Executive Functioning, Language, Visual Abstract Reasoning, and Gender as Predictors of Relational and Physical Aggressi...

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Executive Functioning, Language, Visual Abstract Reasoning, and Gender as Predictors of Relational and Physical Aggression among Young Children
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1 online resource (129 p.)
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english
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Mancil, Twyla L
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University of Florida
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Degree:
Doctorate ( Ph.D.)
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University of Florida
Degree Disciplines:
School Psychology, Special Education, School Psychology and Early Childhood Studies
Committee Chair:
Smith-Bonahue, Tina M
Committee Members:
Joyce, Diana
Kranzler, John H
Kemple, Kristen M
Marcia, Wiesel-Leary

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Subjects / Keywords:
aggression -- brief -- evt-2 -- executive -- functioning -- nepsy-ii -- physical -- raven's -- relational
Special Education, School Psychology and Early Childhood Studies -- Dissertations, Academic -- UF
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School Psychology thesis, Ph.D.
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Abstract:
Given that American youth are engaging in violent and aggressive acts at increasingly younger ages (Lord and Mahoney, 2007; McMahon and Washburn, 2003), knowledge regarding the factors contributing to such violence and aggression is crucial to inform prevention and intervention practices. The majority of previous studies have focused on older children and adolescents, with much less attention to the development and display of aggression among young children. As such, gaps and discrepancies in the knowledge base still remain, and very little research has examined executive functioning among young children, especially how these functions impact aggressive behavior. The present study examined predictors of relational and physical aggression among five- and six-year-old children, including executive functioning, gender, expressive language, and nonverbal reasoning. Age was included in each regression model to account for its influence on variables of interest. The present study also examined the correlations among the BRIEF and the NEPSY-II Attention/EF domain. Results of the study indicate executive functioning significantly predicted physical aggression among young children, as did gender, when controlling for all other variables of interest. Age, expressive language, and nonverbal reasoning did not significantly predict unique variance in physical aggression. Only gender significantly predicted relational aggression when controlling for all other variables of interest. Gender was weakly but significantly correlated with both physical and relational aggression when controlling for age, such that boys were more likely to exhibit physical aggression than girls and girls were more likely to exhibit relational aggression than boys. Expressive language, nonverbal reasoning, and executive functioning were all moderately correlated with one another when controlling for age. Relational aggression and physical aggression were strongly correlated when controlling for age. Furthermore, NEPSY-II sum of scaled scores was weakly but significantly correlated with BRIEF total raw scores when controlling for age. The NEPSY-II Inhibition-Naming and Inhibition-Inhibition subtest errors scores yielded the strongest correlations with the BRIEF subscales. Results of the study are discussed in relation to Barkley's attention research and the social information processing model.
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In the series University of Florida Digital Collections.
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Includes vita.
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This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Twyla L Mancil.
Thesis:
Thesis (Ph.D.)--University of Florida, 2012.
Local:
Adviser: Smith-Bonahue, Tina M.
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RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2013-08-31

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1 EXECUTIVE FUNCTIONING, LANGUAGE, VISUAL ABSTRACT REASONING, AND GENDER AS PREDICTORS OF RELATIONAL AND PHYSICAL AGGRESSION AMONG YOUNG CHILDREN By TWYLA LUCINDA MANCIL A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF TH E UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2012

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2 2012 Twyla Lucinda Mancil

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3 To my mom

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4 ACKNOWLEDGMENTS I would first like to thank Dr. Tina Smith Bonahue, the chair of my doctoral committee, for all of the advice, guidance, sharing of knowledge, and reassurance over the past several years. Her support has been tremendous and greatly appreciated. I also want to thank Dr. John Kranzler, Dr. Diana Joyce, Dr. K risten Kemple, and Dr. Marcia Leary for their contributions as committee members. In addition, I would like to thank Shauna Miller for her help in data collection. Without her assistance, this research project would have taken a considerable more amount of time. I also want to thank Stacy Bender and Meagan Nalls for their support and constant encouragement regarding the completion of my dissertation during my internship. Most of all, I would like to thank God for the gifts I have been given in this life an d for the constant source of hope. Last, but by no means least, I would like to thank my mother for standing by me through the ups and downs of life and graduate school, for listening patiently to all of my woes, and for the endless prayers on my behalf. N o words could ever sufficiently describe my gratitude for her love and support.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 10 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 LITERATURE REVIEW ................................ ................................ .......................... 14 Aggression Overview ................................ ................................ .............................. 14 Aggression in Young Children ................................ ................................ .......... 16 Types of Aggression ................................ ................................ ......................... 18 Consequences of Relational and Physical Aggression ................................ .... 23 Correlates of Relational and Physical Aggression in Early Childhood .............. 26 Gender differences ................................ ................................ .................... 27 Peer status, friendships, and prosocial behavior ................................ ........ 29 Language skills ................................ ................................ .......................... 31 Social Information Processing Model ................................ ................................ ...... 33 ................................ ................................ .................. 33 The SIPM and Aggression Research ................................ ............................... 35 Executive Functioning ................................ ................................ ............................. 37 Defining EF ................................ ................................ ................................ ....... 38 Neuropsychological perspective ................................ ................................ 38 ................................ ................................ ....... 41 Hierarchical structure of EF ................................ ................................ ........ 43 Definitional consensus ................................ ................................ ............... 46 EF in Earl y Childhood ................................ ................................ ....................... 48 Assessment of EF in Early Childhood ................................ .............................. 52 Individual EF tasks/techniques ................................ ................................ ... 53 Norm referenced EF measures ................................ ................................ .. 56 EF and Intelligence ................................ ................................ ........................... 57 EF and the SIPM ................................ ................................ .............................. 58 Purpose of the Present Study ................................ ................................ ................. 61 2 METHODS ................................ ................................ ................................ .............. 66 Participants ................................ ................................ ................................ ............. 66 Procedure ................................ ................................ ................................ ............... 68 Measures ................................ ................................ ................................ ................ 68

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6 Aggression and Prosocial Behavior ................................ ................................ .. 68 EF Measures ................................ ................................ ................................ .... 69 Developmental Neuropsychological Assessment, Second Edition ............ 69 Behavior Rating Invent ory of Executive Function ................................ ....... 71 Language Measure: Expressive Vocabulary Test, Second Edition (EVT 2) ..... 72 Visual Abstract Reasoning M Matrices (CPM) ................................ ................................ ............................. 72 Research Questions ................................ ................................ ............................... 73 Data Analysis ................................ ................................ ................................ .......... 74 Descriptive Statistics ................................ ................................ ........................ 74 Correlational Analyses ................................ ................................ ..................... 74 Comparison of Means ................................ ................................ ...................... 75 Multiple Regression Analyses ................................ ................................ .......... 75 3 RESULTS ................................ ................................ ................................ ............... 77 Descriptive Statistics ................................ ................................ ............................... 77 Correlation Analyses ................................ ................................ ............................... 81 Comparison of Means ................................ ................................ ............................. 84 Multiple Regression Analyses ................................ ................................ ................. 84 Relational Aggression ................................ ................................ ...................... 86 Physical Aggression ................................ ................................ ......................... 86 4 DISCUSSION ................................ ................................ ................................ ......... 98 Predicting Relational and Physical Aggression ................................ ....................... 99 Partial Correlations Between EF and Relational and Physical Aggression ........... 100 Partial Correlations Between Gender and Relational and Physical Aggression .... 101 Partial Correlations Between Relational and Physical A ggression ....................... 102 Partial Correlations Between EF and Language ................................ ................... 103 Partial Correlations Between EF and Visual Abstract Reasoning ......................... 104 Partial Correlations Between the NEPSY II and BRIEF ................................ ........ 106 Summary and Implications for Future Research ................................ ................... 108 Limitations ................................ ................................ ................................ ............. 115 APPENDIX: PRESCHOOL SOCIAL BEHAVIOR SCALE TEACHER FORM ....... 117 LIST OF REFERENCES ................................ ................................ ............................. 120 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 129

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7 LIST OF TABLES Table page 1 1 Proposed EF specific abilities ................................ ................................ ............. 64 3 1 Descriptive statistics for outcome variables: PSBS T relational aggression raw scores (RA) and PSBS T physical aggression raw scores (PA) (N = 101) .. 87 3 2 Descriptive statistics for dependent and independent variables by gender (N = 101) ................................ ................................ ................................ ................. 87 3 3 Descriptive statistics for predictor variables: Gender, CPM total raw score, EVT 2 total raw score, and NEPSY II sum of scaled scores (N = 101) .............. 87 3 4 NEPSY II subtest scaled score averages. ................................ .......................... 88 3 5 Descriptive statistics for NEPSY II sum of scaled scores and subtest raw scores (N = 101) ................................ ................................ ................................ 88 3 6 Descriptive statistics for BRIEF total and subscale raw scores (N = 101) .......... 88 3 7 Pearson product moment and point biserial correlations between age and ethnicity and outcome and predictor variables (N = 101) ................................ .... 89 3 8 Partial correlations among outcome and predictor variables and the BRIEF total raw score controlling for age (N = 101) ................................ ....................... 89 3 9 Partial correlations among NEPSY II subtest raw score and PSBS T relational aggression and physical aggression raw scor es control ling for ag e ... 90 3 10 Partial correlations among NEPSY II subtest raw scores and EVT 2 total raw scores controlling for age (N= 101) ................................ ................................ .... 90 3 11 Partial correlations among NEPSY II subtest raw scores and CPM total raw scores controlling for age (N = 101) ................................ ................................ ... 91 3 12 Partial correlations among NEPSY II a nd BRIE F scores controlling for age (N = 101) ................................ ................................ ................................ ................. 91 3 13 Independent samples t tests among gender and PSBS T relational and physical aggression raw scores (N = 101) ................................ .......................... 92 3 14 Hierarchical multiple regression analyses predicting PSBS T relational aggression raw scores from NEPSY II sum of scal ed scores (N = 101) ............. 92 3 15 Hierarchical multiple regression analyses predicting P SBS T relational aggression raw scores from gender (N = 101) ................................ ................... 93

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8 3 16 Hierarchical m ultiple regression analyses predicting PSBS T relational aggression raw sco res from CPM total raw scores (N = 101) ............................. 93 3 17 Hierarchical multiple regression analyses predicting PSBS T relational aggression raw scor es from EVT 2 total raw scores (N = 101) .......................... 94 3 18 Hierarchical multiple regression analyses predicting PSBS T relational aggression raw scor es from age ( N = 101) ................................ ......................... 94 3 19 Hierarchical multiple regression analyses predicting PSBS T physical aggression raw scores from NEPSY II sum of scaled scores (N = 101) ............. 95 3 20 Hierarchical multiple regression analyses predicting PSBS T physical aggression raw scores from gender (N = 101) ................................ ................... 95 3 21 Hier archical multiple regression analyses predicting PSBS T physical aggression raw scores from CPM total raw scores (N = 101) ............................. 96 3 22 Hierarchical multiple regression analyses predicting PSBS T physical aggression raw scor es from EVT 2 total raw scores (N = 101) .......................... 96 3 23 Hierarchical multiple regression analyses predicting PSBS T physical aggression raw scores from age (N = 101) ................................ ......................... 97

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9 LIST OF FIGURES Figure page 1 1 Six step SIPM as proposed by Crick and Dodge (1994) ................................ .... 65

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10 LIST OF ABBREVIATION S AAE Auditory Attention errors AAT Auditory Attention total AB A not B ADHD Attention deficit/hyperactivity dis order BRIEF Behavior Rating Inventory of Executive Function BRIEF P Behavior Rating Inventory of Executive Function, Preschool Version CD Conduct disorder CPM DFT Design Fluency total D KEFS Delis Kaplan Executive Func tion System EC Emotional Control EF Executive functioning ELLS English Language Learners ETH Ethnicity EVT 2 Expressive Vocabulary Test, Second Edition IIE Inhibition Inhibition errors IIT Inhibition Inhibition time INE Inhibition Naming errors INH Inhibit ion INI Initiate INT Inhibition Naming time M Monitor NEPSY II Developmental Neuropsychological Assessment, Second Edition ODD Oppositional defiant disorder

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11 OM Organization of Materials PA Physical aggression PO Plan/Organize PPVT IV Peabody Picture Vocabu lary Test, Fourth Edition PSBS T Preschool Social Behavior Scale, Teacher Form RA Relational aggression S Shift SD Standard deviation SIPM Social information processing model SPSS 19.0 Statistical Package for the Social Sciences, Version 19.0 ST Statue t otal WM Working Memory

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12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy EXECUTIVE FUNCTIONING, LANGUAGE, VISUAL AB STRACT REASONING, AND GENDER AS PREDICTORS OF RELATIONAL AND PHYSICAL AGGRESSION AMONG YOUNG CHILDREN By Twyla Lucinda Mancil August 2012 Chair: Tina Smith Bonahue Major: School Psychology Given that A merican youth are engaging in violent and aggressive acts at increasingly younger ages ( Lord & Mahoney, 2007; McMahon & Washburn, 2003 ) k nowledge regarding the factors contributing to such violence and aggression is c rucial to inform prevention and intervention practices. The majority of previous studies h ave focused on older children and adolescents with much less attention to the development and display of aggression among young children As such, gaps and discrepancies in the knowledge base still remain and v ery little research has examined executive f unctioning among young children, especially how these functions impact aggressive behavior. The present study examine d predictors of relational and physical aggression among five and six year old children, including executive functioning, gender, expressi ve language, and nonverbal reasoning. Age was included in each regression model to account for its influence on variable s of interest. The present study also examine d the correlation s among the BRIEF and t he NEPSY II Attention/EF domain Results of the st udy indicate executive functioning significantly predicted physical aggression among young children, as did gender, when controlling for all other variables

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13 of interest. Age, expressive language, and nonverbal reasoning did not significantly predict unique variance in physical aggression. Only gender significantly predicted relational aggression when controlling for all other variables of interest. Gender was weakly but significantly correlated with both physical and relational aggression when controlling f or age, such that boys were more likely to exhibit physical aggression than girls and girls were more likely to exhibit relational aggression than boys. Expressive language, nonverbal reasoning, and executive functioning were all moderately correlated with one another when controlling for age. Relational aggression and physical aggression were strongly correlated when controlling for age. Furthermore, NEPSY II sum of scaled scores was weakly but significantly correlated with BRIEF total raw scores when cont rolling for age. The NEPSY II Inhibition Naming and Inhibition Inhibition subtest errors scores yielded the strongest correlations with the BRIEF and the social infor mation processing model.

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14 CHAPTER 1 LITERATURE REVIEW Chapter 1 provides a review of the current literature regarding relational and physical aggression, the social information processing model (SIPM), and executive functioning (EF). The first section fo cuses on examining the literature in relation to the classification of aggressive acts (i.e., the types of aggression), the consequences of relational and physical aggression among males and females, and correlates of relational and physical aggression in young childhood, including gender differences, peer status and friendships, prosocial behavior, and language skills. A review of the SIPM in relation to the different types of aggression is then presented, followed by a review of the literature on EF, with emphasis on the neuropsychological perspective, consensus regarding the EF construct. Next, a review of the EF literature in relation to early childhood, most spec ifically the assessment of EF in early childhood, is presented. Finally, the literature relating to EF and intelligence and EF and the SIPM are presented. Chapter 1 is concluded by a brief overview of the purpose of the present study. Aggression Overview V iolence in America continues to be a pervasive problem, with individuals participating in violent acts at increasingly younger ages (Lord & Mahoney, 2007; McMahon & Washburn, 2003). To further complicate matters, children and adolescents diagnosed with cer tain behavioral disorders, such as conduct disorder (CD) and oppositional defiant disorder (ODD), have worse prognoses (i.e., are at increased risk for aggressive and/or criminal behavior) when diagnosis comes prior to mid

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15 adolescence, as well as when trea tment is delayed (Hodgins, Cree, Alderton, & Mark, 2008; Kempes, Matthys, de Vries, & van Engeland, 2004). Furthermore, developmentally inappropriate aggressive behavior in early childhood is a good predictor of later aggressive behavior (Juliano, Werner, & Cassidy, 2006), as well as other problems such as school failure, alcohol and drug use, depression, and unemployment (Tremblay, 2004). As such, a need exists for the development of methods for early identification of children at risk for developing aggre ssive or violent behaviors, as well as for the development of prevention and intervention programs. However, to accomplish this objective, more research is needed to identify the correlates of early aggression, including intra individual factors that may c ontribute to its expression and support present social information processing theories of aggressive behavior (Ostrov &Godleski, 2010). The present section provides an overview of the aggression literature in relation to young children. The section begins with a brief overview of the literature concerning the development and expression of aggression among young children and suggestions regarding future research in identifying possible risk factors. Next, the literature regarding the various types of aggress ion, including relational and physical aggression, exhibited among young children is explored, followed by an examination of the research regarding the consequences associated with both relational and physical aggression among aggressors and victims at you ng ages. Correlates of relational and physical aggression are then reviewed, with special attention to gender, peer status and friendships, prosocial behavior, and language.

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16 Aggression in Young Children This section provides an overview of the literature r egarding physical and relational aggression among young children. A discussion of the developmental trajectory of physical aggression is first presented, followed by support for the occurrence of relational aggression among young children. Attention is the n given to discussing the mixed results regarding the various risk factors (e.g., gender) associated with the specific types of aggression and the need for future research to explore other possible risk factors so as to better inform prevention and interve ntion practices. The present section concludes with a discussion of possible future avenues of research regarding the risk factors associated with aggression among young children, with special attention given to the suggestion of examining EF and other cog nitive abilities in relation to physical and relational aggression. Physical aggression appears to be a very common behavior during the preschool years. In fact, physical aggression is highest during the preschool years despite previous beliefs that aggre ssion peaks in adolescence or early adulthood (Tremblay, 2004). Studies examining the developmental trajectory of aggression show that physical aggression typically peaks between the ages of 24 and 42 months (Tremblay, 2004) or 24 to 36 months (Alink et al ., 2006). However, for some preschool aged children, levels deemed to be a developmentally appropriate age. For example, observing a two year old shove another child or sit on an other child for a toy or space is likely a more common occurrence than observing a five year old child committing the same aggressive behavior.

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17 In addition to physical aggression, relational aggression is also observable among young children, as a plethor a of recent research has indicated (e.g., Carpenter & Nangle, 2006; Casas et al., 2006; Crick, Casas, & Ku, 1999; Crick, Ostrov, Burr, Cullerton Sen, Jansen Yeh, & Ralston, 2006; Juliano et al., 2006; Werner, Senich, & Przepyszny, 2006). Given that recent research findings also indicate that the display of physical aggression and relational aggression in early childhood are associated with psychological maladjustment for both the aggressor and the intended victim (e.g., Crick, Casas, & Mosher, 1997; Juliano et al., 2006), a major quest for researchers has been to identify those factors associated with increased risk for physically and relationally parental care factors, and classr oom ecology factors. Because the risk factors associated with relational aggression may not be the same as those associated with physical aggression, more research is needed to examine possible predictors or risk factors of relational aggression so as to b etter inform prevention and intervention practices. As it stands, much of the early prevention and intervention programs targeting aggression, such as Second Step: A Violence Prevention Curriculum (Grossman et al., 1997), have not been specifically designe d to target relational aggression. Given the mixed results of studies examining possible predictors of relational aggression in early childhood, such as those examining gender (e.g., Crick & Grotpeter, 1995 vs. McEvoy, Estrem, Rodriguez, & Olson, 2003), an d the lack of research examining various intra individual factors (e.g., differences in cognitive or EF abilities) among relational aggressors, the impact of such programs on relational aggression is unknown. Thus, more research is needed to further examin e the correlates of not only physical

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18 aggression but also relational aggression in early childhood to insure earlier identification and the development of prevention/intervention programs that specifically and successfully target both types of aggression. One potential avenue of research is examining the relationship among various EF skills and aggression. EF skills include those higher order thinking abilities that enable individuals to monitor and control their thoughts and behavior. Although this avenue of aggression research seems promising, research in this area is fairly recent and has not yet been applied extensively to children as young as five and six years of age. This is most likely due to previous assumptions that EF was not developed until midd le or late childhood and adolescence, as well as to a lack of valid methods for assessing EF in young children. Recently, however, theorists have begun to conceptualize the development of EF as occurring continuously from early childhood to adulthood, and new, standardized methods have been developed to assess EF in children as young as three years of age. Overall, the study of EF in relation to aggression and other aggression predictor variables may shed important insight into the development and continuat ion of both physically and relationally aggressive behaviors so as to better inform identification and prevention and intervention practices. Types of Aggression The purpose of the present section is to provide an overview of the research regarding the di fferent types of aggression. A brief history of the study of aggression is classifying various aggressive behaviors within the literature are reviewed, including direct and indirect aggression, overt and covert aggression, and physical, relational, and verbal aggression. Finally, a distinction is made between the forms and functions of

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19 physical aggression and r elational aggression. Prior to the 1960s, aggression was believed to be a relatively homogenous category of behavior, and the aggression literature consisted of two competing theories: aggression model an aggression is an acquired instrumental behavior controlled by an anticipated reward (Dodge & Coie, 1987; Kempes et al., 2005). Eventually, however, it was recognized in the literature that these two competing theories of aggression were referring to diffe rent aspects of aggressive behavior and that there was a need for a more comprehensive theory of aggression that would incorporate aggressive behavior in its multiple forms (Kempes et al., 2005). As such, a plethora of research now exists in which the mult iple forms or types of aggression have been explored in relation to possible predictive variables. Despite theoretical progress and the ongoing study of aggression and its various forms, discrepancies still remain across the early childhood literature rega rding how to define and categorize aggressive acts. For instance, some researchers have categorized aggressive acts in terms of whether or not the aggressive act is premeditated or an impulsive reaction to a given situation, referring to these acts as eith er proactive (i.e., instrumental) or reactive (i.e., hostile) in nature. Typically, proactive or instrumental aggression includes those aggressive acts that serve to obtain a reward or some form of personal gain (thus reflecting the theory of aggression po sited

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20 by Bandura), whereas reactive or hostile aggression includes those aggressive acts that are characterized by anger and hostility and are often retaliatory in nature (thus aggression model) (Ellis, Weis s, & Lochman, 2009). Dodge and Coie (1987) first examined differences among proactive and reactive aggressors when they analyzed hostile attributions between these two types of aggressors. However, these terms for aggressive acts were previously used to de scribe aggression in animals and adults and were studied independently of one another within the child focused research (e.g., Dodge (1980) examined reactive aggression only and Olweus (1978) examined proactive aggression only) (Dodge & Coie, 1987; Kempes many researchers have continued to classify aggressive acts as reactive or proactive, including researchers studying the display of aggression during early childhood (e.g., McAuliffe, Hubba rd, Rubin, & Morrow, 2006; Kimonis et al., 2006; Ellis et al., 2009). Still, other researchers have classified aggressive acts according to whether or not the acts are direct or indirect (i.e., overt or covert) (e.g., Card, Sawalani, Stucky, & Little, 200 8; Nesdale, Milliner, Duffy, & Griffiths, 2009; Valles & Knutson, 2008). Direct aggression is characterized by straightforward attacks that are often visible and disruptive, whereas indirect aggression is characterized by attempts to inflict pain in a mann er that makes it seem as though there was no intention to inflict harm (Valles & Knutson, 2008). Thus, direct aggression may encompass physically aggressive acts, as well as overt verbal abuse (Nesdale et al., 2009). On the other hand, indirect aggression has also been referred to as social aggression or relational aggression (Musher Eizenman, Boxer, Danner, Dubow, Goldstein, & Heretick, 2004; Ostrov & Keating,

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21 elf esteem through such methods as spreading rumors or exclusion from desired activities (Nesdale et al., 2009). In this way, classifying aggression as direct or indirect allows researchers to encompass a multitude of aggressive behaviors. However, such br eadth of coverage may not always be desirable, especially when wanting to look at predictive variables of specific types of aggressive behavior. To improve precision of the language surrounding definitions of aggression, some researchers have moved toward categorizing aggressive acts based upon actual descriptions of the acts (e.g., Crick et al., 1997; Juliano et al., 2006; McEvoy et al., 2003; Ostrov & Keating, 2004). Within this framework, aggressive acts are usually classified as physical, relational, or verbal aggression. Physical aggression typically includes such behaviors as hitting, kicking, pushing, pulling, forcibly taking objects, or biting, whereas verbal aggression typically includes antagonistic teasing, calling others mean names, and making ve rbal threats to harm (Ostrov & Keating, 2004). Relational relationships and may include such acts as excluding others from a playgroup, spreading rumors, withdrawing friendship, maliciously telling lies, or ignoring a peer (Ostrov & Keating, 2004). As such, some forms of verbal aggression may also be considered relational aggression. aggression) Henrich, and Hawley (2003) found support for two overarching forms of aggression overt aggression and relational aggression. Acts classified as overt aggression included

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22 such b ehaviors as hitting, kicking, punching, threatening, and insulting and other your acts have at their core intentional physically aggressive acts or threats of physical aggre feelings of inclusion in a peer group. Overall, classifying aggressive behaviors as overt or relat ional is supported within the research when the functions of aggression are separated from the types of aggression. Accordingly, some authors have begun to study these two types of aggression to the exclusion of others on the basis that other proposed type s of aggression are actually better conceptualized as the functions of aggression (e.g., Juliano et al., 2006) for one could argue that two forms of aggression may share the same function. For instance, a child could threaten social exclusion to get anothe same desired item (e.g., shove the other child and snatch the doll). Understanding why child ren choose (or perhaps do not choose) one form of aggression over another when the end goal (i.e., the function) may be the same, such as getting a desired object, but the potential risk of detection may be different, remains an open question. Overall, agg ressive behavior selection may simply be related to control and their ability to plan and exhibit goal directed appears to require greater self control and planning due to the need to understand social contexts and manipulation strategies, as well as ways of avoiding detection. On

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23 the other hand, overt aggression appears to require less planning and self control and has greater poten tial for detection. Relational aggression, therefore, may not only be indicative of a social advance as described by Carpenter and Nangle (2006), but also a cognitive advance in EF abilities. The present section has provided a brief overview of the aggress ion literature as it relates to identifying and classifying aggressive behavior. Initial research by Dollard and Bandura eventually led to the description of aggressive acts as proactive and reactive. Other researchers have classified aggressive acts based on whether the act is direct or indirect, while others have classified aggressive acts according to descriptions of the actual aggressive behaviors being observed, including overt/physical, verbal, and relational aggression. Still, other researchers have attempted to distinguish the forms of aggressive behavior from their purpose or functions, referring to two overarching forms of aggression as overt (i.e., physical) aggression and relational aggression. These researchers posit that different forms of aggr ession can serve the same purpose or function, although some forms may prove to be more advanced than others. Overall, understanding why children choose one form of aggression over another is crucial to prevention and intervention. Consequences of Relatio nal and Physical Aggression The present section focuses on identifying the negative consequences associated with both physical and relational aggression. Attention is given to identifying such consequences among both aggressors and their victims. In additi on, gender differences in the experience of these negative consequences are also explored. Overall, the purpose of the present section is to highlight that both relational and physical

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24 aggression are associated with negative social and psychological adjust ment among young children The consequences of aggression on both the victim and the aggressor can be far reaching. Repeatedly, studies have illustrated that childhood aggression is a key predictor of future maladjustment (e.g., Crick, Ostrov, & Werner, 200 6; Houbre, Tarquinio, Thuillier, & Hergott, 2006; Huesmann, Dubow, & Boxer, 2009 ). Specific to early childhood, a study by Crick et al. (1997) examined relational and overt aggression in preschool to clarify whether or not either type of aggression would b e related to social and psychological maladjustment. In measuring social and psychological adjustment, Crick and colleagues used teacher reports of prosocial behavior and depressed affect as measured by the Preschool Social Behavior Scale Teacher Form (PSB S T), and peer both forms of aggression were significantly related to social and psy chological maladjustment. More specifically, high levels of relational aggression were related to high levels of peer rejection, though in boys relational aggression was also positively related to peer acceptance. On the other hand, Crick and colleagues fo und that overt aggression was positively related to peer rejection for both genders. Overt aggression was also negatively related to opposite sex peer acceptance among boys, as well as negatively related to both opposite and same sex peer acceptances amon g girls. In other words, girls were less likely to accept other boys and girls who were more overtly aggressive and boys were less likely to accept girls who were more overtly aggressive.

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25 Teacher reports of relational aggression among girls also predicted teacher reports of depressed affect. Another study by Crick et al. (1999) examined the impact of relational and physical aggression in preschool on the social and psychological adjustment of victims. In other words, this study looked at the impact of thes e two types of aggression on peer victimization in preschool. In general, they found that both relational and physical aggression victims experienced greater adjustment problems than did non victim peers. More specifically, however, they found that victims of relational aggression were more likely to have lower levels of peer acceptance, greater levels of peer rejection, more internalizing difficulties, and fewer positive peer relations than non victims. In terms of physical aggression, Crick and colleagues found that victims of physical aggression experienced greater levels of peer rejection, exhibited more hyperactive distractible behaviors, had fewer positive peer relations, and also exhibited more internalizing difficulties than non victims. Again, both relational and physical aggression had adverse effects on the social and psychological adjustment of children. A more recent study by Ostrov, Woods, Jansen, Casas, and Crick (2004) examined the social and psychological adjustment of both relational and phy sical aggressors and their intended victims. In their study, Ostrov and colleagues examined differences in social and psychological adjustment among preschoolers delivering or receiving physical and relational aggression. Overall, for boys, they found that delivered physical aggression was associated with peer rejection and a lack of prosocial behavior and that delivered relational aggression was associated with exclusion by peers. In turn, for girls, Ostrov and colleagues found that delivered relational ag gression was

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26 associated with a lack of prosocial behavior and that received relational aggression was associated with problematic peer relationships. In addition, girls who received physical aggression were also more likely to exhibit asocial behavior. Ov erall, the findings of Ostrov and colleagues underscore previous findings and emphasize the idea that social and psychological maladjustment is associated with both both gen ders. These findings also highlight the fact that relational aggression can be just as detrimental as physical aggression in terms of the impact on the aggressor and victim. Such findings emphasize the need to implement prevention and intervention strategi es as early as preschool in order to circumvent future maladjustment problems. However, in order to do so, clarification of how children make decisions regarding when and how to use the various types of aggression, as well as a greater understanding of the correlates of early childhood physical and relational aggression is needed. Though research has supported the relationship between aggression and social and psychological maladjustment among males and females, the research base regarding the exact relatio nship between gender and the types of aggression is mixed. Thus, more research is needed to clarify this issue. The next section will further explore the literature regarding the associations between gender and the types of aggression. Correlates of Relat ional and Physical Aggression in Early Childhood Previously, relational aggression was thought to occur only during later childhood and adolescence. As such, the majority of aggression research within early childhood has focused exclusively on physical agg ression. However, as previously stated, researchers have now provided evidence for the display of relational aggression during early childhood (e.g., Crick et al., 1997; Crick et al., 1999; Ostrov et al., 2004; Ostrov,

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27 Pilat, & Crick, 2006; Sebanc, 2003). Yet, the use of relational aggression at this age may not be as advanced as its use at later ages, such as by middle school girls. Overall, though this area of research is fairly new and gaps still remain in the knowledge base, the information provided by these studies has been informative and stands to guide early childhood practices and future research. The present section focuses on exploring the literature in relation to the various correlates of relational and physical aggression. Specific attention is given to examining the research in relation to gender, peer status and friendships, prosocial behavior, and language skills. Gender d ifferences Although research examining gender differences in later childhood and adolescence have been consistent in tha t relational aggression is more prominent among females and physical aggression is more prominent among males, findings regarding gender differences in the display of relational and physical aggression among preschoolers have been somewhat contradictory. T hough many studies have found that preschool girls are more likely to display relational aggression and preschool boys are more likely to display physical aggression, others have not replicated these results. Congruent with traditional relational and physi cal aggression findings, a study by Crick et al. (1997) found that preschool girls were significantly more likely to be relationally aggressive than boys and to exhibit less overt (or physical) aggression than boys. Similarly, a study by Ostrov and Keating (2004) found that girls generally displayed more relational aggression than boys, whereas boys tended to display more physical and verbal aggression than girls. However, other studies have found that preschool boys exhibit as much or more relational aggre ssion than girls. For instance, a study by M cEvoy et al. (2003) found that preschool boys exhibited more acts of both relational

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28 and physical aggression than did preschool girls, though the most common form of aggression among preschool girls was relationa l aggression. In addition, Monks, Smith, and Swettenham (2005) found that four to six year old boys received significantly more nominations for physical aggression, verbal aggression, and rumor spreading than did girl peers. Another study by Juliano et al. (2006) found that gender differences only emerged for levels of physical aggression exhibited by preschool children and not for levels of relational aggression. Overall, the authors found that boys were more likely to be physically aggressive than girls, but that boys were also just as likely to be relationally aggressive as girls. In addition, a study by Harman (2010), found that preschool aged boys were more likely to exhibit physical aggression than their female peers, but that boys and girls did not s ignificantly differ in terms of relational aggression. Thus, discrepancies exist in the literature concerning gender differences in the expression of physical and relational aggression during the preschool or early childhood years, prompting a need for fur ther early childhood aggression research in order to clarify these conflicting findings. In all likelihood, there could be some extraneous factors that are impacting these results and, thereby, creating such discrepancies. In addition to research examining gender differences among preschoolers in the expression of physical and relational aggression, some researchers have examined the gender towards which aggressive acts are more likely to be directed. For instance, a study by Crick et al. (2006) examined re lational and physical aggression among preschoolers aged 30 to 52 months, assessing both the types of aggression exhibited most by each gender and the gender each type of aggression was directed towards more often. Like many other studies, they found that girls were more likely to display

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29 relational aggression than male peers and boys were more likely to display physical aggression than female peers. However, Crick and colleagues also found that preschoolers typically directed their aggressive behaviors at same sex peers (i.e., aggressive boys directed their behaviors towards male peers and aggressive girls directed their behaviors towards female peers). Park et al. (2005) found similar results. Overall, these findings could possibly be explained by the fact that preschool children often play or associate more with same sex peers. Peer status, friendships, and prosocial behavior Other aggression research in early childhood has examined the relationship among relational and physical aggression and prosocial behavior. As such, peer nominated sociometric status groups, which usually include ratings of popular, average, rejected, neglected, and controversial, have been used to assess the influence of aggression on prosocial behavior or interactions. For instance as in studies by Nelson, Robinson, and Hart (2005) and by Crick et al. (1997), preschool aged children are typically presented with pictures of their classmates and asked to identify three individuals they like to play with the most and three individuals they like to play with the child has, he/she will be classified into one of the peer status groups mentioned above. Overall, studies of this nature have indicated that there are, indeed, links between the types of aggression displayed by preschoolers and their classification into a particular peer status group, which replicate the findings of studies examining such peer status at later ages. For instance, Nelson and col leagues found relational aggression to be associated with the controversial sociometric status group, implying that relational

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30 aggression could have potential benefits (i.e., these children were not rejected or neglected). Researchers have also examined th e relationship between relational and physical aggression and the ability to form and sustain friendships. A study by Sebanc (2003) examined how friendship support, friendship conflict, relational and overt aggression, prosocial behavior, and peer acceptan friendship support was positively correlated with prosocial behavior, friendship conflict was positively correlated with overt aggression and peer rejection, and friendship exclusivity/intimacy was positive ly correlated with relational aggression and negatively with peer acceptance. Again, it seems that relational aggression could offer some aggressors tended to have more exclusive and intimate relationships). However, a study by Carpenter and Nangle (2006) looked at the development of social skills in conjunction with displays of both overt and relational aggression. Their findings suggest that relational aggression is a varying with increases in social skills. As social skills decreased, relational aggression also decreased. However, Carpenter and Nangle also found that relational aggression co varied with physical aggression at moderat e and high levels of social skills. In other words, at moderate and high levels of social skills, children with high levels of relational aggression also had high levels of overt aggression and children with low levels of relational aggression exhibited lo w levels of overt aggression. At lower levels of social skills, the relationship between relational and overt aggression did not exist. Overall, relational aggression may serve to replace physical aggression by providing a more

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31 socially appropriate or cons may not fully occur during the preschool years as Carpenter and Nangle posit. As such, the relationships between social skills and aggression and between relational and physical aggression may differ as children get older. Furthermore, it is likely that developments in relational aggression also coincide with cognitive advances, such as those associated with EF and language. Language s kills Research examining the possible relationships between the va rious types of aggression and language skills in early childhood is fairly recent. Estrem (2005) attempted to examine the relationships between language skills, gender, and physical and relational aggression among preschool aged children. When physical agg ression and relational aggression were aggregated into one aggression category, Estrem found that relational and physical aggression tended to increase as language scores decreased. Similar findings were also found by Park and colleagues (2005). When Estre m controlled for physical aggression in order to assess the exact relationship between relational aggression and language ability, she found that relationally relati onally aggressive or physically aggressive peers. Estrem also found that receptive language skills predicted physical aggression more than relational aggression, especially among boys, such that the amount of physical aggression increased as receptive lang uage ability decreased. advances in social skills, and together these advances enable young ch ildren to engage

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32 in relational aggression, a more sophisticated form of aggression. On the other hand, the findings of Carpenter and Nangle (2006) suggest that advances in relational aggression comes with advances in social skills. They found that relation al and physical aggression co vary at moderate and high levels of social skills among young children. In their study, young children with higher levels of relational aggression were more likely to exhibit greater overt aggression, though those with higher levels of overt aggression exhibited varying levels of relational aggression. Overall, as described previously, relational aggression may be a developmental advance in aggression that coincides with advances in expressive language and social skills at youn ger ages, thereby enabling younger children with more advanced social and language skills to engage in an alternate form of aggressive behavior that is less likely to be detected. Because language ability is highly correlated with other cognitive abilitie s, the expression of physical and relational aggression in early childhood may relate to other belief that internalization (i.e., the privatization of speech) is a fun damental component the privatization of speech before they can inhibit immediate responses to environmental events. This internal self talk enables one to develop hy pothetical reasoning and then make a choice based on projected scenarios. In essence, it allows the child to act on his environment rather than simply being acted upon. Overall, given the possible links between advances in expressive language and relationa l aggression, as well as theories regarding a link between the privatization of speech and EF, future research is needed in this area in order to see if correlations between language, EF,

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33 and relational and physical aggression can be established. In additi on, additional research is needed to clarify the relationship between gender and aggression given the mixed results within the literature. Social Information Processing Model This section provides an overview of the social information processing model (SI PM) as proposed by Dodge. A brief history of this theoretical model is provided, as research examining this model in relation to physically aggressive adolescent boys is revi ewed, followed by a review of the literature examining the SIPM in relation to girls and relational aggression. Research attempting to validate the various steps of the model is also briefly reviewed. The present section ends with a discussion of the possi ble relationship between the SIPM and EF abilities and implications for future research in early childhood. Proposed SIPM The SIPM is one of the most widely studied theories for understanding how cognitive processes or operations affect behavior. Overall, this processing model is believed to be representative of the mental or cognitive processes involved in encoding, interpreting, decision making, and responding to a given stimulus. Taking the idea of an attributional bias among aggressive boys fur ther, Dodge (1986) first outlined a SIPM of aggressive behavior in children and proposed a five step model to explain why some boys are more aggressive than others. The processing steps in this initial model were: (a) encoding social cues; (b) interpretati on of social cues; (c) response search; (d) response evaluation; and (e) enactment. In a later article by Dodge and Crick (1990), the steps of this model were reiterated and a review of empirical evidence supporting

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34 the proposed model was provided. Further more, they also explained how skillful processing at each step is related to social competence (vs. deviant social behavior). However, a subsequent revision of the model by Crick and Dodge (1994), which was based on new empirical findings, proposed the fol lowing six steps: (a) encoding cues; (b) interpreting cues; (c) clarifying goals; (d) accessing or constructing responses; (e) deciding on responses; and (f) enacting behaviors (Figure 1 1). This subsequent model. According to Dodge and Rabiner In terms of applying this model to aggressive b ehavior, Dodge (1980) took the initial step when he proposed the existence of a social cognitive bias among overtly aggressive children. In one study, Dodge and Newman (1981) examined the response differences among aggressive boys and non aggressive boys t o ambiguous provocations from a peer and found that aggressive boys tended to make more hostile attributions when interpreting social cues than did non aggressive boys. Subsequent work by Dodge and Frame (1982) demonstrated that: (a) aggressive boys were m ore selective attention alone did not fully explain the hostile attribution biases amon g aggressive boys; and (c) aggressive boys were not more likely to recall hostile cues (vs. benevolent cues) than were nonaggressive boys. Overall, according to Dodge and n

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35 researchers have found support for an attributional bias among aggressive children in relation to such predictor variables as social rejection, media violence, parentin g practices, sociometric status, socioeconomic disadvantage, and mindfulness (e.g., DeWall, Twenge, Gitter, & Baumeister, 2009; Heppner et al., 2008; Moller & Krahe, 2009; Nelson & Coyne, 2009; Rieffe, Villanueva, & Terwogt, 2005; and Schultz & Shaw, 2003 ) The SIPM and Aggression Research In general, the SIPM has been supported in relation to overt and physical aggression (e.g., Graybill & Heuvelman, 1993). Others have also found support for the model in terms of proactive and reactive aggression among bo ys (e.g., Crick & Dodge, 1996). Likewise, Crick (1995) found support for a hostile attributional bias and the SIPM among relationally aggressive children. In addition, studies examining different components of the model have also been largely supportive of proposed steps. For example, a study by Rogers and Tisak (1996) examined second fourth and sixth to unprovoked, intentional aggressive acts in two structured interv iew situations: (a) one in which the child was the intended victim of the aggressive act and (b) one in which the child was a witness to the aggressive act. Overall, Rogers and Tisak found support for the response evaluation component of the SIPM for both situations, although the thoroughness of the response evaluations was influenced by whether or not a witness was perceived to be present and by the age of the child. Thus, these results have implications for the SIPM in terms of how developmental age may i mpact the sophistication of thinking observed at each step in the model, indicating that there may be another underlying factor such as executive functioning or social perception (e.g.,

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36 theory of mind and affect recognition) that impacts the execution of t he SIPM steps. For discriminate between different types of facial expressions was predictive of their social competence. Thus, inappropriate interpretation of facial cu es may lead to inaccurate responses (e.g., aggressive behavior). Overall, research examining the SIPM has focused largely on older, school aged males and on overt/physical a ggression (Crain, Finch, & Foster, 2005). Furthermore, discrepancies exist in the application of this model to relationally aggressive girls and there are some debates concerning the accuracy of the existing steps in the model. For example, two studies rep orted by Crain and colleagues failed to find strong correlations between any of the SIPM processing variables and peer nominations of relationally aggressive behavior in girls. More specifically, they found that hostile attribution biases, social goals, ou tcome expectancies for relational aggression, and likelihood of relational addition, another study by van Nieuwenhuijzen, de Castro, van der Valk, Wijnroks, and Vermeer (2006) failed to find support for the response decision making variables (i.e., those related to evaluation, self efficacy, and the selection of a response) when explaining aggressive behavior among individuals with mild intellectual disabilities (MID). Wh en examining the amount of variance accounted for by the different steps in the model via structural equation modeling, they found that adding the response decision making variables did not significantly explain any more of the variance than when these var iables were excluded from the model. Still, it is important to keep in mind

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37 that this study was examining the SIPM in relation to individuals with MID and no control group consisting of individuals with greater intellectual abilities was established. Howev er, this study could have implications concerning the impact of intellectual ability and executive functioning on the usefulness of the SIPM, especially in relation to early childhood when certain thought processes may or may not have developed or may simp ly be less sophisticated. As such, the SIPM may not be as accurate a model for understanding aggressive behavior during early childhood as it is during adolescence. Thus, overall, there is a need within the literature to apply the SIPM to varying types of aggression exhibited by both males and females at different age levels (while keeping in mind developmental differences). In addition, there is also a need to study the functionality of the SIPM when other intraindividual variables (e.g., executive functio ning or temperament) are considered (Ostrov & Godleski, 2010). In general, such research is needed if the specific steps of the SIPM are to be validated as applied to early childhood and if the SIPM is to be validated as an appropriate model for explaining both relationally and physically aggressive behavior. In turn, such validation will be crucial to researchers wanting to target aggressive behaviors in children by targeting specific steps in the model. Thus, validation of the SIPM could lead to the devel opment of more effective prevention and intervention programs designed to target the thought processes of aggressors (and even those of their victims). Executive Functioning The present section focuses on exploring the construct of EF. The section begins w followed by a review of the proposed hierarchical structure of EF and consensus

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38 rega rding the definition of the EF construct. Next, the literature examining EF abilities among young children is then reviewed, with particular attention on the individual tasks/techniques and norm referenced measures developed to assess EF in young children. The present section concludes with a brief review of the literature examining the relationship between intelligence and specific EF abilities and a discussion of the possible connections between EF and the steps of the SIPM. Defining EF Definitions of ex ecutive functioning (EF) vary across the neuropsychological literature, oftentimes lacking consistency in terminology from one field of research to the next. However, despite this lack of consistency, there is general agreement that EF represents a multifa ceted group of abilities that enable individuals to monitor and control describe those complex cognitive processes or operations that serve to maintain behavioral adapta tion, as well as maintain and direct goal oriented behaviors (Martel et al., 2007; Meltzer, 2007). Thus, the development and promotion of EF abilities is of special concern, particularly in relation to social emotional and academic development as these are areas often negatively impacted by weaknesses in EF abilities (Cooper Kahn & Dietzel, 2008). Neuropsycholo gical perspective The concept of EF arose from work within the field of neuropsychology. As l, within the neuropsychological perspective, three interconnected contexts framed initial EF

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39 research and subsequent definitions of the construct: a) a historical link to prefrontal regions of the brain; b) clinical convenience; and c) developmental model s of cognitive growth (Denckla, 1996). In terms of a historical link to prefrontal regions of the brain, much of the early EF research involved case studies within neuropsychology of individuals with prefrontal cortex damage who displayed deficits in funct ioning related to planning, judgment, and inhibition but not necessarily deficits in other cognitive functions such as language and general intelligence (Zelazo, Qu, Muller, 2005). From this historical link to the prefrontal cortex, one sees the interconne ctedness of this research with clinical convenience, as well as that of developmental models of cognitive growth. Clinicians and researchers were aware that by certain ages individuals should be able to perform certain tasks associated with the prefrontal cortex. As a result of these case studies, clinicians began hypothesizing a construct that ing observed in clients with prefrontal cortex damage (Zelazo et al., 2005). Such a construct served to explain the deficits being observed, as well as provided a way of classifying these observed deficits. Thus, perhaps the most important impetus behind t he proposal of an EF construct was clinical convenience clinicians were looking for ways to clinically classify (and perhaps offer a diagnosis of) patterns of problems associated with prefrontal cortex damage (Denckla, 1996). Overall, as Denckla (1996) in dicates,

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40 Luria, a neuropsychologist, proposed a theoretical model of EF as a hier archical system (Zelazo et al., 2005). As such, prior to the conceptualization of a working model of EF, neuropsychologists began proposing certain aspects of mental functioning or ients with specific prefrontal cortex damage (Zelazo et al., 2005). However, as Denckla (1996) indicates, the distinction of these individual abilities has not been a straightforward process and problems have arisen as a result of the proposed higher order structure of EF abilities, which tends to risk confusing EF with the g of general intelligence. Nevertheless, within the neuropsychology literature and across other theoretical perspectives, such as the information processing, behavioral, and developmenta l approaches to the study of EF, the following specific abilities have been proposed to operate under the umbrella of EF: working memory, attention, inhibition, planning/organization, initiation, shifting, and self regulation. A brief description of these proposed specific EF abilities can be seen in Table 1 1. In addition to the proposal of specific EF abilities, neuropsychologists also make a many deviations among de finitions of EF regarding specific abilities. In general, sorting by color or shape and which are largely associated with the dorsolateral prefrontal cortex (Zelazo et al., 2005). On the other hand, researchers propose that the

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41 regulation problems, which a re largely associated with the ventral and medial regions of aspects of EF may also have implications for aggression research, especially regarding heavily when the f aspects of EF may be relied upon more heavily when the function of aggression is general definitions of EF due to assessment difficulties (Zelazo et al., 2005). Yet, researchers are beginning to make strides in this area, with techniques such as the (Zelazo et al., 2005). Thus, ove rall, the contribution of neuropsychology to the study of EF is tremendous, having coined the term and proposed various specific abilities and working models and spurred similar research in other fields. Much how the study of E F within the field of neuropsychology evolved from clinical presenting with attention deficit/hyperactivity disorder (ADHD) in the 1980s (Denckla, 2007; Meltzer, 2007). Thus, ce perspective on EF is behavioral i nhibition, which he states must precipitate any EF action (Barkley, 2006). Regarding this perspective, Barkley borrows from the work of

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42 Bronowski and Fuster. In essence, Barkley emphasizes the response delay resulting from behavioral inhibition. According to Barkley, such a response delay allows for the occurrence of internalized, self directed actions, which Barkley calls the executive functions (Barkley, 2006). Overall, Barkley emphasizes four executive functions: internalization, reconstitution, separat ion of affect, and prolongation (Barkley, 1996, 2006). With regard to separation of affect, Barkley defines this as the capacity to separate a message from regulation (Barkl ability to prolong the effect of the signal or message by symbolically fixing the event (Barkley, 1996, p. 315). On the other hand, internalization refers to the privatization of speech or what neuropsychologists often refer to as verbal working In essence, Barkley posits that internalization allows one to separate behavior from the proje under the control of plans, goals, and directions (Barkley, 1996). Without such stimul and receptive language differences among relational and physical aggressors, such as those observed by Estrem (2005) and Park and colleagues (2005). Specifically, one

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43 hypoth esis is that increased expressive and receptive language abilities likely result in increased privatization of speech, which in turn allows one to more fully govern his or her behavior. In continuation of his EF theory and borrowing heavily from Bronowski Barkley also posits that as a result of such internalization two additional processes are possible, collectively referred to as reconstitution (Barkley, 1996, 2006). These two additional processes of reconstitution include: a) analysis and b) synthesis ( Barkley, 1996). According to Barkley, analysis is the decomposition of a sensory signal or event into parts, whereas synthesis is the process of manipulating and reconstructing these parts into new messages (Barkley, 1996). Both of these processes can be o bserved in our everyday use of language we analyze verbal messages we receive from our environment into parts of speech and then synthesize this information to form an appropriate response (Barkley, 1996). Thus, essential to the two reconstitution processe s is working memory (i.e., prolongation). In addition, the two processes composing reconstitution may find overlap with SIPMs regarding how social cues are interpreted and subsequent responses selected (i.e., analyzed and synthesized). Overall, however, th ough Barkley distinguishes the four broad EF processes, he also concludes that these processes are interactive and interreliant (Barkley, 2006). Therefore, for Barkley, these four EF abilities serve a common purpose, which is to internalize behavior so as to anticipate change and guide behavior toward an anticipated future outcome (Barkley, 2006). Hierarchical structure of EF As previously mentioned, Luria (1973) was the first to propose a hierarchical resents an interactive functional

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44 system that encompasses the integration of various subsystems laid the foundation for numerous working models of EF, including the hierarchical problem solving framework of EF proposed by Zelazo, Carter, Reznick, and Frye (1997). According to these provide many methods of achieving the same end and are i nherently hierarchical in structure (Zelazo et al., 1997). Thus, according to Zelazo and colleagues, EF can be defined as a complex function with a hierarchical structure composed of various subfunctions that attempt to organize around common outcomes or g oals (Zelazo et al., 2005). For these authors, the goal of EF is purposeful problem solving, and the so can be organized around the constant outcome of solving a p 2005; p. 72). Zelazo and colleagues (1997) suggest the following problem solving flowchart of EF: problem representations planning execution evaluation. As such, when viewed side by ch overlap is apparent. Nevertheless, other researchers have proposed slightly different models of EF. For example, within the field of cognitive psychology, researchers have emphasized information processing models of EF. Within this field, Butterfield an d Albertson (1995) have proposed a model with two levels of mental processes: the cognitive level (i.e., the level pertaining to specific knowledge and strategies) and the metacognitive level (i.e., the level pertaining to understanding and modeling of the cognitive level). These two levels work in tandem, with EF responsible for the monitoring and controlling of all steps

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45 necessary to reach understanding, as well as the manipulation of knowledge and strategies (Borkowski & Burke, 1996). In addition, others in this field have incorporated components of EF as part of their problem solving and planning models in which successive strategies and steps within the model result in goal directed decision making and strategy review (Borkowski & Burke, 1996). Overall, despite the different models of EF proposed by researchers, all of these models have in common the view of EF as a higher order or hierarchical construct, which further emphasizes the contributions of Luria and the field of neuropsychology to the ongoing study of EF. regarding the variables believed to form the hierarchical structure of EF. For instance, studies using structural equation modeling, such as exploratory or confirmatory factor analysis, to examine the structure of EF are rare and have yielded mixed results. A seminal study by Miyake, Friedman, Emerson, Witzki, and Howeter (2000) found evidence of a three factor model of EF, including shifting, inhibition, and updating (i.e., updating and monitoring of working memory), across a sample of undergraduate students. Another study by Lehto, Juujarvi, Kooistra, and Pulkkinen (2003) also found evidence of a three factor model (working memory, shifting, and inhibition) across a sample of children and adolescents. However, a study by Wiebe, Espy, and Charak (2007) found that though a two factor (working memory and inhibition) and three factor (working memory, motor inhibition, and cognitive inhibition) model of EF exhibit ed acceptable fit, these models were not significantly better than a general, one factor model of EF across a preschool sample. Wiebe and colleagues did not include measures of shifting because at the time of the study few measures of shifting had been

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46 dev eloped for preschool ages and the noted measure (i.e. the Dimensional Change confirmatory factor analysis. Thus, it is unclear as to whether or not the inclusion of measure s of shifting would have produced a model more similar to those reported by Miyake and colleagues and Lehto and colleagues. Thus, more research is needed to examine the structure of EF, especially in relation to preschool aged children. Definitional consen sus As can be seen from the multitude of perspectives concerning the study of EF, researchers and clinicians do not always agree on the specific abilities included under the umbrella of EF and sometimes present slightly different definitions of these speci fic abilities. For example, some researchers distinguish attention from the specific EF abilities encompassed under the EF umbrella (e.g., Barkley, 1996), while others include attention among critical aspects of EF (e.g., Denckla, 1996; Meltzer, 2007). In addition, exclude those characteristics of EF related to motivation and affect ( Denckla, 1996). Thus, due to the multitude of perspectives concerning the construct of EF, arriving at a consensus regarding the definition is difficult, evidence of which can be found within the fragmented EF research across multiple fields of study. As B arkley (1996) indicates, EF noted to emerge across the various perspectives and attempts to define EF. According to Barkley (1996), there are four common themes that emer ge across the literature regarding the definition of EF. Firstly, EF is composed of response response chains in which the second response is a function (i.e., is conditional) to a

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47 preceding response and less so to the environmental event. Secondly, EF serv es to change the likelihood of a subsequent response. Thirdly, there are no temporal constraints within the response response chain. In fact, delays in the response chain are often viewed as evidence of EF at work. The fourth and last theme indicated by Ba rkley (1996) is that the initial executive act in the response response chain must be is evident in many of the assessment techniques that attempt to measure inhibitio n in relation to EF, although as Barkley (1996) points out the emphasis on inhibition as a fundamental aspect of EF is often only eluded to in the many models of EF. Providing more specific themes than Barkley, Meltzer (2007) identifies five common elemen ts of most definitions of EF across the varied research perspectives. According to Meltzer (2007), most definitions of EF include the following components: a) goal setting and planning; b) organization of behaviors over time; c) flexibility; d) attention a nd memory systems that guide these processes (e.g., working memory); and e) self regulatory processes such as self monitoring and emotion regulation. Similarly, an informal survey administered at the National Institute of Child Health and Human Development conference in January of 1994 indicated agreement of at least 40% among respondents regarding the following terms associated with EF: a) self regulation, b) sequencing of behavior, c) flexibility, d) response inhibition, e) planning, and f) organization o f behavior (Eslinger, 1996). Thus, although there is no universal definition of EF as Eslinger (1996) indicates, there does exist at least some consensus regarding the aspects encompassed under the EF umbrella. As such, an overarching definition of executi ve functioning as provided by Eslinger (1996) is as follows: EF represents the

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48 psychological processes that are responsible for controlling the implementation of activation inhibition response sequences that are guided by diverse neural representations (e. g., goals, emotions, biological needs, verbal rules, etc.) for the purpose of meeting a balance of immediate situational, short term, and long term future goals that span physical environmental, cognitive, behavioral, emotional, and social spheres. More su ccinctly, as eluded in the introduction, EF is an umbrella term that encompasses those complex cognitive processes or operations that maintain and direct goal oriented behaviors so as to maintain behavioral adaptation (Meltzer, 2007; Martel et al., 2007). aggression when the function of aggression is proactive or instrumental (vs. reactive or hostile). Therefore, one would expect EF abilities to be greater in children who more often use aggression to achieve a goal or reward. Furthermore, given the two overarching types of aggression (i.e., overt/physical and relational) posited by Little and colleagues (2003), relational aggression may actually require greater EF skills to execute than o vert/physical given that researchers have found relational aggression to be a particular developmental advance that coincides with advances in social skills (Carpenter & Nangle, 2006) and language (Estrem, 2005). For example, committing an physically aggre ssive act such as hitting likely involves less inhibition and goal directedness than committing a relationally aggressive act geared toward ostracizing a peer. In general, however, more research is needed to clarify whether or not levels of EF abilities di ffer among children primarily exhibiting physical or relational aggression. EF in Early Childhood Within the field of early childhood, the study of EF has only recently begun to gain attention has

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49 been devoted to the structure, organization, and development of executive functions in infants and preschool conventional assumptions about the development of EF abilities. Given t he characteristics of behaviors typically associated with these early ages, such as a lack of inhibitory control, distractibility, cognitive inflexibility, lack of organization or planning, and lack of self monitoring, it was traditionally assumed that pre schoolers were unable to exert higher order control over their cognition, behavior, and emotions (Isquith et al., 2005). In addition, given the historical connection between EF and frontal lobe functioning, much of the earlier research concerning EF in rel ation to children was associated with looking at executive dysfunction among children presenting with various clinical diagnoses, such as traumatic brain injury, lead exposure, and ADHD (Carlson, 2005). Therefore, far less research has focused on studying EF in relation to typically developing preschool aged children. However, despite the initial lag of EF research in early childhood, important gains have been made. The present section provides a brief overview of the research gains in relation to the study of EF abilities among young children. Crucial to the study of EF among young children, assessment measures have been extended to earlier childhood ages and more researchers have examined the developmental trajectory of EF abilities among preschoolers for various EF tasks (e.g., Carlson, 2005; Espy, Kaufman, McDiarmid, & Glisky, 1999; Hughes, 1998). For example, some researchers have examined the planning performances and inhibitory control among preschoolers. Along these lines, Byrd, van der Veen, McNamar a, and

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50 solutions to problems on an adapted Tower of London task were most goal focused when required to give spoken responses (vs. manual or combined manual spoken responses ) to these problems. This finding has special implications when viewed in the privatization of speech. Having the children speak their responses may mirror the private speech involved in many EF or metacognitive tasks. Still, perhaps the most EF research in early childhood has been conducted in relation to inhibitory control. For instance, a longitudinal study by Berlin, Bohlin, and Rydell (2003) examined the relations hip between inhibition, EF, and ADHD symptomology among children aged five to eight and a half years. These researchers used an adapted Go/No presence of a majority stimulus and to inhibit this response in the presence of a minority stimulus) to measure inhibition. In general, Berlin and colleagues found that inhibition performance at preschool ages is predictive of more general EF and ADHD symptoms at school age. In addition, Carlson and Moses ( 2001) examined the relationship mental states to oneself and others and to understand that others have different views) among preschoolers aged three to four years, fi nding a strong correlation between the two constructs. This finding has implications for studying EF in relation to aggression given that advances in theory of mind imply advances in social skills, the latter having been linked to advances in relational ag gression. Another study by Monks and colleagues (2005) examined the psychological correlates, including EF and theory of mind, of peer victimization among preschoolers.

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51 In relation to EF, these researchers assessed inhibitory control and planning using th e Day/Night task (i.e., an EF task similar to the Go/No go task) and the Tower of London task, respectively. Their findings indicated that defenders performed significantly better on the inhibitory task than did aggressors, though all groups, including the victim group, performed relatively low on EF tasks. In relation to theory of mind, aggressors did not perform significantly different from victims or defenders. However, though Monks and colleagues initially classified aggressors into four types (i.e., ph ysical, social exclusion, rumor spreading, and verbal), their subsequent statistical analyses examining differences between aggressors, victims, and defenders on measures of theory of mind and EF tasks grouped all types of aggressors into an overarching ca tegory of relation to the type of aggressor were not assessed. Nevertheless, the authors concluded that the type of aggression used by young children may be less dependent on theory of mind and EF abilities than that of older children and adolescents who may rely more heavily on indirect forms of aggression. Another study by Park et al. (2005) examined early child and family risk factors of relational and physical aggressio n in middle childhood. Risk factors were assessed during infancy and preschool, whereas aggression was assessed in first, third, and fifth grade. Park and colleagues found that aggression severity (i.e., total of relational and overt aggression) was associ ated with lower language abilities, lower levels of temperamental withdrawal/inhibition, and greater exposure to family environments characterized by maternal negativity toward the child, family negative expressiveness, and maternal depression. The fact th at highly aggressive children exhibited lower levels

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52 of withdraw/inhibition has implications for how executive functioning may be related to youth aggression. However, this study as sessed aggression during middle childhood and not during early childhood l eaving further questions regarding whether or not these results would be replicated in a sample of preschool aged children Overall, due to the gaps in the knowledge base, additional research is needed to further examine the associations among physical and relational aggression and EF skills in young children. Assessment of EF in Early Childhood As previously discussed, research on EF in early childhood is fairly recent, with most of the research in this area occurring in the last decade. This short lifespa n of early childhood EF research has major implications for the assessment of EF at younger ages. Nevertheless, as referenced in the aforementioned studies, researchers/clinicians have developed/refined EF assessment techniques to assess EF skills in young children, sometimes adapting traditional EF assessment tasks to meet the developmental needs of a younger population. In addition, efforts have also been made to extend the ages encompassed by EF assessment batteries and rating scales to below age eight, which is the cutoff age for the Delis Kaplan Executive Function System (D KEFS), a traditional EF assessment battery (Delis, Kaplan, & Kramer, 2001). Overall, such advancements are crucial to furthering the goals of EF research within the field of early ch ildhood, as well as to appropriately and accurately assessing children for clinical and educational purposes. The present section provides a brief review of individual EF tasks/techniques and norm referenced EF measures used in the assessment of EF abiliti es among young children.

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53 Individual EF tasks/t echniques Much like traditional EF assessment techniques, researchers and clinicians have also developed or adapted a wide range of techniques to assess EF abilities in preschool aged children. As such, reviews of these techniques are available in the literature, typically in the form of research studies examining the sensitivity of these tasks or the relationship of specific EF abilities and other early childhood constructs, such as aggression or theory of mind For example, a study by Espy et al., (1999) examined the usefulness of various delayed response formatted tasks in measuring EF among preschool children aged 23 to 66 months. More specifically, Espy and colleagues were interested in determining whether o r not the A not B (AB) task developed by Piaget is a valid measure of working memory and inhibitory control. This was accomplished by comparing participant responses on the AB task to their responses on several other EF tasks, including the self control ta sk (which assesses inhibitory control) and the Delayed Alternation task (which assesses working memory). In general, the traditional AB task has infants or young children observe and retrieve a reward hidden at location A (e.g., under a cup) for several t rials and then the contingency is reversed and the reward is hidden at location B (under a different cup). The self control task involves placing a reward in front of a child (either wrapped as a gift, hidden under a cup, or placed openly in front of the c hild) and then instructing the child not to touch the reward while the examiner completes a separate task with his/her body turned partially away from the child. Similar types of tasks may be referred to as the Snack Delay task, the Gift Delay task, or the Delayed Gratification task. The Delayed Alternation task is similar to the AB task except the child does not observe under which cup the reward is being hidden and the reward is alternately hidden from

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54 one cup to the other in an alternate pattern of trial s. Thus, in order to complete this task successfully, children must be able to remember where the reward was placed during the previous trial. Overall, the study by Espy and colleagues (1999) found the use of the AB task was sensitive to individual and dev elopmental differences in working memory and inhibition processes among children in their sample and, as expected, preschool may be limited to specific ages due to wha t Denckla (1996) refers to content domain competence. This highlights the fact that many EF tasks are age limited due to the typical developmental progression of certain task related skills. For example, a behavior a three year old finds hard to inhibit (e .g., touching a snack or gift in front of them when told to wait) is likely not as difficult for an adolescent or adult to inhibit (unless there is significant executive dysfunction). Along the lines of content domain competence, perhaps the most informa tive research study regarding various early childhood EF tasks is a study conducted by Carlson (2005). This study examined the use of multiple tasks in measuring EF among toddlers and preschoolers and reported findings in relation to age trends in performa nce and task difficulty scales at ages two, three, four, and five/six years of age. The toddler tasks assessed included: Reverse Categorization; Snack Delay; Multilocation Search; Shape Stroop; and the Gift Delay tasks. The preschooler tasks assessed inclu ded: Day/Night; Grass/Snow; Bear/Dragon; Hand Game; Spatial Conflict; Whisper; Tower; Delay of Gratification; Gift Delay; Pinball; Motor Sequencing; Count and Label; Backward Digit Span; Standard Dimensional Change Card Sort; Less is More; Simon Says; Kans as Reflection Impulsivity Scale for Preschoolers; Forbidden Toy; and the

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55 Disappointing Gift tasks. Tasks measuring inhibitory control include the Delay of Gratification task, the Gift Delay task, the Snack Delay task, the Forbidden Toy task, and the Disapp ointing Gift task. These tasks were similar in nature to the self control task used by Espy and colleagues (1999). Other tasks, including the Count and Label task, are reported to measure working memory. However, tasks such as the Grass/Snow task and the S imon Says task are reported to measure a combination of both working memory and inhibitory control. Carlson (2005) provides an overview of each of these tasks, as well as references to the original literature regarding the development of these tasks. In ad dition, Carlson (2005) also provides a rank ordered trajectory of all tasks by results, the least difficult of these tasks among four year olds was the Bear/Dragon t ask and the most difficult task was the Backward Digit Span. However, the Tower task, which involved having children build towers of blocks with the examiner through a succession of turn taking, and the Gift Delay task were both within the moderate range o f difficulty. For five and six year olds, the least difficult task was the Bear/Dragon task, and the most difficult task was the advanced Standard Dimensional Change Card Sort task. The Backward Digit Span task, however, was among the moderately difficult tasks for five and six year olds. In conclusion, the study by Carlson is perhaps the most comprehensive study examining developmentally sensitive measures of EF in early childhood and has the potential to serve as a standard reference for other researche rs passing probabilities for each age group in relation to each task.

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56 Norm referenced EF measures Developmental Neuropsychological Assessment, Second Edition. The Developmenta l Neuropsychological Assessment, Second Edition (NEPSY II) (Korkman, Kirk, & Kemp, 2007), is, as the name implies, a developmental assessment battery of neuropsychological abilities. Like the original NEPSY, the NEPSY II allows for the assessment of childr en as young as three years of age. However, unique to the NEPSY II is that it extends assessment of children and adolescents from age 12 to age 16. In addition, the NEPSY II is the only standardized assessment battery that provides a specific EF assessment domain score for children as young as three years of age in addition to other assessment domain scores, including the language, memory and learning, sensorimotor, social perception, and visuospatial processing domain scores. The Attention/EF domain on the NEPSY II includes the following six subtests: Animal Sorting (ages 7 16); Auditory Attention (ages 5 16) and Response Set (ages 7 16); Clocks (ages 7 16); Design Fluency (ages 5 12); Inhibition (ages 5 16); and Statue (ages 3 6). Overall, the NEPSY II is a widely used neurological assessment battery. Behavior Rating Inventory of Executive Function. The Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al., 2000) is a norm based rating scale intended for ages 5 18 years of age that reported ly assesses EF skills based on parent and teacher report (as well as self report for older ages). There is also a preschool version (the BRIEF P), which is intended for ages 3 5.11 years of age (Gioia, Espy, & Isquith, 2003). Overall, the BRIEF represents one of the first standardized rating scales designed to assess EF in preschool aged to adolescent aged children through an indirect format (i.e., through a rating scale). The BRIEF consists of 86 items, deriving 8 clinical scales (Inhibit, Initiate, Shift, Working Memory, Organization of Materials,

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57 Monitor, Emotional Control, and Plan/Organize) as well as two major index scores (Behavioral Regulation and Metacognition) and a Global Executive Composite (Hooper et al., 2007). In general, the BRIEF attempts to assess specific cognitive abilities through what are believed to be behavioral manifestations of these abilities, which is an underlying assumption of the BRIEF that leads many clinicians and researchers to strongly encourage the use of the BRIEF only in conjunction with other direct measures of EF and cognitive abilities (Meltzer & Krishnan, 2007). EF and Intelligence According to Herbers et al. (2011), EF and intelligence are related but distinct constructs. More specifically, numerous studies have foun d moderate correlations among measures of EF and measures of intelligence (e.g., Blair & Razza, 2007; Carlson, Moses, & Breton, 2002; Herbers et al., 2011). However, Herbers and colleagues also report that associations among performance on intelligence tes ts and EF tests vary according to the different skills being assessed. For example, EF measures that involve updating working memory are more highly related to measures of intellectual test performance than are EF measures of behavioral inhibition and set shifting (e.g., Blair & Razza, 2002; Friedman et al., 2006). In addition, though there is a moderate correlation between measures of EF and measures of intelligence, they each uniquely account for the variance in achievement (Herbers et al., 2011). Overall it appears that there may be some overlap among some of the suggested components of EF and the traditional components of intelligence (namely working memory), though the two also appear to be distinct constructs. Further research is needed to further cla rify the two constructs.

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58 EF and the SIPM Because EF includes the ability to perform goal directed or purposeful behaviors, as well as to regulate emotions, motivations, and other behaviors, several parallels can be drawn between these abilities and the SI PM. For instance, as previously discussed, the SIPM proposed by Crick and Dodge (1994) to explain aggressive behavior involves the following six steps: (1) encoding cues; (2) interpreting cues; (3) clarifying goals; (4) accessing or constructing responses ; (5) deciding on responses; and (6) enacting behaviors (Figure 1 regulate is crucial in appropriately executing each of these six steps. If one cannot delay gratification or has problems in forming plans, then he/she is likely to have problems executing the various processing steps of the SIPM. Furthermore, as steps (3), (4), and (5) relate specifically to decision making (i.e., setting goals and following through with behaviors conducive to such goals), it is logical to assume that these processes may adequately be assessed by specific EF tasks, which are designed to tap into inhibitory control, planning abilities, and other goal directed and consequence predictive thoughts and behaviors. Thus, in relation to EF abilities problems may arise among aggressive children at various steps because of their lower EF abilities and not solely because they hold hostile attribution biases or lack empathy skills (i.e., theory of mind) when making decisions regarding the use of aggress ion. Furthermore, the selection of relational (vs. physical aggression) control, working memory, and planning abilities may find relationally aggressive acts to be a more worthwhile method of achieving their goals than physically aggressive acts because such relationally aggressive acts would decrease chances of being caught due to their more covert nature. Overall, differences in EF abilities may better explain the

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59 selecti on of aggressive behavior over other more socially adaptive behaviors. Thus, further research examining preschoolers performance on various EF tasks in relation to their display of relational and physical aggression would help clarify the relationship betw een EF abilities and the SIPM, as well as provide a better understanding of why some preschoolers are more prone to exhibit specific types of aggression than others. Presently, no study could be found examining the relationship between types of aggression displayed, the SIPM, and EF abilities among young children. Though the study by Monk et al. (2005) looked at the performance of aggressors (vs. victims and defenders) on EF task performance, they did not differentiate aggressors according to type of aggres sion displayed. However, a study by Ellis et al., (2009) examined EF in relation to the type of aggression displayed by older elementary boys, as well as in relation to distortions in appraisal processing. In general, Ellis and colleagues investigated whet her or not EF deficits are related to the display of proactive and reactive aggression, as well as whether or not distortions in appraisal processing (i.e., hostile attributional biases) are related to proactive and reactive aggression. Not surprisingly, t he authors found deficits in response inhibition and planning ability to be related to reactive aggression, but not proactive aggression. In addition, the authors also found that hostile attributional biases moderated the relationship between planning abil ity and proactive and reactive aggression, with the relationship between deficits in planning ability and reactive aggression becoming increasingly positive as levels of hostile attributions increased and the relationship between deficits in planning abili ty and proactive aggression becoming increasingly negative as levels of hostile attributions increased. Hostile attributional biases also moderated the relationship

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60 between response inhibition and reactive aggression, with the relationship between deficits in response inhibition and reactive aggression becoming increasingly positive as levels of hostile attributions increased. In conclusion, these authors stated support for ct with other risk factors such as deficits in EF to predict aggressive behavior and that proactive aggression does not primarily result from cognitive or emotional processing difficulties or EF deficits. Furthermore, to explain the moderating role of host ile attributions in relation to proactive aggression and planning ability, the authors posit that higher levels of hostile attributions increase the planning demands required to be successful at proactive aggression, thus limiting proactive aggression to c hildren with good planning abilities and low hostile attributions. The authors also state support for Zelazo and solving model, which presents a hierarchical problem solving framework of EF, in relation to their findings regarding p lanning ability and reactive aggression. They also state that conceptualizing decision as emotionally based vs. rationally based) in relation to proactive and reactive aggression may prove useful for future research. The r esearch of Ellis and colleagues (2009) makes a great stride toward examining the relationship between EF, the SIPM proposed by Crick and Dodge (1994), and aggression. However, this study also raises additional questions. For one, the sample of this study i ncluded only older elementary aged males. Thus, these findings may or may not hold true for females and children at younger (or older) ages. In addition, these authors classified aggression as either reactive or proactive. However, as Little and colleagues (2003) demonstrated, conceptualizing the types of aggression

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61 as overt/physical and relational may be more accurate because reactive and proactive aggression better describe the function of the behavior rather than the type of behavior. Ellis and colleague s used the Teacher Report questionnaire developed by Dodge and Coie (1987), which categorizes aggressive behaviors as either proactive or reactive physical force in order to do Therefore, it is unclear if the findings of Ellis and colleagues will hold true when classifying aggression as either overt/physical aggression or relational aggression. Nevertheless, there appears to be some overlap between the SIPM and specific models of EF, such as the problem solving model of EF proposed by Zelazo and Muller (2002) and Zelazo et al., (2005). Therefore, questions still remain regarding the relationships among the SIPM, EF, and ph ysical and relational aggression, especially among preschoolers of both genders. In addition, given the nature of young children, the role of hostile attributions may not be as discernable at such young ages. As such, precursors to hostile attributions may include failures in social perception (i.e., failures in perspective taking and/or affect recognition), which subsequently are reinforced as the child ages. Purpose of the Present Study A plethora of research has explored and highlighted the potentially negative consequences of relational and physical aggression for both the victim and the aggressor. In addition, curriculums designed to reduce or prevent aggression have been developed. However, these curriculums have largely been designed to target physic al aggression. In addition, research regarding the study of both relational and physical aggression has largely been focused on older children and adolescents. As such, the

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62 empirical literature examining aggression during early childhood is less extensive than aggression research at other ages and is composed of contradictory findings regarding the relationship between types of aggression and gender. Thus, more research is needed in relation to the study of aggression at younger ages, especially if aggressi on prevention/intervention programs are to be appropriate for addressing both relational and physical aggression. Much like the study of aggression, the study of EF in early childhood has also been fraught with challenges. Traditionally, EF abilities were thought not fully developed until late adolescence or early adulthood, which sorely limited explorations of EF at earlier ages. However, as time has progressed, researchers have come to view EF abilities along a developmental trajectory with evidence of t hese abilities emerging at younger and younger ages. Nevertheless, another challenge to the study of EF during early childhood is the difficulty of measuring EF at such young ages, which has led to a lack of EF assessment tools appropriate for preschool ag ed children. Because of developmental differences in language and other cognitive abilities, certain tasks appropriate for measuring specific EF abilities among older populations are not appropriate among preschool aged populations. As suggested by Denckla (1996), such content domain competence not related to the specific EF ability (e.g., the language ability required to understand the directions of a task) must be considered so as to ensure the accurate measurement of the EF skill. Consideration of such c ontent domain competence has led to the use of different measures of EF for different ages, which may add extraneous variation when comparing scores across ages. However, despite these challenges, EF tasks and assessment batteries appropriate for use among early

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63 childhood populations have been developed/adapted. Yet, correlations among direct assessment measures of EF (e.g., the D KEFS or the NEPSY II) and rating inventories of EF (e.g., the BRIEF or BRIEF P) have not been fully explored. It is unclear whet her or not rating inventories of EF based on teacher/parent observations are actually assessing the same constructs as direct assessment measures of EF. The present research project assesses the relationships among relational and physical aggression, EF abilities, gender, and visual abstract reasoning. The current study also assesses predictors of relational and physical aggression in early childhood. More specifically, the present study examines whether or not the predictors of relational aggression diff er from predictors of physical aggression. In addition, the relationship between a direct assessment measure and a rating inventory of executive functioning behaviors is assessed.

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64 Table 1 1. Proposed EF specific abilities Specific Ability Description Working Memory Bennetto, McAleer, & Roberts, 1996, p. 340) that allows individuals to temporally hold information relevant to a current co ntext in order to produce an adaptive action selection (i.e., provides a mental space for problem solving and decision making). Attention The ability to select or sustain efforts (i.e., remain vigilant) for the continued processing of information (Barkle y, 2006; Sergeant, 1996). Inhibition The ability to inhibit or delay a response to a given stimulus or competing stimulus (Borkowski & Burke, 1996). Planning/Organization The conscious or deliberate specification of a sequence of actions aimed at achi eving a goal (Borkowski & Burke, 1996). Initiation The ability to begin a task or activity or independently generate ideas (Gioia, Isquith, Guy, & Kenworthy, 2000). Shift The ability to begin a task or activity or independently generate ideas (Gioia, I squith, Guy, & Kenworthy, 2000). Self Regulation appropriately in a given situation (Gioia et al., 2000).

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65 Figure 1 1. Six step SIPM as proposed by Crick and Dodge (1994)

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66 CHAPTER 2 METHODS Chapte r 2 provides an overview of the methods of the curren t study. More specifically, Chapter 2 first provides an overview of the methods employed in recruiting participants for the present study, as well as an overview of the descriptive statistics of the pres Procedures for implementing the present study are then discussed, including the recruitment of participants, assessment of participating children, consent and assent procedures, data collection, and compensation. Measures used in the present study, including language, EF, and visual abstract reasoning measures, are then reviewed in detail. Chapter 2 and data analysis techniques. Participants Participants included 101 children ranging in age from 5 years, 0 months to 6 years, 11 months. Participants were recruited from preschools/afterschool and elementary schools in counties surrounding a moderately sized Southeastern college town In addition, all participating preschool/afterschool programs participated in the state based preschool initiative, receiving state funding to provide free voluntary preschool to eligible children and all participating elementary schools re ceived Title I funding Furthermore, 57 participants were enrolled in a rural school district, whereas 23 were enrolled in urban preschool/afterschool programs and 31 were enrolled in an urban university based developmental research school. Gender was even ly distributed (50 males, 51 females), and the sample consisted of mostly Caucasian children ( N = 74), followed by African American ( N = 21), Biracial ( N = 3), Hispanic/Latino ( N = 1),

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67 Asian ( N = 2). The mean participant age was 71.68 months ( SD = 5.09). C hildren receiving medication due to a previous diagnosis of ADHD were excluded from the present study, as were children who were considered English Language Learners (ELLs). All other children who met the above criteria and attended a participating school were invited to participate in the present study. Power analysis was conducted using the techniques recommended by Algina and Olejnik (2003) for computing sample sizes with sufficient power for multiple regression and correlation studies. This technique incorporates a review of the literature regarding previous correlation and regression studies involving the present variables. Given that the relationship between many of the variables included in this study, especially in relation to EF, have not been cit ed within the literature, the strength of such relationships could not be determined from such a review. Thus, it is recognized that the present study is exploratory in nature. Though sufficient power is typically set at .80 as proposed by Cohen (1988a), t he appropriate sample size depends largely on the size of the anticipated relationship between the variables. For the present study, power was deemed sufficient at the .80 level for correlations above r = 0.27. It is recognized that not all significant cor relations in this study have sufficient power at the .80 level (with p = .05), given the exploratory nature of the study. For multiple regression analyses, Green (1991) recommends a minimum sample size of N > 50 + 8 k to achieve sufficient power, where k re presents the number of independent variables. The current sample size ( N = 101) surpasses this suggested minimum sample size as the multiple regression analyses for the two outcome variables of relational and physical aggression included five predictor var iables: gender, age, visual abstract reasoning, language skills, and EF.

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68 Procedure Board, permission to recruit teachers and preschool and elementary students was sought from preschool and elementary school administration. Once permission was gained, teachers were compensated for their participation by receiving a $10 Target or Wal Mart gift certificate for every 5 children for whom they completed the PSBS T and BRIEF rating s cales. The examiner explained the nature of the scales, including the classrooms, informed consent including a description of the study was sent home to parents of each o f the students in participating classrooms. Those students returning the signed informed consent and who met eligibility requirements were included in the study. Assent to participate was gained from children prior to any individual assessments using an ag e appropriate scripted assent. After the recruitment process, teachers were asked to complete the PSBS T and BRIEF and graduate level trained examiners proceeded to administer the four subtests from the Attention/EF domain of the NEPSY II that are appropri ate for children aged 5 years, 0 months to 6 years, 11 months, as well as the Expressive Vocabulary Test, Second Edition (EVT age and gender of each child was recorded on a separate data sheet by the examiner, as well as on the BRIEF and PSBS T rating forms completed by teachers. Measures Aggression and Prosocial Behavior In order to measure relational and physical aggression, the Preschool Social Behavior Scale Teacher Form (PSBS T) (Crick et al. 1997) was given to each teacher

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69 to complete for each child ( Appendix A ). This measure contains 25 items and 6 subscales: relational aggression (8 items), physical aggression (8 items), prosocial behavior (4 items), depressed affect (3 items), same sex ac ceptance (1 item), and opposite sex acceptance (1). Each item on these subscales is rated on a 5 point Likert scale from 1 (never or almost never true) to 5 (always or almost always true). For the purposes of the present investigation, scores from only the relational aggression, physical aggression subscales were used. The PSBS T has been shown to reliably from .83 to .96 on the relational aggression subscale and from .87 t o .94 on the physical aggression subscale (Crick et al., 1997; Crick et al., 1999; Casas et al., 2006). EF Measures Developmental Neuropsychological Assessment, Second Edition The Attention/EF domain from the The Developmental Neuropsychological Assessme nt, Second Edition (NEPSY II) (Korkman, Kirk, & Kemp, 2007) was used to yield a NEPSY II sum of scaled scores and NEPSY II subtest raw scores. This domain consists of the following six subtests: Animal Sorting; Auditory Attention and Response Set (a two pa rt subtest); Clocks; Design Fluency; Inhibition; and Statue. However, for the purpose of the present study, only those subtests appropriate for children aged 5 years, 0 months to 6 years, 11 months were used. As such, the following subtests were administer ed to yield an overall EF sum of scaled scores: Auditory Attention (measures selective auditory attention, maintenance of attention, and shifting); Design Fluency (measures behavioral productivity); Inhibition (measures inhibition of automatic responses), and Statue (measures motor persistence and inhibition).

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70 The NEPSY II was normed across a sample of 1200 children ages 3 to 12 years and was stratified according to the 2003 U.S Bureau of the Census on the basis of age, race/ethnicity, geographic region, a nd parent education level. Gender was evenly distributed. In terms of the psychometric properties of the Attention/EF domain of the NEPSY II, the authors report a wide range of reliability estimates for the various subtests. Internal consistency estimates ranged from .44 to .96 and test retest reliability coefficients ranged from .35 to .88, depending upon the age range and subtest for which the reliability estimate was derived, as well as the type of score derived from the subtest (e.g., total error score vs. scaled score). Furthermore, as the authors of the test point out, reliability estimates for subtests within the Attention/EF domain are the lowest across the entire NEPSY II battery, which is a common finding in the literature in relation to measures o f EF and replicates previous studies of the NEPSY (Hooper, Molnar, Beswick, & Jacobi Vessels, 2007; Korkman et al., 2007). However, interrater agreement across all subtests and domain scores on the NEPSY II were high, ranging from .98 to .99, and decision consistency, which is based on the percentage of cases placed in the same group at first and second testings, was more stable across all age ranges, ranging from .72 to .99. In terms of construct validity, patterns of correlations between the various subte sts of the NEPSY II were examined, with most intercorrelations in the low range (.00 .36) and the highest (.46 .79) occurring between subtests within the same domain. Regarding the Attention/EF domain in particular, intercorrelations ranged from .02 to .7 9, with the highest correlations occurring between the different scores of the Inhibition subtest (i.e., between the Inhibition Naming Combined Score, Inhibition Total Errors,

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71 etc.) and the lowest correlations occurring between the Inhibition Total Errors/ Inhibition Combined Scaled Score and the Statue Total Score. In relation to the latter, though these two subtests (i.e., the Inhibition and Statue subtests) reportedly measure the same construct (i.e., inhibition), the lower correlation may be due to the m otor response component of the Statue subtest. Behavior Rating Inventory of Executive Function The Behavior Rating Inventory of Executive Function (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000) is a norm based rating scale intended for ages 5 18 years o f age that reportedly assesses EF skills based on parent and teacher report (as well as self report for older ages). The BRIEF consists of 86 items, deriving 8 clinical scales (Inhibit, Initiate, Shift, Working Memory, Organization of Materials, Monitor, E motional Control, and Plan/Organize) as well as two major index scores (Behavioral Regulation and Metacognition) and a Global Executive Composite. The Inhibition, Shift, and Emotional Control subscales derive the Behavioral Regulation Index score, whereas the Initiate, Working Memory, Plan/Organization, Organization of Materials, and Monitor subscales derive the Metacognition Index score. The authors report relatively high internal consistency reliability (i.e., .80 to .98) and test retest reliability (i.e. .88 for the teacher form) estimates. However, interrater reliability estimates across parent and teacher forms were lower, ranging from .32 to .34. The authors of the BRIEF also report evidence of convergent and discriminant validity through comparisons with other rating scales, such as the Child Behavior Checklist and the Behavior Assessment System for Children. Evidence of construct validity was provided through exploratory factor analytic studies that supported a two factor model of EF. According to Oz onoff and Schetter

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72 (2007), the BRIEF is believed to have more ecological validity than many other measures of EF. Language Measure : Expressive Vocabulary Test, Second Edition (EVT 2) The Expressive Vocabulary Test, Second Edition (EVT 2 ; Williams, 2007 ) i s a norm referenced measure of expressive vocabulary and word retrieval. The EVT requires individuals to answer questions related to pictures or parts of pictures to which the examiner points. Administration time is estimated to be between 10 to 20 minutes EVT 2 age based norms were established across a sample of 3,540 individuals ranging in age from 2 years, 6 months to 90+ years and was co normed with the Peabody Picture Vocabulary Test, Fourth Edition (PPVT IV) The standardization sample was stratified to match U.S. Census data according to sex, race/ethnicity, geographic re gion, socioeconomic status and clinical diagnosis or special education placement. According to the test publisher, internal consistency reliability estimates for the EVT 2 ranged fr om .93 to .94, whereas alternate form reliability was .87 and test retest reliability was .95. To establish construct validity, the EVT 2 was correlated with the Expressive Vocabulary Test (EVT), Comprehensive Asse ssment of Spoken Language Clinical Evalua tion of Language Funda mentals, Fourth Edition Group Reading Assessment a nd Diagnostic Evaluation and PPVT 4 instruments, and new items were chosen based on a review of over 9 published reference works. The correlation between the EVT 2 and the PPVT IV wa s 82. Visual Abstract Reasoning Measure : (CPM)

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73 Progressi ve Matrices were originally developed in 1936 and numerous normative studies have been conducted (Sattler, 2001). The CPM is a 36 item test designed for children ages five to eleven years and takes about 10 to 15 minutes to administer. In this form of Rave attention of younger children (Sattler, 2001). Raw scores on the CPM are converted into percentile ranks; however, for the purpose of the present study, only raw scores were considered. Although stratified random sampling procedures were not used to calculate U. S. norms, Sattler (2001) suggests that the norms are likely representative of the school age population given the large samples obtained in various school districts. Split half re liabilities ranged from .65 to .94 for the CPM, and test retest reliabilities range ive Matrices measure one intelligence factor: induction or reasoning. However, as cited in Sattler (2001), Carlson and Jensen (1980) indicate that performance on the CPM in particular is best accounted for by a three factor model of intelligence: closure a nd abstract reasoning by analogy, pattern completion through identity and closure, and Progressive Matrices and other measures of intelligence range from the .50s to the .80s academic achievement (Sattler, 2001). Research Questions Overall, the following research questions will be addressed by the current study: 1. Are relational and physica l aggression significantly correlated with EF among young children while controlling for age?

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74 2. Are relational and physical aggression significantly correlated with gender among young children while controlling for age? 3. Are relational and physical aggressi on significantly correlated among young children while controlling for age? 4. Are EF and language skills significantly correlated among young children while controlling for age? 5. Are EF and visual abstract reasoning significantly correlated among young childr en while controlling for age? 6. What is the relationship between relational aggression and the following predictor variables: gender, age, EF, language skills, and visual abstract reasoning? 7. What is the relationship between physical aggression and the follow ing predictor variables: gender, age, EF, language skills, and visual abstract reasoning? 8. What is the relationship between a direct assessment measure of EF (i.e., the NEPSY II Attention/Executive Function domain) and a rating inventory of EF (i.e., the BR IEF) while controlling for age? Data Analysis Descriptive Statistics Variables were entered into SPSS 19.0 to yield descriptive statistics for gender, age, ethnicity, NEPSY II sum of scaled scores, NEPSY II subtest raw scores, BRIEF total raw scores, BRIE F subscale/domain raw scores, EVT 2 total raw scores, CPM total raw scores, PSBS T relational aggression raw scores, and PSBS T physical aggression raw scores. Correlational Analyses The relationship between age and ethnicity, NEPSY II sum of scaled score s, NEPSY II subtest raw scores, BRIEF total raw scores, BRIEF subscale/domain raw scores, EVT 2 total raw scores, CPM total raw scores, PSBS T relational aggression raw scores, and PSBS T physical aggression raw scores were assessed using Pearson product m oment correlations. The relationship between age and gender was assessed

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75 using a point biserial correlation. Partial correlations controlling for age were then conducted to assess the relationship between gender, ethnicity, NEPSY II sum of scaled scores, N EPSY II subtest raw scores, BRIEF total raw scores, BRIEF subscale/domain raw scores, EVT 2 total raw scores, CPM total raw scores, PSBS T relational aggression raw scores, and PSBS T physical aggression raw scores. Gender was treated as a categorical vari able. Comparison of Means Independent sample t tests were conducted to further examine the relationship between gender and PSBS T relational aggression raw scores and PSBS T physical aggression raw scores. Multiple Regression Analyses SPSS 19.0 was used to assess multiple regression models. Specifically, hierarchical multiple regression was used to assess the extent to which each variable, including gender, age, NEPSY II EF sum of scaled scores, EVT 2 total raw scores, and CPM total raw scores significan tly predicted levels of (a) relational aggression and (b) physical aggression while controlling for all other variables in the model. This procedure allows for the control of other variables in the model so that the amount of unique variance contributed to the dependent variable by each independent variable of interest may be assessed without the added effects of other independent variables in the model. Age was included in each model to control for its influence on the variance attributed by each variable of interest given the significant correlations between age and many of the independent variables of interest and the use of raw scores (Table 3 6). Findings are interpreted in relation to the social information processing model of aggression initially pro posed by Dodge (1986) and further elaborated by Crick and

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76 Dodge (1994) and Crick and Dodge (1996), as well as in relation to current theories of executive functioning, especially those proposed by Barkley (1996).

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77 CHAPTER 3 RESULTS The purpose of the pre sent study was to examine whether or not gender, language, visual abstract reasoning, and executive functioning skills significantly predict relational and physical aggression, as well as the relationship among these variables. In addition, the present stu dy also sought to examine the relationship between a direct assessment measure of executive functioning and a rating scale of executive functioning. Chapter 3 first provides an overview of the descriptive statistics related to demographic variables, inclu ding gender, age, ethnicity, as well as outcome and predictor variables of interest and BRIEF total and subcale/domain raw scores. Correlations among demographic variables, EVT 2 raw scores, CPM raw scores, NEPSY II sum of scaled scores and subtest raw sco res, and BRIEF total and subscale/domain raw scores are presented next. Hierarchical multiple regression analyses assessing the amount of unique variance in the outcome variable accounted for by each predictor variable while controlling for all other varia bles of interest are then presented. Descriptive Statistics Table 3 1 presents the descriptive statistics for outcome variables measured in this study, including the PSBS T relational aggression raw scores and the PSBS T physical aggression raw scores. Th e mean score for PSBS T relational aggression raw scores was somewhat higher (M = 14.60, SD = 5.91) than the mean score for PSBS T physical aggression raw scores (M = 11.62, SD = 5.23). Because the relational and physical aggression subscales on the PSBS T include eight items scored on a Likert

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78 scale, scores can range from eight to 40. Higher scores indicate greater levels of relational or physical aggression. PSBS T relational aggression raw scores from the present study ranged from 8 to 33. PSBS T physica l aggression raw scores ranged from 8 to 32. On the PSBS T relational aggression subscale, 87.13% of participants obtained raw scores of 20 or below. On the PSBS T physical aggression subscale, 91.09% of participants obtained raw scores of 20 or below. Giv en that the range of scores for the PSBS T relational and physical aggression subscales is eight to 40, the majority of scores represented in the present sample are below the median score of 24 and, therefore, may not adequately represent more significant levels of relational and physical aggression. However, Carpenter, Shepherd, and Nangle (2008) reported a mean raw score below eight for the PSBS T relational aggression subscale, and Harman (2010) reported a mean raw score of 11.6 for the PSBS T relational aggression subscale and a mean raw score of 8.35 for the PSBS T physical aggression subscale. As such, the means for these two subscales in the present study are higher than those reported in previous studies. Table 3 2 presents the descriptive statistics for dependent and independent variables by gender. Table 3 3 presents the descriptive statistics for the predictor variables measured in this study, including gender, age, CPM raw scores, EVT 2 raw scores, and NEPSY II sum of scaled scores. Gender was eve nly distributed, with 52 females and 49 males. Participants ranged in age from 60 months to 83 months, with a mean age of 71.68 months. The mean EVT 2 score was 80.99 (SD = 13.61) and ranged from 47 to 112. Higher scores on the EVT 2 indicate greater expre ssive language skills. The mean CPM score was 17.48 (SD = 4.32), with a range from 6 to 28. Higher scores

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79 on the CPM indicate greater visual abstract reasoning abilities. The mean BRIEF total raw score was 104.08 (SD = 29.74), and the mean NEPSY II sum of scaled scores was 44.65 (SD = 9.18). The BRIEF total raw scores ranged from 73 to 240, with higher scores indicating poorer executive functioning skills. The NEPSY II sum of scaled scores ranged from 22 to 70, with higher scores indicating greater executiv e functioning skills. The NEPSY II scaled score subtest means were mostly within the average range, with only the Inhibition Naming scaled score mean falling slightly below the scaled s core range of eight to ten ( Table 3 4). Table 3 5 presents the descrip tive statistics of the NEPSY II sum of scaled scores and subtest raw scores. The mean NEPSY II sum of scaled scores was 44.65 (SD = 9.18), with a range from 22 to 70. The mean Auditory Attention total (AAT) raw score was 23.77 (SD = 3.99), with a range fro m 12 to 30. Higher scores on the AAT indicate greater selective auditory attention and vigilance. The mean Auditory Attention error (AAE) raw score was 12.80 (SD = 8.98), with a range from 1 to 39. Higher scores on the AAE indicate poorer auditory attentio n, vigilance, and inhibition. The mean Design Fluency total (DFT) raw score was 12.15 (SD = 4.45), with a range from 4 to 26. Higher DFT raw scores indicate greater behavioral productivity. The mean Inhibition Naming time raw score (INT) was 81.46 seconds (SD = 20.39), with a range from 49 to 154. The mean Inhibition Inhibition time (IIT) raw score was 122.16 (SD = 30.37), with a range from 65 to 212. Greater time on the Inhibition subtest conditions indicate slower inhibitory processing of competing stimul i. The mean Inhibition Naming error (INE) raw score was 6.44 (SD = 4.72), with a range from 0 to 27. The mean Inhibition Inhibition error (IIE) raw score was 16.73 (SD = 10.21), with a range from 1 to 45. Greater errors

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80 on the Inhibition subtest conditions indicate poorer inhibition. The mean Statue total (ST) raw score was 21.95 (SD = 5.37), with a range from 8 to 30. Greater scores on the Statu e subtest indicate greater motor persistence and inhibition of automatic responses and the ability to switch betw een response types. Table 3 6 presents the descriptive statistics of the BRIEF total and subscale raw scores. The mean BRIEF total raw score was 104.08 (SD = 29.74), with a range from 73 to 240. The mean Inhibition (INH) subscale raw score was 14.93 (SD = 5.56), with a range from 10 to 30. Higher scores on the Inhibition subscale indicate poorer control of impulses. The mean Shift (S) subscale raw score was 13.15 (SD = 3.62), with a range from 10 to 24. Higher scores on the Shift subscale indicate greater d ifficulty shifting from one situation to another. The mean Emotional Control (EC) subscale raw score was 12.38 (SD = 4.02), with a range from 9 to 27. Higher scores on the Emotional Control subscale indicate greater difficulty modulating emotional response s appropriately. The mean Initiate (INI) subscale raw score was 9.99 (SD = 3.41), with a range from 7 to 21. Higher scores on the Initiate subscale indicate greater difficulty beginning tasks and generating ideas. The mean Working Memory (WM) subscale raw score was 14.68 (SD = 4.92), with a range from 6 to 30. Higher scores on the Working Memory subscale task. The mean Plan/Organize (PO) subscale raw score was 13.78 ( SD = 4.45), with a range from 10 to 30. Higher scores on the Plan/Organize subscale indicate greater difficulty with anticipating future events, developing steps, setting goals, and grasping main ideas. The mean Organization of Materials (OM) subscale raw score was 9.46 (SD = 3.20), with a range from 7 to 21. Higher scores on the Organization of Materials

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81 subscale indicate greater difficulty organizing materials in a meaningful and productive manner so as to assist one in completing tasks. The mean Monitor (M) subscale raw score was 15.30 (SD = 4.71), with a range from 10 to 30. Higher scores on the Monitor subscale indicate greater difficulty checking work and assessing self performance. Correlation Analyses Correlations among age and ethnicity and outcom e and predictor variables are depicted in Table 3 7. Partial correlations among outcome and predictor variables while controlling for age are depicted in Table 3 8. Ethnicity was not significantly correlated with any outcome or predictor variable. Gender w as weakly but significantly correlated with age (r = 0.25, p < .05), as well as with PSBS T physical aggression raw scores (r = 0.23, p < .05) and PSBS T relational aggression raw scores (r = 0.20, p < .05) while controlling for age. Age was weakly but si gnificantly correlated with CPM total raw scores (r = 0.22, p < .05) and with EVT 2 total raw scores (r = 0.22, p < .05), which is expected among raw scores which do not control for age given typical developmental gains in language and reasoning abilities that occur among young children with increases in age. In addition to being weakly correlated with age, CPM total raw scores were also moderately and significantly correlated with EVT 2 total raw scores (r = 0.43, p < .01) and NEPSY II sum of scaled scores (r = 0.39, p < .01) when controlling for age. EVT 2 total raw scores were also moderately and significantly correlated with NEPSY II sum of scaled scores (0.33, p < .01) when controlling for age. NEPSY II sum of scaled scores were also weakly but signific antly correlated with PSBS T physical aggression raw scores (r = 0.23, p < .05) when controlling for age. PSBS T relational aggression raw scores were strongly and significantly correlated with PSBS T physical aggression raw scores (r = 0.62, p < .01) whe n controlling for age.

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82 Table 3 9 depicts the partial correlations among the NEPSY II subtests and PSBS T relational and physical aggression raw scores while controlling for age. Among the NEPSY II subtest raw scores, the Statue subtest total raw score was weakly but significantly correlated with PSBS T physical aggression raw scores (r = 0.25, p < .01). The NEPSY II Auditory Attention total raw score was weakly but significantly correlated with PSBS T relational aggression raw scores (r = 0.22, p < .05). Table 3 10 depicts the partial correlations among the NEPSY II subtests and EVT 2 total raw scores while controlling for age. The EVT 2 was moderately and significantly correlated with the NEPSY II Auditory Attention errors raw score (r = 0.30, p < .01) the Inhibition Naming time (r = 0.31, p < .01), the Inhibition Naming errors raw score (r = 0.29, p < .01), and the Inhibition Inhibition errors raw score (r = 0.38, p < .01), and was weakly but significantly correlated with the Statue total raw score (r = 0.20, p < .05). Table 3 11 depicts the partial correlations among the NEPSY II subtests and the CPM total raw scores while controlling for age. The CPM was moderately and significantly correlated with the NEPSY II Inhibition Naming time (r = 0.40, p < .01) and weakly but significantly correlated with the Inhibition Naming errors raw score (r = 0.27, p < .01), Inhibition Inhibition time (r = 0.29, p < .01), Design Fluency total raw score (r = 0.21, p < .05), Inhibition Inhibition errors (r = 0.24, p < .05), and Statue (r = 0.25, p < .01). No significant correlation was found between CPM total raw scores and the NEPSY II Auditory Attention total raw score or Auditory Attention errors. Partial correlations among NEPSY II sum of scaled scores and subt est raw scores and BRIEF total and subscale raw scores while controlling for age are depicted in Table 3 12. The NEPSY II sum of scaled scores was weakly but significantly correlated with

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83 the BRIEF total raw score (r = 0.26, p < .01). The Auditory Attenti on total, Auditory Attention errors, and Inhibition Inhibition time raw scores from the NEPSY II were not significantly correlated with any of the BRIEF subscale raw scores or with the BRIEF total raw score. Furthermore, the BRIEF Inhibition, Shift, and Em otional Control subscales were not significantly correlated with any of the NEPSY II individual subtests. The NEPSY II Design Fluency total raw score was weakly but significantly correlated with the Plan/Organization subscale from the BRIEF (r = 0.22, p < .05). The Inhibition Naming time raw score from the NEPSY II was weakly but significantly correlated with the Initiate subscale raw score from the BRIEF (r = 0.20, p < .05). The NEPSY II Statue subtest raw score was correlated weakly but significantly wit h the BRIEF Organization of Materials subscale raw score (r = 0.25, p < .05). The NEPSY II Inhibition Naming error raw score was moderately and significantly correlated with the BRIEF Working Memory subscale raw score (r = 0.31, p < .01), the Plan/Organiz e subscale raw score (r = 0.34, p < .01), and the Organization of Materials subscale raw score (r = 0.32, p < .01) and weakly but significantly correlated with the BRIEF Initiate subscale raw score (r = 0.26, p < .01) and Monitor subscale raw score (r = 0. 21, p < .05). The NEPSY II Inhibition Naming error raw score was also weakly but significantly correlated with the BRIEF total raw score (r = 0.22, p < .05). The NEPSY II Inhibition Inhibition error raw score was moderately and significantly correlated wit h the BRIEF Plan/Organize subscale raw score (r = 0.33, p < .01) and Working Memory subscale raw score (r = 0.30, p < .01), as well as weakly but significantly correlated with the BRIEF Initiate subscale raw score (r = 0.28, p < .01), Organization of Mater ials subscale raw score (r = 0.29, p < .01), and Monitor subscale raw score (r = 0.25, p < .05). The NEPSY II

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84 Inhibition Inhibition error raw score was also weakly but significantly correlated with the BRIEF total raw score (r = 0.27, p < .01). The NEPSY I I sum of scaled scores was moderately and significantly correlated with the BRIEF Working Memory subscale raw score (r = 0.30, p < .01) and Organization of Materials subscale raw score (r = 0.31, p < .01) and weakly but significantly correlated with the BRIEF Shift subscale raw score (r = 0.20, p < .05), Initiate subscale raw score (r = 0.29, p < .01), Plan/Organization subscale raw score (r = 0.28, p < .01), and Monitor subscale raw score (r = 0.20, p < .05). Comparison of Means Independent samples t tests, which are depicted in Table 3 13, were conducted to further examine the relationship between gender and PSBS T relational and physical aggression raw scores. In relation to PSBS T relational aggression raw scores, male and females did not differ s ignificantly in terms of PSBS T relational aggression raw scores, t (99) = 1.65, p = .10. However, given the p value of .10, there was a trend in which females (M = 15.54) were rated as exhibiting slightly more relational aggression than males (M = 13.61) In relation to PSBS T physical aggression raw scores, males (M = 12.96) were rated as exhibiting significantly more physical aggression than females (M = 10.37), t (99) = 2.56, p < .01. Multiple Regression Analyses Hierarchical multiple regression analy ses were conducted to examine the amount of variance in the physical aggression and relational aggression outcome variables accounted for by each predictor variable while controlling for all other predictor variables of interest. Age was included in each m odel to account for any influence of age on the prediction of the outcome variables. In relation to relational aggression, hierarchical

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85 multiple regression analyses were conducted to examine (a) whether NEPSY II sum of scaled scores predict a significant p ortion of the variance in PSBS T relational aggression raw scores while controlling for gender, age, CPM total raw scores, and EVT 2 total raw scores; (b) whether gender predicts a significant portion of the variance in PSBS T relational aggression raw sco res while controlling for age, NEPSY II sum of scaled scores, CPM total raw scores, and EVT 2 total raw scores; (c) whether CPM total raw scores predict a significant portion of the variance in PSBS T relational aggression raw scores while controlling for age, NEPSY II sum of scaled scores, gender, and EVT 2 total raw scores, and (d) whether EVT 2 total raw scores predict a significant portion of the variance in PSBS T relational aggression raw scores while controlling for age, NEPSY II sum of scaled scores gender, and CPM total raw scores. Hierarchical multiple regression analyses were also conducted in relation to PSBS T physical aggression raw scores and the above predictor variables. Specifically, hierarchical multiple regression analyses were conducted to examine (a) whether NEPSY II sum of scaled scores predict a significant portion of the variance in PSBS T physical aggression raw scores while controlling for gender, age, CPM total raw scores, and EVT 2 total raw scores; (b) whether gender predicts a significant portion of the variance in PSBS T physical aggression raw scores while controlling for age, NEPSY II sum of scaled scores, CPM total raw scores, and EVT 2 total raw scores; (c) whether CPM total raw scores predict a significant portion of the v ariance in PSBS T physical aggression raw scores while controlling for age, NEPSY II sum of scaled scores, gender, and EVT 2 total raw scores, and (d) whether EVT 2 total raw scores predict a

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86 significant portion of the variance in PSBS T physical aggressio n raw scores while controlling for age, NEPSY II sum of scaled scores, gender, and CPM total raw scores. Relational Aggression Tables 3 14 through 3 18 display the results of the hierarchical multiple regression analyses for PSBS T relational aggression r aw scores. The amount of variance in PSBS 0.05, p < .05). Overall, the full model, which included NEPSY II sum of scaled scores, gender, CPM total raw scores, EVT 2 total raw sco res, and age, did not predict a significant portion of variance in PSBS T relational aggression raw scores (R2 = 0.09, Adjusted R2 = 0.04, F (5, 100) = 1.80, p > .05). Physical Aggression Tables 3 19 through 3 23 display the results of the hierarchical m ultiple regression analyses for PSBS T physical aggression raw scores. NEPSY II sum of scaled scores predicted a small but significant portion of variance in PSBS T physical aggression raw raw scores did not pred ict a significant portion of the variance in PSBS T physical NEPSY II sum of scaled scor es, gender, CPM total raw scores, EVT 2 total raw scores, and age, predicted a significant portion of variance in PSBS T physical aggression raw scores (R2 = 0.11, Adjusted R2 = 0.07, F (5, 100) = 2.45, p < .05), although CPM total raw scores, EVT 2 total raw scores, and age did not contribute significantly to the model.

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87 Table 3 1. Descriptive statistics for outcome variables: PSBS T relational aggression raw scores (RA) and PSBS T physical aggression raw scores (PA) (N = 101) Variable Mean SD Range RA 1 4.60 5.91 8 33 PA 11.62 5.23 8 32 Table 3 2. Descriptive statistics for dependent and independent variables by gender (N = 101) Variable Male Female RA 13.61 15.54 (5.37) (6.29) PA 12.96 10.37 (6.03) (4.01) Age 70.39 72.90 (4.81) (5.08) CPM T otal RS 17.06 17.87 (3.78) (4.78) EVT 2 Total RS 80.20 81.73 (13.48) (13.81) NEPSY II Sum of SS 43.55 45.69 (8.33) (9.89) Note. Standard deviations are reported below means in parentheses; RA = PSBS T relational aggression raw scores; PA = PSBS T physical aggression raw scores; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores. Table 3 3. Descriptive statistics for predictor variables: Gender, CPM total raw score, EVT 2 total raw score, and NEPSY II sum of scaled scores (N = 101) Variable Mean SD Range Gender 0.51 0.50 ---------Age 71.68 5.09 60 83 CPM Total RS 17.48 4.32 6 28 EVT 2 Total RS 80.99 13.61 47 112 NEPSY II Sum of SS 44.65 9.18 22 70 Note. CPM Tot al RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores.

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88 Table 3 4. NEPSY II subtest scaled score averages. NEPSY II Subtest Mean Scaled Score Auditory Attention 10.32 Design Fluen cy 8.85 Inhibition Naming 8.60 Inhibition Inhibition 7.97 Statue 8.91 Table 3 5. Descriptive statistics for NEPSY II sum of scaled scores and subtest raw scores (N = 101) Variable Mean SD Range AAT 23.77 3.99 12 30 AAE 12.80 8.98 1 39 DFT 12.15 4. 45 4 26 INT 81.46 20.39 49 154 INE 6.44 4.72 0 27 IIT 122.16 30.37 65 212 IIE 16.73 10.21 1 45 ST 21.95 5.37 8 30 NEPSY II Sum of SS 44.65 9.18 22 70 Note. AAT = Auditory Attention total; AAE = Autidotyr Attention errors; DFT = Design Fluency total; INT = Inhibition Naming time; INE = Inhibition Naming errors; IIT = Inhibition Inhibition time; IIE = Inhibition Inhibition errors; ST = Statue total; NEPSY II Sum of SS = NEPSY II sum of scaled scores. Table 3 6. Descriptive statistics for BRIEF total and subscale raw scores (N = 101) Variable Mean SD Range INH 14.93 5.56 10 30 S 13.15 3.62 10 24 EC 12.38 4.02 9 27 INI 9.99 3.41 7 21 WM 14.68 4.92 6 30 PO 13.78 4.45 10 30 OM 9.46 3.20 7 21 M 15.30 4.71 10 30 BRIEF Total RS 104.08 29.74 73 240 Note. INH = Inhibition; S = Shift; EC = Emotional Control; WM = Working Memory; PO = Plan/Organization; OM = Organization of Materials; M = Monitor.

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89 Table 3 7. Pearson product moment and point biserial correlations between age and ethnicity and out come and predictor variables (N = 101) Variable Age Gender 0.25* ETH 0.00 CPM Total RS 0.22* EVT 2 Total RS 0.22* NEPSY II Sum of SS 0.17 RA 0.13 PA 0.13 Note. *p < .05; ETH = ethnicity; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores; RA = PSBS T relational aggression raw scores; PA = PSBS T physical aggression raw scores. Table 3 8. Partial correlations among outcome and predictor variables and the BRIEF total raw s core controlling for age (N = 101) Gender ETH CPM Total RS EVT 2 Total RS NEPSY II Sum of SS BRIEF Total RS RA PA Gender 1.00 0.04 0.04 0.00 0.08 0.20* 0.20* 0.23* ETH 1.00 0.04 0.08 0.13 0.12 0.01 0.08 CPM Total RS 1.00 0.43** 0.39** 0.21* 0.12 0.07 EVT 2 Total RS 1.00 0.33** 0.35** 0.00 0.02 NEPSY II Sum of SS 1.00 0.26** 0.11 0.23* BRIEF Total RS 1.00 0.31** 0.56** RA 1.00 0.62** PA 1.00 Note. *p < .05, **p < .01; ETH = ethnicity; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores; BRIEF Total RS = BREIF total raw score; RA = PSBS T relational aggression raw scores; PA = PSBS T physical aggression raw scores.

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90 Table 3 9. Partial correlations among NEPSY II subtest raw score and PSBS T relational aggression and physical aggression raw scores controlling for age (N = 101) NEPSY II Subtest Scores RA PA AAT 0.22* 0.13 AAE 0.10 0.08 DFT 0.14 0.11 INT 0.12 0.05 I NE 0.09 0.07 IIT 0.11 0.07 IIE 0.01 0.17 ST 0.10 0.25** Note. *p < .05, **p < .01; AAT = Auditory Attention total; AAE = Auditory Attention errors; DFT = Design Fluency total; INT = Inhibition Naming time; INE = Inhibition Naming errors; IIT = Inhib ition Inhibition time; IIE = Inhibition Inhibition errors; ST = Statue total; NEPSY II Sum of SS = NEPSY II sum of scaled scores; RA = PSBS T relational aggression raw scores; PA = PSBS T physical aggression raw scores. Table 3 10. Partial correlations a mong NEPSY II subtest raw scores and EVT 2 total raw scores controlling for age (N= 101) NEPSY II Subtest Scores EVT 2 Total RS AAT 0.14 AAE 0.30** DFT 0.09 INT 0.31** INE 0.29** IIT 0.18 IIE 0.38** ST 0.20* Note. *p < .05, **p < .01; AAT = A uditory Attention total; AAE = Auditory Attention errors; DFT = Design Fluency total; INT = Inhibition Naming time; INE = Inhibition Naming errors; IIT = Inhibition Inhibition time; IIE = Inhibition Inhibition errors; ST = Statue total; EVT 2 Total RS = EV T total raw score.

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91 Table 3 11. Partial correlations among NEPSY II subtest raw scores and CPM total raw scores controlling for age (N = 101) NEPSY II Subtest Scores CPM Total RS AAT 0.11 AAE 0.12 DFT 0.21* INT 0.40** INE 0.27** IIT 0 .29** IIE 0.24** ST 0.25** Note. *p < .05, **p < .01; AAT = Auditory Attention total; AAE = Auditory Attention errors; DFT = Design Fluency total; INT = Inhibition Naming time; INE = Inhibition Naming errors; IIT = Inhibition Inhibition time; IIE = Inh ibition Inhibition errors; ST = Statue total; CPM Total RS = CPM total raw score. Table 3 12. Partial correlations among NEPSY II sum of scaled scores and subtest raw scores and BRIEF total raw score and subscale/domain raw scores controlling for age (N = 101) NEPSY II Scores INH S EC INI WM PO OM M BRIEF Total RS AAE 0.08 0.05 0.08 0.04 0.12 0.06 0.10 0.06 0.08 DFT 0.12 0.16 0.04 0.16 0.18 0.22* 0.17 0.15 0.19 INT 0.13 0.07 0.09 0.20* 0.16 0.14 0.15 0.02 0.06 INE 0.06 0.15 0.05 0.26** 0.31** 0.34** 0.32** 0.21* 0.22* IIT 0.06 0.12 0.05 0.19 0.10 0.11 0.10 0.06 0.07 IIE 0.14 0.17 0.13 0.28** 0.30** 0.33** 0.29** 0.25* 0.27** ST 0.13 0.06 0.06 0.10 0.18 0.11 0.25** 0.12 0.16 NE PSY II Sum of SS 0.12 0.20* 0.15 0.29** 0.30** 0.28** 0.31** 0.20* 0.26** Note. *p < .05, **p < .01; AAT = Auditory Attention total; AAE = Auditory Attention errors; DFT = Design Fluency total; INT = Inhibition Naming time; INE = Inhibition Namin g errors; IIT = Inhibition Inhibition time; IIE = Inhibition Inhibition errors; ST = Statue total; INH = Inhibition; S = Shift; EC = Emotional Control; WM = Working Memory; PO = Plan/Organization; OM = Organization of Materials; M = Monitor.

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92 Table 3 13. Independent samples t tests among gender and PSBS T relational and physical aggression raw scores (N = 101) Female Male t df RA 15.54 13.61 1.65 99 (6.29) (5.37) PA 10.37 12.96 2.56** 99 (4.01) (6.03) Note. **p < .01; Standard de viations appear in parentheses below means; RA = PSBS T relational aggression raw scores; PA = PSBS T physical aggression raw scores. Table 3 14. Hierarchical multiple regression analyses predicting PSBS T relational aggression raw scores from NEPSY II sum of scaled scores while controlling for other predictors (N = 101) Predictor Variables B SE B 2 Model 1 0.08 Age 0.18 0.12 0.16 Gender 2.52 1.19 0.21* CPM Total RS 0.22 0.15 0.16 EVT 2 Total RS 0.03 0.05 0.07 Model 2 0.01 Age 0.18 0.12 0.15 Gender 2.61 1.20 0.22* CPM Total RS 0.18 0.16 0.13 EVT 2 Total R S 0.04 0.05 0.10 NEPSY II Sum of SS 0.07 0.07 0.11 Note: Model 1 Adjusted R 2 = 0.04, F (4, 100) = 1.99, p > .05; Model 2 Adjusted R 2 = 0.04, F (5, 100) = 1.80, p > .05; p < .05; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores.

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93 Table 3 15. Hierarchical multiple regression analyses predicting PSBS T relational aggression raw scores from gender while controlling for other predictors (N = 101) Predictor Variab les B SE B 2 Model 1 0.04 Age 0.12 0.12 0.10 NEPSY II Sum of SS 0.07 0.07 0.10 CPM Total RS 0.17 0.16 0.12 EVT 2 Total RS 0.04 0.05 0.09 Model 2 0.05* Age 0.18 0.12 0.15 NEPSY II Sum of SS 0.07 0.07 0.11 CPM Total RS 0.18 0.16 0.13 EVT 2 Total RS 0.04 0.05 0.10 Gender 2.61 1.20 0.22* Note: Model 1 Adjusted R 2 = 0.00, F (4, 100) = 1.02, p > .05; Model 2 Adjusted R 2 = 0.04, F (5, 100) = 1.80, p > .05; *p < .05; CPM Total RS = CPM total raw score ; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores. Table 3 16. Hierarchical multiple regression analyses predicting PSBS T relational aggression raw scores from CPM total raw scores while controlling for other p redictors (N = 101) Predictor Variables B SE B 2 Model 1 0.08 Age 0.19 0.12 0.17 NEPSY II Sum of SS 0.10 0.07 0.15 Gender 2.58 1.20 0.22* EVT 2 Total RS 0.02 0.05 0.05 Model 2 0.01 Age 0.18 0.12 0.15 NEPSY II Su m of SS 0.07 0.07 0.11 Gender 2.61 1.20 0.22 EVT 2 Total RS 0.04 0.05 0.10 CPM Total RS 0.18 0.16 0.13 Note: Model 1 Adjusted R 2 = 0.04, F (4, 100) = 1.94, p > .05; Model 2 Adjusted R 2 = 0.04, F (5, 100) = 1.80, p > .05; *p < .05; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores.

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94 Table 3 17. Hierarchical multiple regression analyses predicting PSBS T relational aggression raw scores from EVT 2 total raw scores while controlling for other predictors (N = 101) Predictor Variables B SE B 2 Model 1 0.09 (p = .09) Age 0.17 0.12 0.14 NEPSY II Sum of SS 0.06 0.07 0.09 Gender 2.58 1.19 0.22 CPM Total RS 0.13 0.15 0.09 Model 2 0.00 Age 0.18 0.12 0.15 NEPSY II Sum of SS 0.07 0.07 0.11 Gender 2.61 1.20 0.22* CPM Total RS 0.18 0.16 0.13 EVT 2 Total RS 0.04 0.05 0.10 Note: Model 1 Adjusted R 2 = 0.04, F (4, 100) = 2.08, p = .09; Model 2 Adjusted R 2 = 0.04, F (5, 100 ) = 1.80, p > .05; *p < .05; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores. Table 3 18. Hierarchical multiple regression analyses predicting PSBS T relational aggression raw scores from age while controlling for other predictors (N = 101) Predictor Variables B SE B 2 Model 1 0.07 NEPSY II Sum of SS 0.08 0.07 0.12 Gender 2.20 1.17 0.19* EVT 2 Total RS 0.03 0.05 0.08 CPM Total RS 0.20 0.16 0.15 Model 2 0.02 NEPSY II Sum of SS 0.07 0.07 0.11 Gender 2.61 1.20 0.22* EVT 2 Total RS 0.04 0.05 0.10 CPM Total RS 0.18 0.16 0.13 Age 0.18 0.12 0.15 Note: Model 1 Adjusted R 2 = 0.03, F (4, 100) = 1.69, p > .05; Model 2 Adjusted R 2 = 0.04, F (5, 100) = 1.80, p > .05; *p < .05; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores.

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9 5 Table 3 19. Hierarchical multiple regression analyses predicting PSBS T ph ysical aggression raw scores from NEPSY II sum of scaled scores while controlling for other predictors (N = 101) Predictor Variables B SE B 2 Model 1 0.07 Age 0.06 0.11 0.06 Gender 2.39 1.06 0.23* CPM Total RS 0.08 0.14 0.07 EVT 2 Total RS 0.01 0.04 0.02 Model 2 0.04* Age 0.05 0.11 0.05 Gender 2.22 1.04 0.21* CPM Total RS 0.00 0.14 0.00 EVT 2 Total RS 0.02 0.04 0.06 NEPSY II Sum of SS 0.14 0.06 0.24* Note: Model 1 Adjusted R 2 = 0.03, F (4, 100) = 1.82, p > .05; Model 2 Adjusted R 2 = 0.07, F (5, 100) = 2.45, p < .05; *p < .05; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores. Table 3 20. Hierarchical multiple regression analyses predicting PSBS T physical aggression raw scores from gender while controlling for other predictors (N = 101) Predictor Variables B SE B 2 Model 1 0.07 Age 0.10 0.10 0.10 NEPSY II Sum of SS 0.14 0.06 0.25* CPM Total RS 0.00 0.14 0.00 EVT 2 Total RS 0.03 0.04 0.07 Model 2 0.04* Age 0.05 0.11 0.05 NEPSY II Sum of SS 0.14 0.06 0.24* CPM Total RS 0.00 0.14 0.06 EVT 2 Total RS 0.02 0.04 0.06 Gender 2.33 1.04 0.21* Note: Model 1 Adjusted R 2 = 0.03, F (4, 100) = 2.17, p > .05; Model 2 Adjusted R 2 = 0.07, F (5, 100) = 3.04, p < .05; *p < .05; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores.

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96 Table 3 21. Hierarch ical multiple regression analyses predicting PSBS T physical aggression raw scores from CPM total raw scores while controlling for other predictors (N = 101) Predictor Variables B SE B 2 Model 1 0.11* Age 0.05 0.11 0.05 NEPSY II Su m of SS 0.13 0.06 0.24* Gender 2.22 1.04 0.21* EVT 2 Total RS 0.02 0.04 0.06 Model 2 0.00 Age 0.05 0.11 0.05 NEPSY II Sum of SS 0.14 0.06 0.24* Gender 2.22 1.04 0.21* EVT 2 Total RS 0.02 0.04 0.06 CPM Total RS 0.00 0.14 0.00 Note: Model 1 Adjusted R 2 = 0.07, F (4, 100) = 3.10, p < .05; Model 2 Adjusted R 2 = 0.07, F (5, 100) = 2.45, p < .05; *p < .05; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled s cores. Table 3 22. Hierarchical multiple regression analyses predicting PSBS T physical aggression raw scores from EVT 2 total raw scores while controlling for other predictors (N = 101) Predictor Variables B SE B 2 Model 1 0.11* Age 0 .04 0.11 0.04 NEPSY II Sum of SS 0.13 0.06 0.22* Gender 2.24 1.04 0.22* CPM Total RS 0.03 0.13 0.03 Model 2 0.00 Age 0.05 0.11 0.05 NEPSY II Sum of SS 0.14 0.06 0.24* Gender 2.22 1.04 0.21* CPM Total RS 0.00 0.14 0.00 EVT 2 Total RS 0.02 0.04 0.06 Note: Model 1 Adjusted R 2 = 0.07, F (4, 100) = 3.01, p < .05; Model 2 Adjusted R 2 = 0.07, F (5, 100) = 2.45, p < .05; *p < .05; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score; NEPSY II Sum of SS = NEPSY II sum of scaled scores.

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97 Table 3 23. Hierarchical multiple regression analyses predicting PSBS T physical aggression raw scores from age while controlling for other predictors (N = 101) Predictor Variables B SE B 2 Model 1 0.1 1* NEPSY II Sum of SS 0.14 0.07 0.24* Gender 2.33 1.01 0.22* EVT 2 Total RS 0.02 0.04 0.06 CPM Total RS 0.00 0.14 0.00 Model 2 0.00 NEPSY II Sum of SS 0.14 0.06 0.24* Gender 2.22 1.04 0.21 EVT 2 Total RS 0.02 0.04 0.06 CPM Total RS 0.00 0.14 0.00 Age 0.05 0.11 0.05 Note: Model 1 Adjusted R 2 = 0.08, F (4, 100) = 3.04, p < .05; Model 2 Adjusted R 2 = 0.07, F (5, 100) = 2.45, p < .05; *p < .05; CPM Total RS = CPM total raw score; EVT 2 Total RS = EVT 2 total raw score ; NEPSY II Sum of SS = NEPSY II sum of scaled scores.

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98 CHAPTER 4 DISCUSSION The purpose of the present study was to examine the relationships among teacher ratings of relational and physical aggression and variables of interest, as well as to examine the a mount of variance in teacher ratings of relational and physical aggression predicted by (a) executive functioning skills, (b) gender, (c) visual abstract reasoning skills, and (d) language skills. Another purpose of the present study was to examine the cor relations among a direct assessment measure of executive functioning and a teacher rating scale of observed executive functioning skills. Results of the present study indicate that both gender and executive functioning contributed significantly to the pred iction of physical aggression when controlling for all other variables of interest, whereas no variables of interest contributed significantly to the prediction of relational aggression. However, gender was significantly correlated with both relational and physical aggression when controlling for age, such that girls were significantly more likely to exhibit relational aggression and boys were significantly more likely to exhibit physical aggression. Weak to moderate correlations were found between the Meta cognition subscales of the BRIEF and various subtests of the NEPSY II, and moderate correlations were found among CPM total raw scores, EVT 2 total raw scores, and NEPSY II sum of scaled scores when controlling for age. Furthermore, relational aggression w as strongly correlated with physical aggression. These results, as well as implications for future research and possible limitations of the present study are discussed in greater detail in Chapter 4

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99 Predicting Relational and Physical Aggression A primary goal of this study was to explore the relationship between relational and physical aggression and important predictor variables based on previous research and theory. PSBS T relational aggression raw scores and PSBS T physical aggression raw scores were u sed as outcome variables in two separate models, and predictor variables included NEPSY II sum of scaled scores, gender, age, CPM total raw scores, and EVT 2 total raw scores. A series of hierarchical multiple regression analyses were conducted in order to examine the amount of unique variance associated with each outcome variable in significantly predicting (a) PSBS T relational aggression raw scores and (b) PSBS T physical aggression raw scores while controlling for all other variables of interest. Regar ding relational aggression, gender accounted for a significant portion of the unique variance (5%) in PSBS T relational aggression raw scores when controlling for the other four outcome variables of interest. The full model, including all five predictor va riables, accounted for about 9% of the variance in PSBS T relational aggression raw scores, though this was not considered significant [R2 = .09, F (4, 100) = 1.80, p > .05]. Regarding physical aggression, NEPSY II sum of scaled scores significantly predi cted 4% of the unique variance in PSBS .04, p < .05) when controlling for the other four predictor variables. Gender also significantly predicted 4% of the unique variance in PSBS T physical aggression raw 2 = .04, p < .05) when controlling for the four other predictor variables. CPM total raw scores and EVT 2 total raw scores did not significantly account for any unique variance in PSBS T physical aggression raw scores. However, the full model, which includ ed all five predictor variables, significantly accounted for just over 11% of the

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100 variance in PSBS T physical aggression raw scores (R2 = .11, F (4, 100) = 2.45, p < .05). Though the model was statistically significant in predicting about 11% of the varian ce in PSBS T physical aggression raw scores, the actual effect size was small (f2 especially when considering the number of variables within the model. Nevertheless, this does not discount that a significant relationship between physical aggression and gender and EF was found in the present study. Partial Correlations Between EF and Relational and Physical Aggression Another goal of the current study was to investigate the rela tionship between executive functioning skills and teacher ratings of relational and physical aggression. More specifically, the present study sought to examine whether EF skills, as measured by the NEPSY II sum of scaled scores from the EF battery, signifi cantly predict a portion of the variance in PSBS T physical aggression and relational aggression raw scores. Due to the nature of relationally aggressive acts, one would hypothesize that such acts require greater self control, planning, and goal directed b ehavior. As Carpenter and Nangle (2006) state, relational aggression may be indicative of a social advance. Given the hypothesized need for greater self control, planning, and goal directed behavior in children utilizing relational aggression, one would ex pect those exhibiting higher levels of relationally aggressive acts to have greater EF abilities because EF abilities theoretically encompass the aforementioned skill sets. Conversely, given the more impulsive behavior (i.e., lack of inhibitory control) ex hibited by children who engage in greater levels of physical aggression, one would expect lower levels of EF skills. Partial correlations indicated that PSBS T relational aggression raw scores were not significantly correlated with NEPSY II sum of scaled scores (r = 0.11, p > .05).

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101 However, it is interesting to note that the observed relationship, though not significant, was an inverse relationship. Thus, lower PSBS T relational aggression raw scores were associated with higher NEPSY II sum of scaled scor es. Furthermore, the PSBS T relational aggression raw score was weakly but significantly and negatively correlated with the NEPSY II Auditory Attention total raw score (r = 0.22, p = .01), indicating that as NEPSY II Auditory Attention total raw scores in creased, PSBS T relational aggression raw scores decreased. In other words, higher levels of selective auditory attention and vigilance (as measured by this subtest) were associated with lower levels of relational aggression. In another partial correlati onal analysis, PSBS T physical aggression raw scores were weakly but significantly correlated with NEPSY II sum of scaled scores (r = 0.23, p < .05). This was also an inverse relationship. As PSBS T physical aggression raw scores increased, NEPSY II sum o f scaled scores decreased. In other words, higher levels of physical aggression were associated with lower levels of EF skills, confirming the original hypothesis in relation to physical aggression. Among the NEPSY II subtest raw scores, PSBS T physical ag gression raw scores were weakly but significantly correlated with the Statue total raw score (r = 0.25, p < .01). As PSBS T physical aggression raw scores increased, Statue total raw scores decreased, meaning that as physical aggression increased problems with motor persistence and inhibition of automatic responses when distractions were presented were observed. Partial Correlations Between Gender and Relational and Physical Aggression The present study also sought to examine whether or not relational an d physical aggression are significantly correlated with gender among young children when controlling for age. PSBS T relational aggression raw scores were weakly but

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102 significantly correlated with gender in the present study. Though further analysis did not indicate that females exhibited significantly more relational aggression than males, a trend in that direction did emerge. In addition, PSBS T physical aggression raw scores were also weakly but significantly correlated with gender, such that males exhibi ted significantly more physical aggression than females. Partial Correlations Between Relational and Physical Aggression The present study also examined whether or not relational aggression and physical aggression are significantly correlated among young children when controlling for age. Results indicated that PSBS T relational and physical aggression raw scores were strongly and significantly correlated (r = 0.62, p < .01). Such a finding may indicate that children who engage in one form of aggression ar e more likely to engage in another form of aggression. Another explanation is that teachers who perceive a child as exhibiting one form of aggression may perceive that child as being more aggressive in other ways. However, the initial research by Crick an d colleagues (1997) using the PSBS T found an even stronger correlation between overt (i.e., physical) aggression and relational aggression than the present study. Results from their study indicated that a large portion of their sample were identified as e ither overtly or relationally aggressive, but not both. Their factor analyses of the PSBS T also supported two separate aggression constructs overt aggression and relational aggression. A study by Harman (2010) also found a significant moderate correlatio n between relational and physical aggression raw scores on the PSBS T among four and five year old children. The difference in the size of the correlation between relational and e to a variety of factors, including methodological differences, such as the age of the

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103 participants included in the studies and accounting for age in the correlational analyses. It could be that even younger children exhibit less of one or the other type of aggression (likely relational aggression) than children at older ages, thereby reducing the correlation among relational and physical aggression. Research has shown that the developmental trajectory of physical aggression typically peaks between the age s of 24 to 36 months (Alink et al., 2006) or 24 and 42 months (Tremblay, 2004). Thus, levels and expression of physical aggression and relational aggression may differ according to age. Given that the present study controlled for age, it appears that a str ong association between relational and physical aggression exists among five and six year old children, at least as measured by the PSBS T. Future research needs to focus on further exploring the associations between relational and physical aggression acr oss a wider age range, as well as on using various methods and informants for assessing relational and physical aggression. Partial Correlations Between EF and Language The present study also sought to examine the relationship between EF skills and langua ge skills. More specifically, the present study sought to examine the partial correlation between NEPSY II sum of scaled scores from the EF battery and EVT 2 total raw scores when controlling for age. A correlational analysis indicated that NEPSY II sum of scaled scores were moderately and significantly correlated with EVT 2 total raw scores (r = 0.33, p < .01), indicating that as NEPSY II sum of scaled scores increased so did EVT increased, so did their EF skills. This finding may have important implications in relation However, this finding may also be reflective of the idea that various cognitive abilitie s

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104 should be correlated to some extent. In the present study, EVT 2 total raw scores were also moderately and significantly correlated with CPM total raw scores (r = 0.46, p < .01). In relation to the individual NEPSY II EF subtests, EVT 2 total raw scores were moderately and significantly correlated with the Auditory Attention errors raw score (r = 0.30, p < .01), the Inhibition Naming time (r = 0.31, p < .01), the Inhibition Naming errors raw score (r = 0.29, p < .01), the Inhibition Inhibition errors raw score (r = 0.38, p < .01), and was weakly but significantly correlated with the Statue total raw score (r = 0.20, p < .05). All of these NEPSY II EF subtests measure some aspect of inhibitory control. (Though the Auditory Attention task reportedly mea sures selective auditory attention and vigilance, children must exert inhibitory control to keep from touching incorrect colors when certain colors are named on the audio). Thus, as expressive language skills increased, so did their inhibitory control. Thi s finding could lend potential as increases in expressive language skills is likely reflective of more sophisticated internal self talk. Partial Correlations Betwe en EF and Visual Abstract Reasoning The present study also sought to examine the relationship between EF skills and visual abstract reasoning skills. The partial correlation between NEPSY II sum of scaled scores from the EF battery were examined in relatio n to CPM total raw scores while controlling for age. The present study found a moderate and significant relationship between NEPSY II sum of scaled scores and CPM total raw scores (r = 0.39, p < .01). Other studies have found similar moderate correlations among measures of EF and performance on intelligence tests (e.g., Herbers et al., 2011; Blair & Razza, 2007;

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105 Carlson et al., 2002). Herbers and colleagues (2011) found a 0.56 (p < .01) correlation ed scores from the Vocabulary, Matrix Reasoning, and Block Design subtests of the Wechsler Preschool and Primary Scale of Intelligence, Third Edition (WPPSI III) among a sample of children aged four to seven years. Regarding the relationship between CPM t otal raw scores and individual NEPSY II EF subtests, the CPM was moderately and significantly correlated with the NEPSY II Inhibition Naming time (r = 0.40, p < .01), and weakly but significantly correlated with the NEPSY II Design Fluency total raw score (r = 0.28, p < .01), Inhibition Naming errors raw score (r = 0.27, p < .01), and Inhibition Inhibition time (r = 0.29, p < .01), Inhibition Inhibition errors (r = 0.24, p < .05), and Statue (r = 0.25, p < .01) when controlling for age. As CPM total raw scores increased, Inhibition Naming time, Inhibition Naming errors, Inhibition Inhibition time, Inhibition Inhibition errors decreased. In other words, higher visual abstract reasoning was related to higher mental inhibitory processing of competing stimul i. As CPM total raw scores increased, so did Design Fluency and Statue total raw scores. In other words, higher visual abstract reasoning skills were associated with higher behavioral productivity and inhibitory control. No significant correlation was foun d between CPM total raw scores and the NEPSY II Auditory Attention total raw score or Auditory Attention errors. According to Herbers et al. (2011), EF measures that involve the updating of nce, and EF measures that involve behavioral inhibition and set shifting are less related to intelligence. The

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106 the Inhibition subtest on the NEPSY II and CPM total raw s cores. However, given the nature of the Inhibition subtest on the NEPSY II, this subtest likely requires considerable usage of working memory given that individuals have to maintain information about designs in their mind before giving responses to competi ng stimuli. Thus, this subtest likely assesses both working memory and mental inhibitory processing. However, the Statue subtest on the NEPSY II, which is likely a more direct measure of behavioral inhibition, yielded the weakest significant correlation wi th the CPM total raw score as would be expected according to Herbers et al. Partial Correlations Between the NEPSY II and BRIEF The present study also sought to examine the relationship between a direct measure of EF and a rating inventory of EF. More spe cifically, the current study examined the partial correlations among the NEPSY II sum of scaled scores and subtest raw scores and the BRIEF total and subscale raw scores. The NEPSY II sum of scaled scores was weakly but significantly correlated with the BR IEF total raw score (r = 0.26, p < .01) when controlling for age. Higher NEPSY II sum of scaled scores indicate higher levels of EF skills, whereas higher BRIEF total raw scores indicate lower levels of observed EF skills. As such, the present inverse rel ationship found between the NEPSY II sum of scaled scores and BRIEF total raw scores indicate that children with higher levels of EF skills on the NEPSY II were also rated as having higher levels of EF skills on the BRIEF by their teachers. However, this i s a relatively weak correlation given that the two measures reportedly measure the same construct EF. The Inhibition, Shift, and Emotional Control subscales of the BRIEF that compose the Behavior Regulation Index of the BRIEF did not significantly correla te with any subtest score from the NEPSY II EF battery nor with the NEPSY II sum of scaled

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107 scores. In addition, the Auditory Attention total, Auditory Attention errors, and Inhibition Inhibition time raw scores from the NEPSY II were not significantly corr elated with any of the BRIEF subscale raw scores or with the BRIEF total raw score. The Design Fluency total raw score was weakly but significantly correlated with Plan/Organization subscale raw score from the BRIEF (r = 0.22, p < .05). The Inhibition Nam ing time raw score from the NEPSY II was weakly but significantly correlated with the Initiate subscale raw score from the BRIEF (r = 0.20, p < .05), and the NEPSY II Statue subtest raw score was correlated weakly but significantly with the BRIEF Organizat ion of Materials subscale raw score (r = 0.25, p < .01). Furthermore, the NEPSY II Inhibition Naming error raw score was moderately and significantly correlated with the BRIEF Plan/Organize subscale raw score (r = 0.34, p < .01), Organization of Materials subscale raw score (r = 0.32, p < .05), and Working Memory subscale raw score (r = 0.31, p < .01), and weakly but significantly correlated with the BRIEF Initiate subscale raw score (r = 0.26, p < .01) and Monitor subscale raw score (r = 0.21, p < .05). T he NEPSY II Inhibition Naming error raw score was also weakly but significantly correlated with the BRIEF total raw score (r = 0.22, p < .05). The NEPSY II Inhibition Inhibition error raw score was moderately and significantly correlated with the BRIEF Pla n/Organize subscale raw score (r = 0.31, p < .01) and Working Memory subscale raw score (r = 0.29, p < .01), as well as weakly but significantly correlated with the BRIEF Initiate subscale raw score (r = 0.28, p < .01), Organization of Materials subscale r aw score (r = 0.29, p < .01), and Monitor subscale raw score (r = 0.25, p < .01). The NEPSY II Inhibition Inhibition error raw score was also weakly but significantly correlated with the BRIEF total raw score (r = 0.27, p < .01).

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108 Interestingly, none of the Behavior Regulation Index subscales from the BRIEF were correlated with the NEPSY II EF scores. The subscales from the BRIEF that were significantly correlated with the NEPSY II EF scores comprise the Metacognition Index on the BRIEF. In addition, the maj ority of correlations between the BRIEF and NEPSY II were found between the Inhibition Naming and Inhibition Inhibition error scores of the NEPSY II and the Metacognition Index subscales. This may be due to inhibitory control problems being more observable within the naturalistic setting of the classroom. However, the majority of correlations between the two measures and their various subtests and subscales were weak, with a range of correlations from 0.20 to 0.34. Given that the two measures reportedly mea sure the same construct, higher correlations were expected. However, the weaker correlations may be explained by the fact that the two measures assess EF skills in very different formats, one being a formal measure of various individual EF skills and the o ther being a teacher rating of observed behaviors thought to represent EF skills. Summary and Implications for Future Research Results of the current study indicate that physical aggression is related to lower levels of EF abilities. In addition, NEPSY I I sum of scaled scores significantly predicted 4% of the unique variance in PSBS T physical aggression raw scores. No significant relationship was found between EF abilities and relational aggression. To date, no known studies have investigated the relatio nship between EF abilities and both relational aggression and physical aggression in early childhood, and much of the research investigating either EF abilities or relational aggression in early childhood are fairly recent. Additional research is needed to clarify the relationship between EF abilities and overall aggression (e.g., combined physical and relational aggression)

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109 among young children. Such studies could compare EF abilities among participants in a combined high relational and high physical aggre ssion group vs. a high relational aggression group vs. a high physical aggression group vs. a control group. Although Harman (2010) found that EVT 2 scores significantly accounted for the variance in PSBS T relational aggression scores even when age was h eld constant, this was not found in the present study. The difference in these results might be explained (2010) used stepwise multiple regression analyses given the e xploratory nature of her research questions. Stepwise multiple regression maximizes or inflates R2 by selecting the best set of predictors from a set of all possible predictors without taking into consideration all possible variables when the significance levels are computed (i.e., a smaller k is used in calculating the significance levels than is truly accurate). Thus, stepwise regression capitalizes on chance. It may indicate a variable is a significant predictor of the outcome variable when in fact the c orrelation between the two variables does not differ significantly from zero. The finding that NEPSY II sum of scaled scores accounted for an equal amount of unique variance as that of gender in predicting physical aggression raw scores is a very interest ing finding, especially given that the correlation between gender and NEPSY II sum of scaled scores in the present study was 0.08 (p > .05). Children who were rated as exhibiting higher levels of physical aggression obtained significantly lower scores on m easures of EF than did children who were rated as exhibiting lower levels of physical aggression. No other studies to date have explored this relationship in young children.

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110 Overall, the significant relationship between physical aggression and EF skills fo und in the present study may have important implications in relation to the SIPM of aggressive behavior as proposed by Crick and Dodge (1994). Specifically, young children who tend to be more physically aggressive may be unable to choose more appropriate b ehavioral responses because they act out physically before they review behavior response options. In other words, they may be unable to navigate through the various steps of the SIPM due to a lack of inhibitory control. For example, in the present study, t he NEPSY II EF subtest with the strongest correlation with PSBS T physical aggression raw scores was the Statue subtest, which requires a child to inhibit automatic responses and switch between various response types. However, the age of the child, as well as existing disorders (e.g., ADHD), are also important to consider. Further studies are needed to examine the relationship among EF skills and physical and relational aggression at different ages so as to inform the SIPM of aggressive behavior. Neverthele ss, the present findings have important implications for designing violence and/or aggression prevention and treatment programs for young children. so as to increase the ir ability to select more prosocial responses in given situations may prove more successful. Research examining current prevention and treatment programs in relation to their impact on EF abilities is also needed. Like Harman (2010), the present study fou nd gender to be a significant predictor of PSBS T physical aggression scores. Gender was also a significant predictor of PSBS T relational aggression raw scores in the present study. Girls were significantly more likely to be relationally aggressive than b oys, and boys were significantly more likely to be

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111 physically aggressive than girls. Unlike Harman (2010), the present study utilized hierarchical multiple regression to control for other predictor variables instead of stepwise multiple regression, which c apitalizes on chance. Thus, it is felt that present PSBS T physical aggression and relational aggression scores accounted for by gender. It is important to note that the mean and range of both the PSBS T relational aggression raw scores and the PSBS T physical aggression raw scores were somewhat low in the present study. The mean PSBS T relational aggression raw score was 14.60 and ranged from 8 to 33, whereas the mean PSB S T physical aggression raw score was 11.62 and ranged from 8 to 32. The potential range of each of these scales is 8 to 40. Furthermore, given the high correlation between PSBS T relational and physical aggression total raw scores, future studies may expl ore the relationship between overall aggression (i.e., combining relational and physical aggression into one category) and EF abilities. Overall, the significant relationship between gender and relational aggression among young children has been establish ed in previous literature (e.g., McEvoy et al., 2003; Monks et al., 2005), although some studies have not found girls to be more relationally aggressive at young ages (e.g., Harman, 2010; Juliano et al., 2006). Further research is needed to clarify the dev elopmental trend of relational aggression, especially given the negative sequelae associated with both types of aggression. Differences in findings might be explained through differences in research methodologies used in various studies. For example, the c urrent study measured relational and physical aggression using a teacher rating form, whereas other studies

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112 have used peer nominations and direct observation as ways of assessing relational and physical aggression. Nevertheless, the current findings are su pported within the literature among young children. Overall, a longitudinal study spanning across early childhood and into adolescence would help further elucidate the developmental trend of relational aggression among both males and females, especially if different methodologies for assessing relational and physical aggression are utilized. The strongly significant correlation between relational and physical aggression found in the present study also replicates previous research findings (e.g., Crick et a l., 1997; Harman, 2010). In their original research using the PSBS T to assess relational and physical aggression, Crick and colleagues (1997) found the correlation between relational and physical aggression to be .76 among boys and .73 among girls. Howeve r, factor analyses conducted by Crick and colleagues showed relational aggression and overt (i.e., physical) aggression to be distinct constructs. Crick and colleagues also explained that the high correlations between overt and relational aggression found in their study were similar to those found among other forms of aggression in previous studies. Furthermore, Crick and colleagues explained that over 60 percent of the children in their study were identified as either relationally or overtly aggressive, bu t not both. Thus, Crick and colleagues concluded that relational aggression and physical aggression are distinct forms of aggression that occur among children as young as three or four years of age, though there is also some overlap among aggressors. Overa ll, more research using different methodologies for assessing relational and physical aggression is needed for clarification.

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113 Regarding the relationship between language and EF abilities, the present study found a moderate, positive correlation between EV T 2 total raw scores and NEPSY II sum of scaled scores. However, only the NEPSY II sum of scaled scores (vs. EVT 2 total raw scores) accounted for a significant amount of unique variance in PSBS T physical aggression raw scores when controlling for the inf luence of age. This finding in relation to language may be explained by considering age as a factor. In other words, language may prove a more significant predictor of physical and relational aggression at much younger ages than the current sample, such as at two, three, or four years of age. Furthermore, such a relationship between language ability and EF may simply reflect a correlation between two distinct cognitive abilities. Too high a correlation would have suggested the two measures were measuring th e same construct, and too low a correlation would have suggested the NEPSY II EF subtests were not measuring a cognitive ability (as we know verbal ability to be a strong indicator of intelligence). Nevertheless, this moderate significant relationship betw een EF abilities and language being a precursor to inhibitory control, especially as the strongest correlations were found among the NEPSY II subtests that measured inhi bitory control. Future research is needed to further examine the relationship between EF abilities and language. A possible avenue of research may be examining the differences between receptive vs. expressive language skills in relation to individual EF ab ilities. The present study also found a significant relationship between visual abstract reasoning as measured by the CPM total raw score and EF abilities as measured by the NEPSY II sum of scaled scores. The correlation between these two measures was 0.39

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114 (p < .01), indicating a moderate, positive relationship. Such a moderate correlation between measures of intelligence and EF abilities is supported in the literature (e.g., Herbers et al., 2011). However, it is important to note that only the NEPSY II sum of scaled scores (vs. the CPM total raw score) accounted for a significant portion of unique variance in PSBS T physical aggression raw scores when controlling for all other predictor variables. As such, the moderate relationship between EF abilities and visual abstract reasoning found in the current study may suggest that EF as measured by the NEPSY II sum of scaled scores is a related but distinct construct from visual abstract reasoning. Further research is needed to clarify the exact relationship betwe en individual EF abilities and other cognitive factors that measure general intelligence. Confirmatory factor analytic studies may help further clarify the construct of EF in relation to g. Another important finding of the current study was the weak but si gnificant correlations between the NEPSY II sum of scaled scores and the BRIEF total raw various NEPSY II EF subtests. Given that these two measures reportedly measure the s ame construct (i.e., EF), higher correlations were expected. However, these two measures attempt to assess EF abilities in very different ways. The NEPSY II EF subtests measure EF in the more traditional neuropsychological format by assessing individual EF abilities through individual subtests, whereas the BRIEF attempts to measure EF through observed behaviors within the classroom or home setting. Further research is needed to examine the relationship between the BRIEF subscales and NEPSY II EF subtests in relation to how these subscales and subtests load onto an

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115 overall construct or individual constructs of EF. A truly interesting finding in the present study was that none of the Behavior Regulation Index subscales from the BRIEF, including the Inhibition subscale, were correlated with any of the NEPSY II EF subtests. More research is needed to examine why this was the case. Given the names of these subscales, one would have expected them to be more highly correlated with direct measures of inhibitory contr ol than other subscales on the BRIEF. Limitations As discussed previously, the current sample was sufficient to have power at the .80 level for correlations greater than or equal to r = 0.27. Many of the correlations in the present study were weak correla tions, ranging from 0.20 to 0.29, indicating power levels mostly within the .60s to .70s range. Sufficient power was observed for the five predictor model of physical aggression given that R 2 = 0.11. Overall, a larger sample size would have increased power levels in relation to the observed significant correlations and would have allowed for the inclusion of individual NEPSY II EF subtests within the hierarchical multiple regression models, further elucidating the relationship between individual EF abilitie s and physical aggression. Furthermore, it is important to note that the mean and range of both the PSBS T relational aggression raw scores and the PSBS T physical aggression raw scores were somewhat low in the present study. The mean PSBS T relational ag gression raw score was 14.60 and ranged from 8 to 33, whereas the mean PSBS T physical aggression raw score was 11.62 and ranged from 8 to 32. The true range of each of these subscales is 8 to 40. A more evenly distributed sample in terms of relational and physical aggression scores may have produced somewhat different results in relation to the EF abilities measured. Also, measuring relational and physical aggression through

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116 more direct means, such as through direct observations of child behavior or peer n omination may have produced different results. However, given the time and resource constraints of the present study, the PSBS T was deemed the best manner in which to measure relational and physical aggression. In addition, grouping the EF abilities meas ured by the various NEPSY II EF subtests into one composite score may have masked the influence of stronger (vs. weaker) EF abilities. The scaled scores from these subtests were added into a sum of scaled scores so as to derive a more global (i.e., all enc ompassing) measure of EF due to the sample size of the current study. However, research assessing EF abilities in relation to other variables often looks at discreet EF abilities, not summations of overall EF scores. Despite the aforementioned limitation s, the present study suggests that there is a relationship between EF abilities and physical aggression, as well as between EF abilities and language and visual abstract reasoning skills. Overall, the construct of EF represents a promising construct for fu ture research within the fields of psychology and early childhood.

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117 APPENDIX PRESCHOOL SOCIAL BEHAVIOR SCALE TEACHER FORM The following measure is based upon that described in Crick, N. R., Casas, J. F., & Mosher, M. (1997). Relational and overt aggress ion in preschool. Developmental Psychology, 33 579 588. Subscales: Relational Aggression: Items # 4, 8, 11, 13, 15, 19, 21, 22 Overt/Physical Aggression: Items # 2, 5, 7, 12, 14, 17, 20, 23 Prosocial Behavior: Items # 1, 3, 6, 10 Depressed Affect: Items # 9, 16, 18

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118 Preschool Social Behavior Scale Teacher Never or Always almost Not Some or almost never true often times Often always true 1. This child is good at sharing and taking turns 1 2 3 4 5 2. This child kicks or hits others. 1 2 3 4 5 3. This child is helpful to peers. 1 2 3 4 5 1 2 3 4 5 that peer or does what this child asks. 5. This child verbally threatens to hit or beat up other 1 2 3 4 5 children. 6. This child is kind to peers. 1 2 3 4 5 7. This child pushes or shoves other children. 1 2 3 4 5 8. T his child tells others not to play with or be a 1 2 3 4 5 1 2 3 4 5 10. This child says or does nice things for other kids. 1 2 3 4 5 11. When mad at a peer, this child keeps that peer 1 2 3 4 5 from being in the play group. 12. This child verbally threatens to physically harm 1 2 3 4 5 another peer in order to get what they want. 13. This child tries to embarrass peers by making fun 1 2 3 4 5 of them in front of other child ren. 1 2 3 4 5 projects, toys) when he/she is upset. 1 2 3 4 5 birthday party unless he/she does what the child wants. Age ______

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119 Never or Alwa ys almost Not Some or almost never true often times Often always true 16. This child looks sad. 1 2 3 4 5 17. This child throws things at others when he/she 1 2 3 4 5 18. This child smiles at other kids. 1 2 3 4 5 19. Th is child walks away or turns his/her back when 1 2 3 4 5 he/she is mad at another peer. 20. This child verbally threatens to push a peer off a 1 2 3 4 5 toy (e.g. tricycle, play horse) or ruin what the peer is working on (e.g. building blocks) unless that peer shares. 21. This child tries to get others to dislike a peer 1 2 3 4 5 (e.g. by whispering mean things about the peer 22. This child verbally threatens to keep a peer out of 1 2 3 4 5 the play group if the peer do says. 23. This child hurts other children by pinching them. 1 2 3 4 5 24. This child is well liked by peers of the same sex. 1 2 3 4 5 25. This child is well liked by peers of the opposite sex. 1 2 3 4 5

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129 BIOGRAPHICAL SKETCH Twyla L. Mancil was born in Statesboro, Georgia. She obtained her undergraduate degree at Georgia Southern Universit y, where she majored in psychology. Upon receiving her Bachelor of Science degree in Psychology, Twyla was admitted to the Clinical Psychology Master of Science program at Georgia Southern University, where she completed one year of study before being admi tted to the School Psychology doctoral program at the University of Florida in 2006 with an Alumni Fellowship. Twyla earned her Masters of Education in School Psychology from the University of Florida in May of 2008. During the 2011 2012 academic year, Twy la completed her clinical internship at the Florida State University Louise R. Goldhagen Multidisciplinary Evaluation and Consulting Center. Following graduation with her doctoral degree in August 2012, she will be completing a postdoctoral clinical positi on at the Moore Counseling Center in Moore, Oklahoma, where she will work towards licensure and gain further experience with autism spectrum disorders.