about children’s relationally aggressive behaviors (Crick, 1996).

Method Effects

Method effects were observed for both parents’ and children’s self-reports of behavior. At a fundamental level, these results highlight the potential for correlations between measures completed by the same raters to be inflated due to rater bias. Evidence of method effects in the current study is not unexpected. For example, halo effects may account for the tendency for reporters to respond to items in similar ways. To the extent that a parent views a child as being aggressive, he or she may be more likely to endorse all aggression-related items, regardless of the specific content of a particular item.

Further, such method effects are also consistent with the observed effect of social desirability in contributing to child and parent reports of CU traits and parent reports of delinquent behaviors. For example, a response style indicative of a desire to present one’s child in a favorable light is likely to contribute to similar patterns of responses across a variety of aggression items.

Limitations

Certain limitations restrict the scope of some of the interpretations available from this study. First, the sample represents only a small portion (i.e., 7.4%) of the children

86 targeted for participation. In addition, despite efforts to recruit children from ethnically diverse backgrounds, the sample was predominantly Caucasian. Although an examination of correlations between variables included in this sample indicated that measures of various constructs were related to each other in predicted ways, replication with larger, more diverse samples is necessary to determine the generalizability of the present results.

Certain statistical considerations should also be noted. First, because regression analyses were tested separately for children, parents, and teachers, the total number of models tested was relatively large. As a result, there was an overall increase in the probability of a Type I error (i.e., due to familywise error rate). However, one way to address the increased potential for “false positives” is to adopt a more stringent criterion for statistical significance. From this perspective, it should be noted that many of the regression results were associated with p-values less than .01. In addition, the results in this study were largely consistent with previously reported findings and theory-based expectations.

With respect to the null effects for many of the regression analyses, it should be noted that in some cases, restriction of range for some of the variables of interest may have decreased the likelihood of finding significant correlations between variables (i.e., between predictor and predicted variables). As a result, attempts to draw conclusions about differences between child, parent, and teacher regression models may be limited.

For example, the significant results for predictions of child and parent social problems but not for teacher reports of social problems should not necessarily be interpreted as a lack of association between relational aggression and social problems from teachers’

87 perspectives. It may be that characteristics of both the data and the nature of the statistical analyses performed prevented the identification of significant effects for teacher reports.

The inability to converge on plausible MTMM solutions for two of the girls’ models also highlights the power limitations of these analyses. Specifically, analyzing the data from boys and girls separately in the MTMM analyses substantially reduced the sample size, thereby decreasing power. In particular, the null findings with respect to the failure to find support for the discriminant validity of various constructs may be reflective of the low power of these analyses to detect significant findings and should be interpreted cautiously. Additional studies with more participants are necessary to determine if the results in this sample reflect an error in the underlying theory (i.e., inappropriate model specification) or limitations in the particular data considered in this study. As a result, the MTMM analyses, in particular, should be viewed as preliminary in nature. Finally, the results of this study are correlational and therefore cannot be interpreted in terms of causality. For example, the observed association between relational aggression and such characteristics as social or internalizing problems does not indicate whether relational aggression causes these behaviors. In addition, the extent to which the associations between the various behaviors assessed in this study change over time cannot be determined. Longitudinal data are necessary to explore these possibilities.

Some of the measures used in this study were adapted for use with different reporters than those for whom they were originally developed. For example, in order to allow for comparisons between child, parent, and teacher reports of relational aggression,

Brown et al’s (1996) teacher rating scale was adapted for use with children and parents.

Although this approach was useful in that it allowed for direct comparisons between

parallel measures, the extent to which the items were assessing the same constructs across

reporters was not known. For example, the factor structure identified for the teacher

version of the measure may not have been appropriate for the self-report version.

However, although the limited sample size did not warrant factor analyses of the adapted

versions of these measures, observed Cronbach’s alphas for the scale scores on the

adapted measures were all acceptable. That is, the internal consistency of these scales

provides preliminary evidence that these items may be assessing unitary concepts.

Finally, it should be noted that interpretation of reports of a child’s behavior may

be confounded with gender stereotypes. That is, children’s, parents’ and teachers’

knowledge of gender stereotypes may influence how they respond to measures about

their aggressive behavior (Underwood, Galen, & Paquette, 2001). For example,

informants may be more willing to acknowledge physical forms of aggression for boys,

but be more hesitant to either recognize or endorse the same types of behaviors in girls.

However, it should also be noted that it is possible that such gender stereotypes may also

actually affect children’s behavior, thereby making them more likely to behave in gender-

expected ways.

Implications

The results of this investigation offer several implications for future research. At a fundamental level, the current pattern of results underscores the importance of including both boys and girls in investigations of aggression. Merely examining aggressive behaviors with either boys or girls (for example, many earlier studies

89 examined aggressive behaviors solely in boys) precludes the opportunity to examine the interacting effects of aggressive behaviors for boys and girls. Only by including both boys and girls in the same study can potential gender differences in aggressive behavior and its implications be identified.

Another intriguing implication of the present study is that it may be important to critically examine the utility of using multiple informants to assess children’s aggressive behavior. It is generally accepted that multiple reports of behavior provide a more representative view of a child’s behavior. From this perspective, the evidence of poor agreement between children’s, parents’, and teachers’ reports of specific subtypes of aggressive behavior might be viewed as support for the importance of using multimethod approaches when assessing children’s behaviors. That is, these results indicate that different informants provide unique perspectives on a child’s behavior.

However, a comparison of the results across the regression analyses and the

MTMM analyses also highlight the importance of considering the potential for specific information essential to the fundamental nature of particular constructs to be “washed out” when averaged across multiple reporters. That is, simply aggregating multiple informants’ reports does not ensure that the resulting combined information will be relevant or pertinent to addressing a particular research question. More precisely, identifying a portion of common shared variance across reporters does not guarantee that the common variance actually represents the construct of interest. Rather than automatically assuming that combining scores across several reporters will provide a more “accurate” description of a child’s behavior than merely considering one perspective in isolation, it might be better to consider the appropriateness of a given

90 reporter’s perspective on a case-by-case basis. For example, children might be expected to have greater insight into their own internalizing problems than an observer such as a parent. On the other hand, children may also have more of a tendency than a more objective informant (e.g., a teacher) to underreport their levels of such behaviors as relational aggression for social desirability reasons. To the extent that different informants are likely to offer different perspectives, the purpose or goals of a particular study may dictate a preference for the emphasis of one informant’s perspective over another informant’s perspective. Therefore, a viable approach for addressing the discrepancies between different informants of children’s behaviors might be to select information based on opinions about which source(s) of information is/are most likely to offer the most reliable information about the behaviors of interest (Garber & Strassberg,

1991). For example, the selection of a particular informant’s perspective for addressing various research questions could then be guided by an examination of the strength of associations between relevant predictors and variables of interest.

Given the suggestion in the present results that the constructs of relational aggression, reactive aggression, and proactive aggression may be indistinguishable,

(especially for girls) one important possibility to consider is that the present pattern of results reflects limitations in the ways in which the constructs of interest were assessed in this study. For example, rather than assuming that the failure to detect a common pattern among multiple respondents’ reports of relationally aggressive behavior reflects a fundamental lack of construct validity, the possibility that limitations in the mode of measurement are accounting for low discriminant validity must first be considered. At a global level, it is possible that the measures used in this study are not adequately

91 capturing essential features of the aggression subtype constructs they are purported to assess. Perhaps changes in the items comprising each of the aggression subscales considered in this study may result in greater reliability and validity of aggression subtype scores. Similarly, it is possible that the particular method of evaluation (i.e., questionnaires) is insufficient for measuring the constructs of interest. For example, behavioral observations may have yielded stronger support for the construct validity of relational aggression.

Although a lack of discriminant validity is typically interpreted as being problematic for construct validity, it should be emphasized that the specific finding that relational aggression overlaps with reactive (at least for girls) and proactive aggression is not necessarily indicative of a lack of construct validity for relational aggression. That is, both reactive and proactive subtypes of aggression have been hypothesized to exist, and from this perspective, measures of relational aggression would therefore be logically expected to share variance with measures of both reactive and proactive aggression.

However, based on this expectation of relational aggression subtypes, refinement to current assessment techniques is also important. For example, tests of the potential existence and specific nature of hypothesized subtypes as well as the development of instruments to assess such subtypes would represent an important advance in our understanding of relational aggression (Underwood, Galen, & Paquette, 2001). In addition, as highlighted by the present results, the possibility that subtypes of relational aggression may differ for boys and girls should also be considered.

At a more fundamental level, determining the hierarchical organization of relational, reactive, and proactive aggression might also help to clarify the nature of

92 associations between these aggression subtypes. For example, one interpretation of the overlap between measures of these constructs is that this shared variance may reflect the existence of reactive and proactive subtypes of relational aggression. Alternatively, reactive and proactive aggression may represent higher order constructs, with relational aggression being conceptualized as one specific form of either reactive or proactive aggression, or both. Finally, it should be noted that the same frameworks and/or research methods utilized for investigations of physical forms of aggression may not be appropriate for attempts to explore the nature of relational aggression (Underwood,

Galen, & Paquette, 2001). It may be the case that unique frameworks are necessary to conceptualize relational aggression.

The lack of support for the distinction between the subtypes of aggression in this study also lends support to the position that attempts to classify aggressive behaviors into subtypes may be too complex or even misguided. Bushman and Anderson (2001) have argued that attempts to distinguish between hostile or reactive forms of aggression and instrumental or proactive forms of aggression are obsolete. From this perspective, ambiguity in classifying aggressive behaviors along such dimensions as: underlying goals or motivations, the presence of anger, or the extent to which behaviors are preceded by extensive planning (i.e., premeditation), makes it extremely difficult to adequately categorize aggressive behaviors into subtypes. For example, a single aggressive act can often serve multiple goals (for example, attempting to “steal” another person’s boyfriend or girlfriend can result in both hurting that individual’s feelings as well as advancing one’s own social status) (Underwood, Galen, & Paquette, 2001). On a similar note, attempts to assess aggressive behaviors related to relationship contexts might also be

93 more adequately considered within a broader framework. For example, in order to understand an isolated act of aggression aimed at harming another’s relationships with others, it may be important to understand the broader context for the act. Because relationally aggressive behaviors (at least among older children) are more likely to reflect retaliation for a past perceived wrongdoing, it may be especially important to have access to underlying motives for relationally aggressive acts in order to gain insight into such behaviors (Crick, 1996).

The current study provides a synthesis and extension of previous investigations of relational aggression. With respect to the construct validity of relational aggression, the present results suggest that in middle-school-aged children, relational aggression: is associated with harmful correlates, may be both reactive and proactive in nature, and may differ in its implications for boys and girls. In sum, the results of this study support theoretically-based predictions and existing ideas about relational aggression, and provide a foundation for further investigations of relational aggression.

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104

APPENDIX A

TABLES

105

Demographic Variable Percent

Sex: Male 58.9% Female 41.1%

Race: African-American 1.3% Asian 1.8% Caucasian 89.3% Native American .4% Other 5.8%

Grade: 6th 39.6% 7th 34.7% 8th 24.8% 9th .9%

Age: 11 18.0% 12 38.3% 13 27.7% 14 14.6% 15 1.4%

Families’ Occupational Status: Unskilled Workers .3% Semi-skilled Workers 9.8% Skilled Workers 20.0% Minor Professionals 45.5% Professionals 21.0%

Marital Status: Married 68.8% Separated 4.0% Divorced 20.1% Widowed 2.2% Single or Never Married 2.2%

Table 1. Demographic variable information

106

Child Self-Report Variables Sex N Minimum Maximum Mean SD

Relational Aggression M 127 7.00 21.00 12.46 3.71 F 88 7.00 27.00 11.95 3.79 Same-Sex Liked M 131 1.00 5.00 4.15 .85 F 91 2.00 5.00 4.44 .69 Opposite-Sex Liked M 131 1.00 5.00 3.89 .94 F 90 1.00 5.00 3.92 .95 Reactive Aggression M 129 0.00 12.00 2.57 2.06 F 91 0.00 9.00 2.43 1.92 Proactive Aggression M 128 0.00 13.00 2.98 2.89 F 92 0.00 10.00 2.04 2.09 Prosocial Behavior M 129 11.00 20.00 16.34 2.37 F 89 0.00 8.00 2.65 1.42 Callous-Unemotional Traits M 130 0.00 7.00 3.32 1.74 F 89 0.00 8.00 2.74 1.38 APSD – Impulsivity M 129 0.00 8.00 3.89 1.70 F 92 0.00 7.00 3.41 1.64 APSD – Narcissism M 129 0.00 12.00 3.35 2.17 F 92 0.00 11.00 2.85 2.10 Children’s Depression Inventory M 129 0.00 34.00 6.27 6.27 F 90 0.00 22.00 5.54 4.52 Revised Children’s Manifest M 119 4.00 63.00 27.68 10.87 Anxiety Scale F 86 8.00 59.00 29.80 9.76

Effortful Control-Distractibility M 123 20.00 60.00 44.30 8.23 F 85 17.00 60.00 45.44 7.86 Effortful Control–Impulsivity M 127 12.00 44.00 28.34 6.11 F 89 13.00 42.00 29.72 6.72 SPPC - Behavioral Competence M 129 1.00 4.00 3.07 .59 F 90 2.00 4.00 3.34 .52 SPPC - Social Competence M 128 1.17 4.00 3.01 .67 F 90 1.50 4.00 3.22 .58 YSR – Anxious / Depressed M 130 0.00 25.00 4.82 4.70 F 92 0.00 17.00 5.69 4.19 YSR- Social Problems M 130 0.00 11.00 3.59 2.72 F 92 0.00 8.00 2.83 2.20 YSR – Attention Problems M 130 0.00 16.00 5.34 3.31 F 92 0.00 15.00 5.26 3.09 YSR – Delinquent Behavior M 130 0.00 14.00 2.82 2.53 F 92 0.00 9.00 1.71 1.62

Table 2. Descriptive Statistics

107

Table 2 continued

Parent-Report Variables Sex N Minimum Maximum Mean SD

Relational Aggression M 129 7.00 27.00 12.38 3.81 F 88 7.00 26.00 13.52 4.34 Same-Sex Liked M 130 2.00 5.00 4.14 .82 F 91 2.00 5.00 4.26 .74 Opposite-Sex Liked M 130 2.00 5.00 4.00 .90 F 91 2.00 5.00 4.07 .87 Reactive Aggression M 130 0.00 11.00 4.28 2.50 F 91 0.00 12.00 3.87 2.34 Proactive Aggression M 130 0.00 9.00 2.12 2.14 F 92 0.00 19.00 1.91 2.64 Prosocial Behavior M 129 11.00 20.00 16.51 2.31 F 91 12.00 20.00 17.34 2.21 Callous-Unemotional Traits M 131 0.00 6.00 2.47 1.84 F 92 0.00 9.00 2.13 1.80 APSD – Impulsivity M 130 0.00 9.00 3.82 1.95 F 92 0.00 9.00 3.11 1.79 APSD – Narcissism M 131 0.00 10.00 2.45 2.04 F 92 0.00 11.00 2.76 2.45 Children’s Depression Inventory M 129 .00 23.00 5.40 4.88 F 89 .00 22.00 4.52 5.08 PRS - Behavioral Competence M 129 1.00 4.00 3.60 .62 F 92 1.67 4.00 3.71 .58 PRS - Social Competence M 129 1.00 4.00 3.15 .90 F 92 1.00 4.00 3.30 .79 CBC – Anxious / Depressed M 131 0.00 21.00 3.95 4.03 F 92 0.00 19.00 4.08 4.08 CBC- Social Problems M 131 0.00 12.00 2.16 2.48 F 92 0.00 10.00 1.85 2.21 CBC – Attention Problems M 131 0.00 17.00 4.05 3.80 F 92 0.00 15.00 2.58 3.16 CBC – Delinquent Behavior M 131 0.00 9.00 1.47 1.83 F 92 0.00 10.00 1.27 2.00

Continued

108

Table 2 continued

Teacher-Report Variables Sex N Minimum Maximum Mean SD

Relational Aggression M 113 7.00 27.00 11.19 4.20 F 85 7.00 32.00 11.47 4.67 Same-Sex Liked M 119 1.00 5.00 3.93 .97 F 86 2.00 5.00 4.15 .82 Opposite-Sex Liked M 119 1.00 5.00 3.66 1.00 F 86 1.00 5.00 3.73 .93 Reactive Aggression M 118 0.00 11.00 1.71 2.36 F 86 0.00 10.00 1.22 2.02 Proactive Aggression M 117 0.00 12.00 1.73 2.55 F 85 0.00 10.00 .87 2.00 Prosocial Behavior M 117 6.00 20.00 14.28 2.37 F 85 11.00 20.00 16.48 2.60 Callous-Unemotional Traits M 115 0.00 10.00 3.23 2.32 F 84 0.00 8.00 1.88 1.93 APSD – Impulsivity M 116 0.00 8.00 2.92 1.91 F 85 0.00 7.00 1.52 1.51 APSD – Narcissism M 118 0.00 10.00 2.03 2.48 F 83 0.00 10.00 1.53 2.37 TRS - Behavioral Competence M 119 1.00 4.00 3.50 .72 F 86 1.00 4.00 3.82 .54 TRS - Social Competence M 119 1.00 4.00 3.00 .92 F 86 1.00 4.00 3.25 .80 CTRS – Anxious / Shy M 117 0.00 12.00 3.40 2.72 F 85 0.00 16.00 3.90 2.98 CTRS- Social Problems M 117 0.00 15.00 2.47 3.44 F 84 0.00 8.00 29.72 6.72 CTRS – Restless / Impulsivity M 117 0.00 16.00 4.33 3.86 F 85 0.00 16.00 1.73 2.88 CTRS – Emotional Lability M 117 0.00 8.00 .89 1.57 F 85 0.00 9.00 .84 1.63 CTRS – Attention Problems M 117 0.00 27.00 7.32 6.62 F 82 0.00 25.00 3.17 4.86

109

RelAgg_C RelAgg_P RelAgg_T Prosoc_C Prosoc_P Prosoc_T

RelAgg_C 1 RelAgg_P .049 1 RelAgg_T .136 .210** 1 Prosoc_C -.379** -.059 -.088 1 Prosoc_P -.197** -.314** -.100 .356** 1 Prosoc_T -.247** -.121 -.255** .316** .353** 1 SameSex_C -.070 .047 -.119 .319** .228** .228** SameSex_P .013 -.250** -.074 .059 .436** .273** SameSex_T .057 -.165* -.223** .112 .168* .634** OppSex_C .013 .105 .076 .221** .108 .119 OppSex_P -.013 -.118 -.007 .091 .351** .266** OppSex_T -.057 -.145* -.247** .084 .140* .520** RA_C .436** -.021 .124 -.320** -.131 -.220** RA_P .199** .420** .149* -.120 -.244** -.265** RA_T .141* .195** .539** -.110 -.168* -.458** PA_C .524** .093 .227** -.384** -.192** -.267** PA_P .175* .409** .267** -.225** -.338** -.219** PA_T .257** .211** .605** -.204** -.330** -.475** AnxDep_C .300** .061 .152* -.178** -.063 -.104 RCMAS_C .313** .112 .210** -.193** -.061 -.045 CDI_C .216** .181** .173* -.174* -.162* -.144* AnxDep_P .035 .393** .203** -.008 -.161* -.055 AnxShy_T .059 .051 .227** -.025 .000 -.193**

Continued

* p < .05 ** p < .01

Note: RelAgg_C=Child-rated Relational Aggression; RelAgg_P=Parent-rated Relational Aggression; RelAgg_T=Teacher-rated Relational Aggression; Prosoc_C=Child-rated Prosocial Behavior; Prosoc_P=Parent-rated Prosocial Behavior; Prosoc_T=Teacher-rated Prosocial Behavior; SameSex_C=Child-rated Same-Sex Liked; SameSex_P=Parent-rated Same-Sex Liked; SameSex_T=Teacher-rated Same-Sex Liked; OppSex_C=Child-rated Opposite-Sex Liked; OppSex_P=Parent-rated Opposite-Sex Liked; OppSex_T=Teacher-rated Opposite-Sex Liked; RA_C = Child-rated Reactive Aggression; RA_P= Parent-rated Reactive Aggression; RA_T=Teacher-rated Reactive Aggression; PA_C=Child-rated Proactive Aggression; PA_P=Parent-rated Proactive Aggression; PA_T=Teacher-rated Proactive Aggression; AnxDep_C=Child-rated Anxious/Depressed; RCMAS_C = Revised Children’s Manifest Anxiety Scale; CDI_C= Children’s Depression Inventory; AnxDep_P=Parent- rated Anxious/Depressed; AnxShy_T= Teacher-rated Anxious / Shy

Table 3. Correlations Between Study Variables

110

Table 3 continued

RelAgg_C RelAgg_P RelAgg_T ProSoc_C ProSoc_P ProSoc_T

SocProb_C .266** .044 .061 -.165* -.174* -.161** SocProb_P -.027 .230** .090 .009 -.186** -.195** SocProb_T .027 .014 .174* -.160* -.185** -.518** Attn_C .342** .040 .158* -.181** -.149* -.168* Attn_P .108 .208** .236** -.081 -.187** -.254** Attn_T .220** .115 .178* -.163* -.093 -.388** Delinq_C .431** .050 .231** -.293** -.194** -.316** Delinq_P .175* .353** .255** -.154* -.334** -.247** Opp_T .183* .296** .579** -.101 -.278** -.401** CU_C .308** .063 .149* -.362** -.143* -.281** CU_P .126 .257** .204** -.180** -.486** -.266** CU_T .237** .129 .397** -.310** -.308** -.689** SocCom_C -.115 .070 .077 .211** .124 .216** SocCom_P .053 -.047 -.051 -.092 .082 .092 SocComT .017 -.007 -.098 -.069 .043 .202** BehComC -.416** -.128 -.210** .516** .336** .295** BehComP -.216** -.230** -.222** .266** .423** .299** BehComT -.277** -.126 -.474** .175* .234** .506** EC_C -.479** -.118 -.169* .386** .283** .294** Lie_C -.410** -.087 -.112 .464** .157* .204**

Continued

Note: SocProb_C=Child-rated Social Problems; SocProb_P=Parent-rated Social Problems; SocProb_T=Teacher-rated Social Problems; Attn_C=Child-rated Attention Problems; Attn_P=Parent-rated Attention Problems; Attn_T=Teacher-rated Inattention; Delinq_C=Child-rated Delinquent Behavior; Delinq_P=Parent-rated Delinquent Behavior; Opp_T=Teacher-rated Oppositional; CU_C=Child-rated Callous-Unemotional Traits; CU_P=Parent-rated Callous-Unemotional Traits; CU_T = Teacher-rated Callous-Unemotional Traits; SocCom_C=Child-rated Social Competence; SocCom_P=Parent-rated Social Competence; SocCom_T=Teacher-rated Social Competence; BehCom_C=Child-rated Behavioral Competence; BehCom_P = Parent-rated Behavioral Competence; BehCom_T=Teacher-rated Behavioral Competence; EC_C=Child-rated Effortful Control; Lie_C=Child-rated Lie Scale from RCMAS

111

Table 3 continued

SameSex_C SameSex_P SameSex_T OppSex_C OppSex_P OppSex_T

SameSex_C 1 SameSex_P .294** 1 SameSex_T .321** .408** 1 OppSex_C .485** .194** .259** 1 OppSex_P .267** .661** .347** .336** 1 OppSex_T .265** .333** .769** .210** .296** 1 RA_C -.132 -.076 -.150* -.034 -.103 -.163* RA_P -.001 -.256** -.241** .017 -.186** -.235** RA_T -.092 -.213** -.365** -.037 -.174* -.315** PA_C -.232** -.069 -.139* -.107 -.015 -.123 PA_P -.085 -.276** -.190** .145* -.114 -.222** PA_T -.124 -.130 -.337** .026 -.071 -.256** AnxDep_C -.227** -.139* -.108 -.112 -.119 -.105 RCMAS_C -.171* -.092 -.098 -.088 -.120 -.103 CDI_C -.226** -.229** -.119 -.167* -.145* -.098 AnxDep_P -.191** -.391** -.208** -.092 -.333** -.227** AnxShy_T -.063 -.179* -.373** -.093 -.099 -.411** SocProb_C -.297** -.256** -.212** -.263** -.231** -.220** SocProb_P -.203** -.518** -.369** -.148* -.433** -.323** SocProb_T -.298** -.415** -.745** -.270** -.330** -.598** Attn_C -.186** -.177** -.104 -.084 -.148* -.114 Attn_P -.225** -.336** -.267** -.049 -.294** -.248** Attn_T -.084 -.146* -.246** .011 -.090 -.237** Delinq_C -.266** -.101 -.231** -.083 -.029 -.167* Delinq_P -.136* -.319** -.195** -.003 -.109 -.194** Opp_T -.089 -.169* -.322** -.062 -.094 -.234** CU_C -.296** -.023 -.087 -.221** -.005 -.049 CU_P -.043 -.232** -.160* -.032 -.195** -.112 CU_T -.141 -.131 -.466** -.048 -.098 -.377** SocCom_C .560** .347** .279** .552** .429** .232** SocCom_P .080 .336** .176* .058 .273** .206** SocCom_T .125 .303** .422** .080 .233** .413** BehCom_C .285** .083 .091 .094 .062 .057 BehCom_P .207** .237** .236** .154* .145* .223** BehCom_T .125 .178* .326** .060 .118 .310** EC_C .241** .143* .186** .098 .095 .197** Lie_C .231** .080 .126 .114 .093 .083

Continued

112

Table 3 continued

RA_C RA_P RA_T PA_C PA_P PA_T AnxDep_C RCMAS_C

RA_C 1 RA_P .256** 1 RA_T .152* .251** 1 PA_C .580** .196** .223** 1 PA_P .233** .651** .218** .237** 1 PA_T .245** .182** .652** .427** .265** 1 AnxDep_C .514** .199** .150* .477** .146* .194** 1 RCMAS_C .440** .267** .112 .411** .191** .157* .718** 1 CDI_C .297** .215** .098 .486** .172* .238** .623** .615** AnxDep_P .514** .199** .198** .141* .146* .105 .281** .271** AnxShy_T .093 .200** .390** .023 .282** .175* .149* .135 SocProb_C .378** .194** .081 .407** .186** .178* .561** .445** SocProb_P .123 .368** .151* .023 .338** .062 .149* .148* SocProb_T .150* .155* .309** .185** .147* .292** .195** .181* Attn_C .464** .206** .081 .458** .192** .189** .613** .550** Attn_P .118 .444** .283** .179** .444** .246** .185** .195** Attn_T .149* .151* .278** .260** .225** .399** .108 .016 Delinq_C .438** .176** .183** .623** .259** .431** .478** .357** Delinq_P .214** .552** .169* .289** .631** .285** .209** .202** Opp_T .235** .258** .704** .303** .263** .700** .152* .172* CU_C .261** .188** .117 .415** .234** .213** .282** .201** CU_P .179** .393** .123 .127 .417** .246** .102 .106 CU_T .264** .193** .472** .255** .238** .597** .155* .128 SocCom_C -.271** -.113 -.072 -.262** -.081 -.079 -.407** -.345** SocCom_P .046 -.073 -.066 .074 -.003 .016 .053 .047 SocCom_T .008 -.016 -.048 -.012 -.025 -.018 -.049 -.012 BehCom_C -.356** -.225** -.150* -.553** -.268** -.337** -.416** -.441** BehCom_P -.273** -.439** -.227** -.300** -.518** -.323** -.221** -.187** BehCom_T -.279** -.188** -.645** -.475** -.233** -.714** -.256** -.199** EC_C -.535** -.221** -.123 -.524** -.303** -.251** -.498** -.538** Lie_C -.316** -.172* -.080 -.385** -.130 -.117 -.270** -.312**

Continued

113

Table 3 Continued

CDI_C AnxDep_P AnxShy_T SocProb_C SocProb_P SocProb_T

CDI_C 1 AnxDep_P .357** 1 AnxShy_T .167* .295** 1 SocProb_C .429** .212** .040 1 SocProb_P .177** .516** .244** .307** 1 SocProb_T .174* .225** .469** .211** .352** 1 Attn_C .523** .205** .045 .654** .280** .114 Attn_P .315** .543** .249** .319** .648** .240** Attn_T .177* .148* .215** .273** .249** .290** Delinq_C .470** .128 .142* .430** .102 .211** Delinq_P .312** .543** .190** .241** .511** .142* Opp_T .149* .127 .274** .084 .141* .241** CU_C .309** .072 .013 .353** .077 .147* CU_P .168* .163* .162* .138* .271** .130 CU_T .136 .036 .202** .240** .133 .430** SocCom_C -.422** -.220** -.122 -.463** -.314** -.303** SocCom_P -.030 -.151* -.166* -.116 -.349** -.259** SocCom_T -.065 -.182* -.299** -.199** -.342** -.537** BehCom_C -.475** -.159* -.027 -.388** -.145* -.084 BehCom_P -.272** -.447** -.190** -.273** -.366** -.184** BehCom_T -.280** -.108 -.299** -.261** -.120 -.247** EC_C -.511** -.138* -.114 -.467** -.142* -.199** Lie_C -.311** -.184** -.023 -.286** -.137* -.134

Continued

114

Table 3 continued

Attn_C Attn_P Attn_T Delin_C Delin_P Opp_T CU_C CU_P CU_T

Attn_C 1 Attn_P .452** 1 Attn_T .334** .468** 1 Delinq_C .431** .213** .337** 1 Delinq_P .282** .551** .184** .338** 1 Opp_T .150* .268** .238** .227** .306** 1 CU_C .402** .240** .362** .422** .230** .138 1 CU_P .211** .333** .156* .184** .506** .289** .201** 1 CU_T .215** .266** .497** .348** .211** .460** .235** .239** 1 SocCom_C -.308** -.284** -.097 -.225** -.158* -.041 -.270** -.123 -.098 SocCom_P -.054 -.189** -.153* .106 -.103 -.037 .057 -.173* -.057 SocCom_T -.097 -.208** -.235** -.076 -.103 -.002 -.001 -.071 -.147* BehCom_C -.449** -.326** -.303** -.552** -.324** -.239** -.423** -.185** -.350** BehCom_P -.321** -.546** -.264** -.386** -.641** -.318** -.248** -.525** -.325** BehCom_T -.290** -.367** -.472** -.482** -.315** -.703** -.302** -.196** -.570** EC_C -.648** -.352** -.377** -.488** -.288** -.210** -.357** -.224** -.322** Lie_C -.376** -.232** -.221** -.302** -.302** -.072 -.283** -.140* -.188**

Soc_C Soc_P Soc_T Beh_C Beh_P Beh_T EC_C Lie_C

SocCom_C 1 SocCom_P .141* 1 SocCom_T .190** .447** 1 BehCom_C .245** -.198** -.047 1 BehCom_P .184** -.011 .077 .440** 1 BehCom_T .164* .012 .011 .359** .363** 1 EC_C .298** .001 .085 .599** .427** .373** 1 Lie_C .208** -.081 .058 .496** .264** .172* .416** 1

115

CHILD SOCIAL PROBLEMS COMPOSITE

Child Social Child Social Problems YSR Social Problems Competence - Reversed Composite

Child Social Problems 1 Composite

.853** 1 YSR Social Problems

Child Social Competence– .858** .463** 1 Reversed

PARENT SOCIAL PROBLEMS COMPOSITE

Parent Social Parent Social Problems CBCL Social Problems Competence - Reversed Composite

Parent Social Problems 1 Composite

.897** 1 CBCL Social Problems

Parent Social Competence– .893** .602** 1 Reversed

Continued

Table 4. Component - Composite Correlations

116

Table 4 continued

TEACHER SOCIAL PROBLEMS COMPOSITE

Teacher Social Teacher Social Problems CTRS Social Problems Competence - Reversed Composite

Teacher Social Problems 1 Composite

.953** 1 CTRS Social Problems

Teacher Social .953** .817** 1 Competence–Reversed

CHILD INTERNALIZING COMPOSITE

Child Internalizing YSR Anxious / Composite Depressed RCMAS CDI

Child Internalizing 1 Composite

.888** 1 YSR Anxious / Depressed

.888** .718** 1

RCMAS

.855** .623** .615** 1

CDI

Continued

117

Table 4 continued

CHILD INATTENTION COMPOSITE

Child Inattention YSR Attention Child EC (P/D) - Composite Problems Reversed

Child Inattention 1 Composite

.906** 1 YSR Attention Problems

Child EC (Persistence / .909** .648** 1 Distractibility)–Reversed

TEACHER INATTENTION COMPOSITE

Teacher Inattention CTRS Restless / CTRS Attention Composite Impulsivity Problems

Teacher Inattention 1 Composite

.938** 1 CTRS Restless / Impulsivity

CTRS Attention Problems .938** .759** 1

118

Step and Predictor B SE ß t ∆R2 ∆F df F Sig ∆F

Results for Child Reports Step 1 .009 .89 (2, 206) .89 .413 Constant .183 .210 .875 Gender -.130 .140 -.064 -.928 SES -.069 .069 -.070 -1.00 Step 2 .289 42.02 (2, 204) 21.63 .000** Constant -.082 .181 -.454 Gender .054 .121 .027 .450 SES .037 .059 .038 .629 RA .196 .071 .198 2.749 PA .411 .072 .418 5.720 Step 3 .019 2.75 (2, 202) 15.58 .000** Constant -.097 .179 -.541 Gender .078 .121 .039 .645 SES .038 .059 .039 .648 RA -.049 .214 -.050 -.237 PA .219 .218 .222 1.001 RA x Gender .169 .146 .250 1.156 PA x Gender .163 .162 .223 1.008

Results for Parent Reports Step 1 .023 2.45 (2, 211) 2.45 .089 Constant -.381 .206 -1.848 Gender .281 .138 .138 2.028 SES -.055 .068 -.055 -.809 Step 2 .221 30.47 (2, 209) 16.80 .000** Constant -.511 .183 -2.790 Gender .379 .123 .186 3.075 SES -.001 .061 .001 .011 RA .285 .080 .286 3.568 PA .235 .080 .237 2.933 Step 3 .006 0.76 (2, 207) 11.43 .469 Constant -.516 .184 -2.813 Gender .387 .124 .191 3.129 SES .002 .061 .002 .035 RA .200 .240 .200 .832 PA .079 .250 .079 .316 RA x Gender .070 .171 .102 .410 PA x Gender .096 .161 .154 .596

Continued

* p < .05, ** p < .01

Table 5. Hierarchical Regression Analysis Results for RA and PA in predicting Relational Aggression

119

Table 5 continued

Step and Predictor B SE ß t ∆R2 ∆F df F Sig ∆F

Results for Teacher Reports Step 1 .057 5.78 (2, 193) 5.78 .004** Constant -.075 .214 -.351 Gender .057 .141 .028 .403 SES -.240 .072 -.235 -3.349 Step 2 .384 65.60 (2, 191) 37.63 .000** Constant -.421 .169 -2.497 Gender .307 .112 .152 2.740 SES -.126 .056 -.123 -2.225 RA .257 .075 .247 3.412 PA .454 .075 .450 6.063 Step 3 .025 4.37 (2, 189) 27.42 .014* Constant -.445 .166 -2.680 Gender .344 .111 .171 3.102 SES -.135 .056 -.132 -2.426 RA -.007 .224 -.007 -.032 PA .205 .224 .204 .916 RA x Gender .184 .162 .255 1.139 PA x Gender .199 .169 .272 1.178

120

Step and Predictor B SE ß t ∆R2 ∆F df F Sig ∆F Parent Anxious Depressed Step 1 .006 .64 (2, 213) 0.64 .531 Constant -.004 .184 -.024 Gender -.042 .124 -.002 -.026 SES .001 .061 .003 .045 Step 2 .209 56.46 (1, 212) 19.36 .000** PA .324 .065 .324 5.426 Step 3 .053 15.14 (1, 211) 19.27 .000** Rel Agg .359 .188 .358 3.892 Step 4 .001 .20 (1, 210) 15.40 .653 Rel Agg x Gender -.042 .122 -.066 -.452

Child Inattention Composite Step 1 .024 2.34 (2, 194) 2.34 .099 Constant -.121 .164 -.738 Gender .084 .110 .046 .763 SES -.067 .053 -.075 -1.26 Step 2 .305 87.67 (1, 193) 31.48 .000** PA .381 .061 .435 6.29 Step 3 .028 8.27 (1, 192) 26.57 .004** Rel Agg .282 .167 .314 1.69 Step 4 .001 .26 (1, 191) 21.23 .610 Rel Agg x Gender -.057 .107 -.098 -.535

Child Soc Probs Composite Step 1 .055 5.84 (2, 202) 5.84 .000** Constant .310 .167 1.86 Gender -.232 .111 -.137 -2.09 SES -.054 .054 -.065 -1.00 Step 2 .110 26.40 (1, 201) 13.19 .000** PA .268 .063 .325 4.28 Step 3 .001 .325 (1, 200) 9.94 .572 Rel Agg .363 .168 .433 2.16 Step 4 .017 4.26 (1, 199) 8.93 .041* Rel Agg x Gender -.226 .109 -.407 -2.09

Continued

* p < .05, ** p < .01

Note: Coefficients shown reflect values in the final significant step of each model. SES = socioeconomic status, RA = Reactive Aggression, PA = Proactive Aggression, Rel Agg = Relational Aggression

Table 6. Hierarchical Regression Analysis Results for RA Correlates 121

Table 6 continued

Step and Predictor B SE B t ∆R2 ∆F df F Sig ∆F

Child Intern. Composite Step 1 .020 1.90 (2, 185) 1.90 .153 Constant -.361 .166 -2.17 Gender .251 .111 .146 2.27 SES -.049 .054 -.058 -.91 Step 2 .256 64.93 (1, 184) 23.34 .000** PA .423 .061 .496 6.87 Step 3 .001 .250 (1, 183) 17.50 .621 Rel Agg .342 .176 .379 1.94 Step 4 .011 2.70 (1, 182) 14.67 .095 Rel Agg x Gender -.202 .115 -.335 -1.75

Note: Intern. = Internalizing

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Step and Predictor B SE ß t ∆R2 ∆F df F Sig ∆F Child Delinquency Step 1 .114 13.31 (2, 207) 13.31 .000** Constant .637 .174 3.653 Gender -.456 .117 -.225 -3.916 SES -.171 .058 -.172 -2.961 Step 2 .153 42.95 (1, 206) 24.99 .000** RA .282 .064 .283 4.385 Step 3 .060 18.13 (1, 205) 24.83 .000** Rel Agg .486 .178 .483 2.736 Step 4 .005 1.65 (1, 204) 20.26 .200 Rel Agg x Gender -.150 .117 -.226 -1.286

Parent Delinquency Step 1 .070 7.93 (2, 212) 7.93 .000** Constant .396 .171 .791 Gender .636 .116 -.050 -.875 SES -.453 .056 -.225 -4.066 Step 2 .278 89.85 (1, 211) 37.45 .000** RA -.168 .062 .467 7.549 Step 3 .017 5.57 (1, 210) 30.09 .019* Rel Agg .279 .176 -.008 -.044 Step 4 .003 .89 (1, 209) 24.24 .348 Rel Agg x Gender .106 .112 .163 .941

Teacher Oppositional Step 1 .037 3.71 (2, 193) 3.71 .026* Constant -.108 .142 -.758 Gender .082 .094 .041 .870 SES -.079 .049 -.077 -1.62 Step 2 .509 215.44 (1, 192) 77.04 .000** RA .623 .058 .596 10.75 Step 3 .039 17.99 (1, 191) 67.39 .000** Rel Agg -.076 .149 -.076 -.512 Step 4 .011 5.31 (1, 190) 56.19 .022* Rel Agg x Gender .213 .092 .333 2.305

Continued

* p < .05, ** p < .01

Note: Coefficients shown reflect values in the final significant step of each model. SES = socioeconomic status; PA = Proactive Aggression; RA = Reactive Aggression

Table 7. Hierarchical Regression Analysis Results for PA Correlates

123

Table 7 continued

Step and Predictor B SE ß t ∆R2 ∆F df F Sig ∆F Child CU Traits Step 1 .049 5.20 (2, 204) 5.20 .006** Constant .513 .202 2.539 Gender -.360 .136 -.178 -2.651 SES -.099 .067 -.101 -1.482 Step 2 .018 3.84 (1, 203) 4.80 .051* RA .060 .074 .061 .808 Step 3 .026 5.75 (1, 202) 5.11 .017* Rel Agg .320 .207 .321 1.549 Step 4 .002 0.54 (1, 201) 4.20 .461 Rel Agg x Gender -.100 .136 -.152 -.738

Parent CU Traits Step 1 .031 3.42 (2, 212) 3.42 .035* Constant .304 .196 1.551 Gender -.227 .133 -.112 -1.710 SES -.119 .064 -.120 -1.876 Step 2 .098 23.75 (1, 211) 10.44 .000** RA .233 .071 .234 3.276 Step 3 .027 6.76 (1, 210) 9.73 .010** Rel Agg .137 .201 .137 .680 Step 4 .000 0.07 (1, 209) 7.77 .799 Rel Agg x Gender .033 .129 .051 .256

Teacher CU Traits Step 1 .131 14.29 (2, 189) 14.29 .000** Constant .847 .196 4.325 Gender -.587 .130 -.289 -4.528 SES -.128 .067 -.123 -1.897 Step 2 .121 30.56 (1, 188) 21.20 .000** RA .291 .079 .279 3.693 Step 3 .013 3.38 (1, 187) 16.95 .067 Rel Agg .119 .203 .119 .583 Step 4 .000 .014 (1, 186) 13.49 .907 Rel Agg x Gender .015 .126 .023 .117

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Step and Predictor B SE ß t ∆R2 ∆F df F Sig ∆F

Child Inattention Composite Step 1 .328 31.03 (3, 191) 31.03 .000** Constant -.081 .156 -.519 Gender .055 .104 .030 .533 SES -.034 .051 -.038 -.672 PA .283 .066 .323 4.322 Step 2 .027 8.01 (1, 190) 26.13 .005** Rel Agg .117 .060 .130 1.962 Step 3 .056 18.14 (1, 189) 26.42 .000** RA .267 .063 .304 4.259

Parent Anxious Depressed Step 1 .215 19.17 (3, 210) 19.17 .000** Constant -.065 .181 -.358 Gender .040 .122 .019 .323 SES -.013 .059 -.013 -.227 PA .219 .080 .219 2.751 Step 2 .053 15.01 (1, 209) 19.09 .000** Rel Agg .209 .067 .208 3.111 Step 3 .031 9.10 (1, 208) 17.68 .003** RA .241 .080 .240 3.017

Continued

* p < .05, ** p < .01

Note: Coefficients shown reflect values in the final step of each model. SES = socioeconomic status; PA = Proactive Aggression; RA = Reactive Aggression

Table 8. Tests of Mediation (RA)

125

Table 8 continued

Step and Predictor B SE ß t ∆R2 ∆F df F Sig ∆F

Child Social Problems - BOYS Step 1 .218 16.01 (2, 115) 16.01 .000** Constant .083 .072 1.146 SES -.028 .073 -.032 -.390 PA .202 .086 .254 2.352 Step 2 .010 1.52 (1, 114) 11.23 .221 Rel Agg .077 .084 .085 .915 Step 3 .051 8.00 (1, 113) 10.94 .006** RA .241 .085 .280 2.828

Child Social Problems - GIRLS Step 1 .014 .59 (2, 82) .59 .559 Constant -.212 .081 -2.615 SES -.019 .080 -.026 -.236 PA -.040 .130 -.045 -.308 Step 2 .000 .01 (1, 81) .39 .925 Rel Agg -.065 .099 -.091 -.662 Step 3 .059 5.09 (1, 80) 1.58 .027* RA .235 .104 .315 2.257

Child Internalizing Problems - BOYS Step 1 .346 27.46 (2, 104) 27.46 .000** Constant -.094 .073 -1.295 SES -.030 .071 -.034 -.428 PA .331 .084 .395 3.919 Step 2 .009 1.40 (1, 103) 18.85 .239 Rel Agg .060 .087 .061 .689 Step 3 .048 8.28 (1, 102) 17.20 .005** RA .264 .092 .274 2.877

Child Internalizing Problems - GIRLS Step 1 .317 6.01 (2, 76) 6.01 .004** Constant .114 .082 1.400 SES -.082 .086 -.108 -.955 PA .366 .135 .370 2.707 Step 2 .006 .54 (1, 75) 4.16 .466 Rel Agg -.086 .105 -.108 -.815 Step 3 .003 .22 (1, 74) 3.14 .643 RA .050 .107 .062 .465

126

Step and Predictor B SE ß t ∆R2 ∆F df F Sig ∆F

Child Delinquency Step 1 .267 24.787 (3, 204) 24.79 .000** Constant .443 .163 2.707 Gender -.317 .109 -.156 -2.910 SES -.142 .054 -.142 -2.651 RA .087 .066 .087 1.327 Step 2 .059 17.729 (1, 203) 24.55 .000** Rel Agg .117 .063 .116 1.853 Step 3 .119 43.218 (1, 202) 32.58 .000** PA .458 .070 .462 6.574

Child CU Traits Step 1 .069 5.00 (3, 201) 5.00 .002** Constant .455 .205 2.219 Gender -.310 .138 -.154 -2.256 SES -.098 .067 -.100 -1.470 RA -.037 .082 -.038 -.451 Step 3 .025 5.42 (1, 200) 5.18 .021* Rel Agg .102 .080 .102 1.277 Step 4 .024 5.37 (1, 199) 5.31 .022* PA .204 .088 .209 2.317

Parent Delinquency Step 1 .437 37.26 (3, 210) 37.26 .000** Constant .105 .153 .605 Gender -.073 .103 -.036 -.710 SES -.156 .050 -.156 -3.119 RA .177 .067 .176 2.629 Step 3 .017 5.57 (1, 209) 29.94 .019* Rel Agg .063 .057 .062 1.104 Step 4 .137 56.91 (1, 208) 41.74 .000** PA .506 .067 .506 7.544

Continued * p < .05, ** p < .01

Note: Coefficients shown reflect values in the final step of each model. SES = socioeconomic status; PA = Proactive Aggression; RA = Reactive Aggression; Rel Agg = Relational Aggression

Table 9. Tests of Mediation (PA)

127

Table 9 continued

Step and Predictor B SE ß t ∆R2 ∆F df F Sig ∆F

Parent CU Traits Step 1 .129 10.40 (3, 210) 10.40 .000** Constant .305 .192 1.587 Gender -.222 .130 -.110 -1.708 SES -.085 .063 -.086 -1.349 RA .085 .085 .085 .991 Step 3 .026 6.47 (1, 209) 9.62 .000* * Rel Agg .138 .071 .139 .937 Step 4 .036 9.24 (1, 208) 9.85 .000** PA .257 .084 .259 3.040

Teacher Oppositional Step 1 .546 76.02 (3, 190) 76.02 .000** Constant -.268 .137 -1.961 Gender .198 .091 .098 2.172 SES -.049 .045 -.048 -1.081 RA .439 .062 .420 7.071 Step 3 .040 18.46 (1, 189) 66.87 .000** Rel Agg .109 .058 .109 1.888 Step 4 .065 35.17 (1, 188) 70.20 .000** PA .387 .065 .383 5.930

Teacher CU Traits Step 1 .258 21.55 (3, 186) 21.55 .000** Constant .653 .192 3.396 Gender -.459 .128 -.227 -3.588 SES -.110 .064 -.107 -1.714 RA .135 .086 .130 1.568 Step 3 .012 3.01 (1, 185) 17.09 .085 Rel Agg .003 .080 .003 .032 Step 4 .059 16.17 (1, 184) 18.03 .000** PA .364 .091 .363 4.022

Teacher Oppositional - BOYS Step 1 .488 51.46 (2, 108) 51.46 .000** Constant -.074 .059 -1.249 SES -.055 .063 -.056 -.881 RA .360 .075 .381 4.803 Step 2 .020 4.37 (1, 107) 36.84 .039* Rel Agg -.006 .079 -.006 -.007 Step 3 .105 28.89 (1, 106) 42.05 .000** PA .417 .078 .473 5.375

Continued

128

Table 9 continued

Step and Predictor B SE ß t ∆R2 ∆F df F Sig ∆F

Teacher Oppositional - GIRLS Step 1 .663 78.68 (2, 80) 78.68 .000** Constant .154 .066 2.330 SES -.066 .062 -.062 -1.069 RA .582 .106 .475 5.505 Step 2 .046 12.39 (1, 79) 64.05 .001** Rel Agg .163 .082 .163 1.990 Step 3 .038 11.79 (1, 78) 57.54 .001** PA .415 .121 .313 3.433

129

APPENDIX B

FIGURES

130

Child Social Problems - Rel Agg x Sex Interaction

0.6 0.5 0.4 0.3 0.2 Boys 0.1 Girls 0 -0.1 -0.2 Child Social Problems Social Child -0.3 Low Rel Agg High Rel Agg Relational Aggression

Note: This graph depicts standardized Relational Aggression scores 1 SD above and below the mean. RA is held constant at its mean. The values for Child Social Problems are also standardized.

Figure 1. Relational Aggression x Gender interaction in predicting Child-Rated Social Problems

131

Child Internalizing - Rel Agg x Gender Interaction

0.14 0.12 0.1 0.08 Boys 0.06 Girls

Problems 0.04 0.02 Child Internalizing Child Internalizing 0 Low Rel Agg High Rel Agg Relational Aggression

Note: This graph depicts standardized Relational Aggression scores 1 SD above and below the mean. RA is held constant at its mean. The values for Child Internalizing Problems are also standardized.

Figure 2. Relational Aggression x Gender interaction in predicting Child-Rated Internalizing Problems

132

Teacher Oppositional - Rel Agg x Gender Interaction

0 -0.1 -0.2 -0.3 -0.4 Boys -0.5 Girls -0.6 -0.7 -0.8 Teacher Oppositional -0.9 Low Rel Agg High Rel Agg Relational Aggression

Note: This graph depicts standardized Relational Aggression scores 1 SD above and below the mean. PA is held constant at its mean. The values for Teacher Oppositional are also standardized.

Figure 3. Relational Aggression x Gender interaction in predicting Teacher-Rated Oppositional Behaviors

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1.21 Child

1.25 Parent

2.72 R el Teach

0.78 Child 0.71 1.16 Parent

1.20 Teach 0.11 0.82

1.31 Child 0.72 0.80 0.28 Parent

1.17 Teach 0.28

0.53 Child

Soc 0.79 Parent 0.59

Teach

Model fit: Chi-square = 51.93, df = 30, p = 0.01, RMSEA = 0.075

Note: Paths representing correlations between methods are not depicted in this model.

Figure 4. MTMM for Boys’ Social Problems

134

Figure 4 Continued. MTMM for Boys’ Internalizing Problems

1.06 Child

1.18 Parent

3.03 R el Teach

0.67 Child 0.70 1.05 Parent

1.44 Teach 0.49 0.80

1.28 Child 0.73 0.75 0.45 Parent

1.36 Teach 0.55

3.97 Child

I nte r 3.35 Parent 0.76

Teach

Model fit: Chi-square = 51.47, df = 30, p = 0.01, RMSEA = 0.074

Note: Paths representing correlations between methods are not depicted in this model.

135

Figure 4 Continued. MTMM for Boys’ Inattention

0.69 Child

0.93 Parent

1.62 R el Teach

1.07 Child 0.72 1.10 Parent

3.24 Teach 0.43 0.72

1.26 Child 0.74 0.69 0.31 Parent

1.45 Teach 0.46

1.41 Child

I natt 2.48 Parent 4.10

Teach

Model fit: Chi-square = 41.19, df = 30, p = 0.08, RMSEA = 0.053

Note: Paths representing correlations between methods are not depicted in this model.

136

Figure 4 Continued. MTMM for Boys’ CU Traits

0.27 Child

0.61 Parent

2.19 R el Teach

0.70 Child 0.70 1.15 Parent

3.80 Teach 0.31 0.77

0.93 Child 0.64 0.50 0.05 Parent

1.66 Teach 0.60

0.50 Child

CU 0.94 Parent 1.05

Teach

Model fit: Chi-square = 57.07, df = 30, p = 0.00, RMSEA = 0.083

Note: Paths representing correlations between methods are not depicted in this model.

137

Figure 4 Continued. MTMM for Boys’ Delinquency (3 Factors)

Child 1.64 Parent Delinquency 0.94 Measures

Teach 0.92

1.36 Child

1.04 Parent Relational Agg Measures 1.21 R el Teach

1.08 Child 0.53 0.92 Parent

0.48 Teach 0.98

1.74 Child 0.71 0.43 Parent

1.02 Teach

Model fit: Chi-square = 39.60, df = 33, p = 0.20, RMSEA = 0.039

Note: Paths representing correlations between methods are not depicted in this model.

138

2.17 Child

1.77 Parent

0.99 R el Teach

0.83 Child 0.59 0.85 Parent

0.67 Teach 0.16 0.62

0.97 Child 0.76 0.42 0.56 Parent

1.08 Teach 0.09

0.40 Child

Soc 0.55 Parent 0.54

Teach

Model fit: Chi-square = 30.75, df = 30, p = 0.43, RMSEA = 0.017

Note: Paths representing correlations between methods are not depicted in this model.

Figure 5. MTMM for Girls’ Social Problems

139

Figure 5 Continued. MTMM for Girls’ Inattention

2.20 Child

1.39 Parent

1.46 R el Teach

1.06 Child 0.68 0.86 Parent

0.78 Teach 0.72 0.76

1.34 Child 0.88 0.52 0.74 Parent

0.96 Teach 0.54

2.64 Child

I natt 2.60 Parent 3.52

Teach

Model fit: Chi-square = 52.84, df = 30, p = 0.01, RMSEA = 0.091

Note: Paths representing correlations between methods are not depicted in this model.

140

Figure 5 Continued. MTMM for Girls’ Delinquency (3 Factors)

Child 0.76 Parent Delinquency 1.45 Measures

Teach 1.39

0.59 Child

2.71 Parent Relational Agg Measures 2.61 R el Teach

0.67 Child 0.89 1.70 Parent

0.98 Teach 0.88

0.41 Child 0.83 1.63 Parent

0.67 Teach

Model fit: Chi-square = 52.98, df = 33, p = 0.02, RMSEA = 0.082

Note: Paths representing correlations between methods are not depicted in this model.

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APPENDIX C

IN-CLASS STUDY DESCRIPTION

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Script for In-Class Study Announcement

“I’m here to tell you about a research study being done in the Department of Psychology at the Ohio State University. We’re looking for volunteers to participate in the study. Each participant will be paid $40 for their time and we will also donate $25 per participant to a fund to support your classrooms. The study is meant to help us understand why different young people perceive things that happen in the world around them in such different ways. For example, some people tend to focus on the negative while others focus on the positive. We’re trying to understand some of the reasons for such differences. We are also studying the ways in which parents perceive their children. Some parents seem to understand their children very well while others don’t seem to understand them. We’re trying to understand why that is.

We hope you’ll consider helping us by participating in the study. If you do participate, here is what will happen. We’ll arrange a time to come to your home where we will meet with you and one of your parents. During that visit, we will have you and your parent fill out some questionnaires and do a computer activity. The questionnaires ask about many different aspects of you personality and behavior – for example, how often you behave in various ways and how you feel in different situations. We will also have one of your teachers fill out some questionnaires. As I noted earlier, participants will receive $40 for taking the time to help us out and, for your teacher’s help, we will donate $25 to help support his or her classroom here at school.

If you decide you might like to be part of the study, your parents can call us for more information.

Thanks a lot for listening. We hope you’ll be a part of the study because it will help us learn many interesting and important things that may help other young people in the future.

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APPENDIX D

PARENT LETTER

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Dear Parent:

I am writing in hopes that you will consider allowing your 6th, 7th, or 8th grade son or daughter to participate in a research study that is being conducted through the Department of Psychology at the Ohio State University. Participants in the study will receive $40 for their time and we will also donate $25 to support your child’s school. The study is focused on two topics. First, it is meant to help us understand some of the reasons why young people often react so differently from one another to the same situations. We're trying to understand some of the reasons for those differences – for example, why some young people usually tend to focus on the negative while others focus on the positive. Second, we are trying to understand how parents perceive and interpret information about their children. Some psychologists have argued that parents are commonly the last to notice changes in their child whereas other people have argued that parents are especially likely to notice changes in their child. Surprisingly, nobody has ever studied this question and we are hoping that we can help settle the question through this study.

I hope you will encourage your son or daughter to participate. By doing so, you and your child will help us learn a great deal about how parents view their children, how children perceive the world and how that affects their reactions to things that happen to them. Ultimately, we hope that understanding differences in how typical children like yours perceive and react to their experiences will allow us to design better ways of helping young people who have adjustment difficulties. With regard to parents’ perceptions of their children, by studying typical parents, we hope to better understand how it is that some parents seemingly fail to notice when their child begins to develop a problem while other parents notice very quickly. By participating, you and your son or daughter will also get an opportunity to see how some types of psychological research are conducted.

Here is what will happen if you and your child participate. We will arrange a time at your convenience for your child and one parent to complete the study. To make this as easy as possible, we will be happy to come to your home to do this. However, if you prefer, you can come to our laboratory in the Psychology Department at The Ohio State University. During this session, your child will complete a series of questionnaires about his or her behavior, emotional experiences, and personality. He or she will also complete a computer activity in which he or she will see pairs of words on the computer screen and be asked to press a button every time he or she sees a small dot appear. Also during this session, the participating parent will complete a variety of questionnaires about the child and attitudes about being a parent. The participating parent will also complete a brief computer activity in which she/he will be asked to respond to words that may or may not

145

describe the participating child. All together, these things will take about 2-hours. To help compensate you and your child for the time the study takes, we will pay you $40. Of course, if you come to campus, we will also pay for your parking.

Finally, we will send several questionnaires to one of your child's teachers. We'll get your child’s input regarding which of his or her teachers is the best choice. The questionnaires the teacher will complete ask for his or her point of view on your child’s behavior in school and with other children. To compensate the teacher for her/his time, we will donate $25.00 to help your child’s teacher support her/his classroom.

Unavoidably, some of the questions in a study of children's personalities are somewhat personal in nature (e.g., “Do you worry a lot?”; “Do you have a hot temper?”; “How often are you unhappy, sad, or depressed?”). However, rest assured that all the information we gather in the study will be kept strictly confidential. Participant's names will not be placed on any of the study materials. Instead, each participant will be given a code number. Thus, you can be confident that nobody other than authorized study personnel will have access to any information that can be linked to your child. All study results will be reported only in terms of group averages. Because teachers will need to know whom the questionnaire is about, your child's name will be attached to those questionnaires. However, we will ask teachers to remove participants' names from the questionnaires before sending them back to us.

If you would like your child to participate or you would like more information about the study, please call the Childhood Cognition and Emotion Laboratory at 247-6332 at your earliest convenience (or if you prefer, you can send an e-mail message to [email protected]).

Thanks very much for taking the time to consider this invitation.

Sincerely,

Michael W. Vasey, Ph.D.

146

APPENDIX E

CONSENT FORM

147

CONSENT FOR PARTICIPATION IN

SOCIAL AND BEHAVIORAL RESEARCH

I consent to my child’s and my own participation in research entitled:

Dimensions of Personality and Children’s Information Processing

Dr. Michael Vasey______or his authorized representative has (Principal Investigator) explained the purpose of this study, the procedures to be followed, and the expected duration of my participation. Possible benefits of the study have been described as have alternative procedures, if such procedures are applicable and available.

I acknowledge that I have had the opportunity to obtain additional information regarding the study and that any questions I have raised have been answered to my full satisfaction. Further, I understand that I am free to withdraw consent at any time and to discontinue participation in the study without prejudice to me.

Finally, I acknowledge that I have read and fully understand the consent form. I sign it freely and voluntarily. A copy has been given to me.

Date: ______Signed: ______(Participant)

Signed: ______Signed: ______(Principal Investigator or his (Parent or Guardian) Authorized Representative)

148

APPENDIX F

TEACHER LETTER

149

Dear Teacher’s Name:

My name is Dr. Michael Vasey, I am an associated professor of psychology at the Ohio State University. I am writing to ask for your help in a research project concerning the relationship between young people’s personalities and how they allocate their attention to their own thoughts and feelings as well as to the world around them. There is reason to believe that certain ways of allocating attention are associated with aspects of personality. For example, children with low self-esteem appear prone to allocate attention to negative information about themselves while ignoring positive information. To the extent that they do so, such children’s low self-esteem is likely to persist and even worsen. In effect, such children’s experience of the world is skewed by how they allocate their attention.

You can help us learn more about how children’s personalities affect how children allocate attention to events around them by providing information about the student named on the enclosed questionnaires. Please note that this child has volunteered to participate in this study and his or parents have provided written consent for him or her to participate. A copy of the consent form is attached to assure you of this fact. The questionnaires typically take about 15-20 minutes to complete. I hope you will take time and complete them. The addition of you perspective will greatly increase the level of confidence in the results of the study. To compensate you for your time, you will receive $25 meant to support your classroom for each child for whom you complete a packet of questionnaires. We understand that teachers often pay for classroom materials or events out of their own pocket and we hope this payment will help to offset such expenses. You will receive a check in the mail in four to six weeks.

When you have completed the questionnaires, please sign and date the enclosed form. Remove the slip on which the child’s name appears and mail the forms back to Ohio State using the enclosed envelope. Thank you very much for your time and trouble. If you have any questions or concerns, please free to give us a call at 247-6332.

Sincerely,

Michael W. Vasey, Ph.D. Associate Professor

150