PREDICTING DEPRESSION ACROSS MULTIPLE DOMAINS IN A 12 YEAR LONGITUDINAL INVESTIGATION OF A POPULATION SAMPLE OF CHILDREN AND ADOLESCENTS by Sherry Bellamy BSc University of Northern British Columbia 2009 THESIS SUBMITTED IN PARTIAL FULLFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMMUNITY HEALTH SCIENCES UNIVERSITY OF NORTHERN BRITISH COLUMBIA August 2012 © Sherry Bellamy 2012 1+1 Library and Archives Canada Bibliotheque et Archives Canada Published Heritage Branch Direction du Patrimoine de I'edition 395 Wellington Street Ottawa ON K1A0N4 Canada 395, rue Wellington Ottawa ON K1A 0N4 Canada Your file Votre reference ISBN: 978-0-494-94118-8 Our file Notre reference ISBN: 978-0-494-94118-8 NOTICE: AVIS: The author has granted a non­ exclusive license allowing Library and Archives Canada to reproduce, publish, archive, preserve, conserve, communicate to the public by telecommunication or on the Internet, loan, distrbute and sell theses worldwide, for commercial or non­ commercial purposes, in microform, paper, electronic and/or any other formats. L'auteur a accorde une licence non exclusive permettant a la Bibliotheque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par telecommunication ou par I'lnternet, preter, distribuer et vendre des theses partout dans le monde, a des fins commerciales ou autres, sur support microforme, papier, electronique et/ou autres formats. The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. L'auteur conserve la propriete du droit d'auteur et des droits moraux qui protege cette these. Ni la these ni des extraits substantiels de celle-ci ne doivent etre imprimes ou autrement reproduits sans son autorisation. In compliance with the Canadian Privacy Act some supporting forms may have been removed from this thesis. Conform em ent a la loi canadienne sur la protection de la vie privee, quelques formulaires secondaires ont ete enleves de cette these. W hile these forms may be included in the document page count, their removal does not represent any loss of content from the thesis. Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant. Canada Abstract The aim of this longitudinal study was to investigate the strength and relative importance of multiple predictors of depression in adolescence and young adults aged 16 to 20 years. Data for this study were drawn from Statistics Canada’s National Longitudinal Survey of Children and Youth. Hierarchical regressions were conducted separately by gender in a mixed sample containing biological mothers and other caregivers and in a sample containing exclusively biological mother-child dyads. In both samples, age predicted depression with adolescents reporting more depression symptoms compared to young adults. Girls reported higher depression scores than boys. Anxiety/depression and lower self-esteem predicted depression for boys. Girls’ depression was predicted by loss of a parent, higher anxiety/depression, and higher aggression. The biological mother-child sample revealed a stronger effect of maternal depression as a predictor of depression for girls. Lower parental monitoring predicted depression for girls and parental rejection predicted depression for boys and girls. iii TABLE OF CONTENTS Abstract ii Table of Contents iii List of Tables v Acknowledgement and Dedication vi Chapter One Introduction 1 Chapter Two Literature Review Depression, Prevalence, Diagnosis Guidelines and Comorbidity Theories of Depression Summary 3 3 Chapter Three 12 28 Methods Methods for the National Longitudinal Survey of Children and Youth (NLSCY) The Present Study 33 Chapter Four Results Mixed Sample Regressions Biological Mother and Child Regressions 43 48 54 Chapter Five Discussion Limitations and Strengths of the Study Implications Summary 61 72 74 75 References 30 30 77 Appendix A Results of the Hierarchical Multiple Regression Model for the Mixed Sample Predicting Depression 95 Appendix B Results for Hierarchical Multiple Regression for Predicting Youth Depression 97 Appendix C Proportions of Youth in Outcome Depression Measure 99 Appendix D Descriptive Statistics for the Mixed Sample for Boys 100 Appendix E Descriptive Statistics for the Mixed Sample of Girls 101 Appendix F Descriptive Statistics for the Biological Mother and Child Sample for Boys Appendix G Descriptive Statistics for the Biological Mother and Child Sample for Girls Appendix H Correlations Between Cycle 1 Variables for the Mixed Sample Appendix I Correlations between Cycle 4 Variables for the Mixed Sample Appendix J Correlations between Cycle 1 and Cycle 4 Variables for the Mixed sample V List of Tables Table 1 Outcome Variable for Cycle 7 and Predictor Variables for Cycle 1 and Cycle 4 34 Table 2 Child Age, Child Gender. PMK Marital Status and PMK Depression for Missing and Complete Samples 46 Table 3 Results for Hierarchical Multiple Regression for Predicting Depression in the Mixed Sample for Boys 49 Table 4 Results for Hierarchical Multiple Regression for Predicting Depression in the Mixed Sample for Girls 52 Table 5 Results for Hierarchical Multiple Regression for Predicting Depression in Biological Mother-Child Dyads for Boys 55 Table 6 Results for Hierarchical Multiple Regression for Predicting Depression in Biological Mother-Child Dyads for Girls 59 VI Acknowledgements and Dedication I would like to express my gratitude to several individuals as without their support the completion of my thesis would not have been possible. First and foremost I would like to express my appreciation for my supervisor Dr. Cindy Hardy for her ongoing guidance, patience and support. Dr. Hardy’s continuous encouragement and engagement in this project has made the process of completing my master’s thesis a joyful and rewarding experience. I could not have asked for a better supervisor. I would also like to thank the members of my supervisory committee Dr. Corinne Koehn and Dr. Ken Prkachin for their advice and guidance in shaping this project. I would also like to express my sincere thanks to Statistics Canada for providing the opportunity for students to analyze rich sources of data through their Research Data Centres Program (RDC). The research and analyses are based on data from Statistics Canada and the opinions expressed do not represent the views of Statistics Canada. I am also grateful to the RDC staff at the University of British Columbia for their very friendly and helpful service. I would also like to acknowledge and give thanks for the financial support I have received from UNBC, particularly for the Graduate Research Awards, Research Project Award and the Research Travel Awards I have received. Last but not least I thank my husband and best friend Andy Bellamy for his love, support and encouragement throughout my education and in life. I also express my sincerest gratitude to my father for his encouragement and patience throughout this long process. Chapter 1: Introduction This study is a longitudinal investigation of predictors o f depression from early childhood to young adulthood in a population sample of Canadian children and youth. Data for the study were drawn from the National Longitudinal Survey o f Children and Youth (NLSCY) conducted by Statistics Canada. The NLSCY is a long term survey that follows children from birth to adulthood (Statistics Canada, 2007a). The NLSCY is designed to collect information on a broad range of factors thought to influence a child’s social, emotional and behavioural development and examine the impact o f these factors over time (Statistics Canada, 2007a). The current study is based on the original cohort of children who have participated in the NLSCY since it first began in 1994/95 and have been followed until 2006/07 over 7 waves of data collection which occurred every 2 years. Three waves of data were used for analysis with children in Cycle 1 who were between the ages o f 4 and 8 years; these same children were between the ages of 10 and 14 years in Cycle 4 and 16 to 20 years in Cycle 7. The present study seeks to contribute to and expand on existing research on the effects of childhood factors that are thought to increase risk for depression in adulthood. According to Mason et al. (2004) there is a gap in research that investigates a wide range o f childhood risk factors that may be predictive of adult problems. Knowledge of predictors of depression during childhood development provides evidence for the aetiology of depression and informs the development of prevention programs. Examining the effects of specific predictors of depression emerging over different developmental time periods allows for a better understanding of dynamic risk profiles emerging in childhood and adolescence (Kosterman et al., 2010). As a result, longitudinal investigations are invaluable as they offer the opportunity 2 to address issues of timing and content across childhood therefore allowing for the refinement of targeted prevention efforts (Kosterman et al., 2010). The advantage of the study is it allows for a prospective examination of predictors of depression in multiple domains across child and adolescent development in a representative sample of Canadian children. The study will also examine potential gender differences in risk to develop depression. Measures will be taken from both parents and children, allowing for the utility of both parental and child self-report of behaviour. The addition of self-report measures of child emotions and behaviour are useful as internalizing problems are more difficult to discern compared to externalizing behaviours (Kosterman et al., 2010). The value of child self-report has been demonstrated in research. For example, Mason et al. (2004) found child reported behaviour problems at 10 years predicted depression at 21 years while parent reported behaviour problems did not. A comprehensive review of the literature investigating genetic, psychological and social predictors of depression guided the selection of predictor variables. Measures were taken from both parents and children. Parents provided information concerning characteristics of parenting behaviour as well as parental symptoms of depression (Statistics Canada, 2007). Child behaviour was measured by the parent during the child’s early years and then by selfreport when the child reached 10 years of age. Child self-complete questionnaires allowed for measurement of the child’s perceptions concerning the quality of their relationships with parents and peers as well as emotional problems such as anxiety and depression. 3 Chapter 2: Literature Review Depression, Prevalence, Diagnosis Guidelines and Comorbidity According to the World Health Organization (WHO), unipolar depression is currently the leading cause of disability as measured by years of life lost to disability (YLDs), and is the second cause of disability adjusted life years (DALYs) for the age group 15 to 44 (WHO, 2010). Unipolar depression is currently the fourth leading contributor to the global burden of disease and has been projected to increase and become the second leading cause of DALYs for both genders at all ages by the year 2020 (WHO, 2010). In a recent review of epidemiologic studies of children and adolescents bom between 1965 and 1996 the overall prevalence rates for depression were 2.8% for children under 13, and 5.6% for adolescents between the ages of 13 to 18 (Costello, Erkanli & Angold, 2006). Adolescent girls had a higher prevalence of depression (5.9%) compared to adolescent boys (4.6%). While the experience o f sadness is normal, prolonged depression that is persistent over time is not (Oltmanns, Emery & Taylor, 2006). Clinical depression is characterized by a combination of emotional, cognitive, somatic and behavioural symptoms (Oltmanns et al., 2006). As a result, depression often presents with depressed mood, disturbed sleep and/or appetite and loss of interest. Cognitive symptoms may also present with poor concentration, feelings of guilt or low self-worth, and thoughts of suicide (Oltmanns et al., 2006). These symptoms may become chronic or reoccur over the lifespan and can lead to substantial functional impairments resulting in great difficulty in the ability to participate in everyday responsibilities (WHO, 2010). The substantial burden of depression and its projected increase in DALYs over both genders and age groups indicate prevention of depression is an important public health issue. 4 Depression diagnostic guidelines. Clinical guidelines for diagnosis of depression may be found in the Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSMIV-TR, 2000) established by the American Psychiatric Association; as well as the International Statistical Classification of Diseases and Related Health Problems (ICD-10) developed by WHO. Both the ICD-10 and DSM-IV-TR take a categorical approach to diagnosis of mental disorders and coordination between the developers have resulted in fully compatible manuals with the same diagnostic codes and terms (DSM-IV-TR, 2000). The DSM-IV-TR defines Major Depressive Disorder as suffering from one or more major depressive episodes characterized by at least two weeks of depressed mood and loss of interest in addition to four additional symptoms of depression. Major Depressive Disorder can be further classified into mild, moderate or severe. Severe Major Depressive Disorder is further classified into subtypes with or without psychotic features (DSM-IV-TR, 2000). Severity of Major Depression is based on the degree of distress and functional impairment, as well as the number and severity of symptoms. For instance, mild depressive episodes have the presence of five or six depressive symptoms accompanied by either mild functional impairment or the ability to function with substantial effort (DSM-IV-TR, 2000). In contrast, severe episodes without psychotic features are characterized by clear, observable disability such as the inability to work or care for the family. Episodes considered to be moderate have a severity judged to be intermediate between mild and severe (DSM-IV-TR, 2000). Dysthymic Disorder is characterized by depressed mood occurring more days than not, for the duration of at least two years and accompanied by additional symptoms of depression without meeting the criteria for a major depressive episode (DSM-IV-TR, 2000). Subclinical depression is also studied by researchers as it may serve as a risk factor for the development of more serious depressive illness (Ingram & Siegle, 2009). Moreover, subclinical depression is often accompanied by clear depressive symptoms, for example, lack of motivation or cognitive deficits, and may also lead to some impairment in daily life (Ingram & Siegle, 2009). Subclinical depression may be defined using the criteria for minor depression provided by the DSM-IV-TR as a proposed new category for further research. Minor depressive disorder has symptoms which are identical to major depressive disorder episodes but are fewer in number and result in less impairment (DSM-IV-TR, 2000). A minor depressive episode involves sad mood or lack of ability to experience pleasure or loss of interest. Two but less than five depressive symptoms must be present (DSM-IV-TR, 2000). However, not all researchers define the construct of depression in the same way or use the DSM-IV-TR or the ICD-10 to select participants for research. A more common approach is to select participants with elevated scores on depression questionnaires (Ingram & Siegle, 2009). For example, the Beck Depression Inventory provides a lower cut off point for minor depression in order to define subclinical depressed states (Ingram & Siegle, 2009). The selection of participants based on elevated scores on questionnaires is based on the contention that depression may be viewed as dimensional rather than categorical. The dimensional view of depression comes from the assumption that mild and severe depression are essentially the same construct that exists on either end of the same continuum. Researchers investigate minor or subclinical depression for a variety of reasons. For instance, minor depression, although less serious, may also impose significant disruption in individual’s lives and may lead to cognitive deficits, interpersonal difficulties, as well as motivational problems (Ingram & Siegle, 2009). Additionally, minor depression may be a 6 significant risk factor for later development of Major Depressive Disorder and therefore offers an opportunity to investigate risk (Ingram & Siegle, 2009). Prevalence. Estimates of prevalence rates for major depression, dysthymia, and other depressive disorders vary widely between studies (Avenevoli, Knight, Kessler, & Merikangas, 2008). Overall, prevalence of depression in school children aged 7 to 12 years are generally lower in comparison to adolescents 13 to 18 years (Avenevoli et al., 2008). According to Garber, Gallerani and Frankel (2009) estimates of depression rates for children range from 2.8% to 3.0% however, the rate depends of the type of depression measured. For example, prevalence rates for dysthymic disorder have been measured at 0.6%, depression at 0.7% and major depressive disorder approximately 1.4% (Garber et al., 2009). Lifetime prevalence rates for depression are higher in adolescents with approximately 9% experiencing at least one episode of major depression in their lifetime and 11 % experiencing at least one episode of minor depression (Abela & Hankin, 2008a). Most studies of depression and related disorders show substantially different prevalence rates between the sexes (Kendler, Prescott, Myers & Neale, 2003). For instance Kendler et al. (2003) found lifetime prevalence for major depressive disorders were much higher for women (40.4%) compared to men (28.5%). In a longitudinal study investigating gender differences in depressive symptoms in adolescents and young adults aged 12 years to 23 years, Galambos, Leadbeater and Barker (2004) found gender differences emerge in early adolescence, with girls experiencing significant increases in depression beginning at about 12 to 13 years of age. Consistent with the literature, the authors found throughout the adolescent years and into adulthood, the ratio of girls experiencing depression compared to boys escalated to and remained at 2:1 with one wave of measurement finding the ratio at 3:1; 7 however, the authors caution the 3:1 ratio may be due to sampling variability (Galambos et al., 2004). Comorbidity. Comorbidity refers to the co-occurrence of two or more disorders in the same individual which may occur at the same time or at different times over the lifespan (Seligman & Ollendick, 1998). Research has shown depression in youth often occurs with other disorders and the occurrence of “pure” depression is the exception rather than the rule (Angold, Costello & Erkanli, 1999; Bhardwaj & Goodyer, 2009; Gallerani, Garber & Martin, 2010). In an analysis of DSM-III disorders in a longitudinal study of a large, population based sample of adolescents, McGee et al. (1990) found depressive disorder was most frequently associated with other disorders, with almost two-thirds of the 40 depressed adolescents presenting with a co-occurring disorder. In a review of several studies investigating comorbidity in the general population, Angold et al. (1999) found depressive disorder was highly comorbid with anxiety, ADHD, and combined categories CD/ODD. Anxiety was the most strongly associated with depression followed by CD/ODD and ADHD. The co-occurrence of anxiety and depression has been found in children, adolescents and adults (Colletti et al., 2009). In a review of recent literature, Colletti et al. (2009) found especially high anxiety/depression comorbidity rates in children and adolescents ranging from 30 to 75%. Further, Seligman and Ollendick (1998) reviewed several studies examining the rates of comorbidity of anxiety and depression among children and adolescents in both the general population as well as help-seeking samples. Seligman and Ollendick (1998) found estimates of comorbidity in the general population varied greatly depending on which disorder was diagnosed first. For instance, of individuals diagnosed with anxiety first 13% to 69% also met criteria for a depressive disorder, whereas 8 33% to 75% of individuals diagnosed with a depressive disorder also met the criteria for an anxiety disorder (Seligman & Ollendick, 1998). Evidence suggests anxiety in children often precedes the development o f depression (Seligman & Ollendick, 1998) indicating anxiety may be a predictor for later depression symptoms in children (Colletti et al., 2009). Support for the existence of a temporal relationship of anxiety preceding depression has been found in cross-sectional as well as longitudinal examinations. In a cross-sectional investigation, Strauss, Lease, Last and Francis (1988) examined differences in patterns of comorbidity among a younger sample aged 5 to 11 years and an older sample aged 12 to 19 years. Using DSM-III criteria for overanxious disorder and concurrent major depression, Strauss, Lease et al. (1998) found older children more frequently presented with anxiety and concurrent major depression compared to the younger age group. Strauss, Last, Hersen and Kazdin (1988) found 28% of children and adolescents in a sample aged 5 to 17 years had co-occuring anxiety and major depression with higher rates of comorbidity in the older portion of the sample. Specifically, 35% of the children aged nine and older presented with comorbid depression and anxiety (Strauss, Last, et al., 1988). A longitudinal investigation by Feng, Shaw, and Silk (2008) found boys whose anxiety levels increased between ages 3.5 to 8 years were more likely to develop depression compared to children whose anxiety decreased overtime. These results indicate a potential pathway of the development of depression, particularly when levels of anxiety increase across middle childhood (Feng et al., 2008). Overall, evidence suggests there is a high rate of comorbidity of anxiety and depression in children, adolescents and adults. Strong evidence for a temporal relationship between anxiety and depression has also been demonstrated, with anxiety symptoms occurring before depression in many children. Depression has also been found to be highly comorbid with behavioural disorders in children and adolescents. Behavioural disorders include Conduct Disorder (CD), Oppositional Defiant Disorder (ODD), and Attention Deficit Hyperactivity Disorder (ADHD) (Costello, Mustillo, Erkanli, Keeler & Angold, 2003). It is important to note that many studies combine conduct disorder and oppositional defiant disorder into one category defined as externalizing disorders. While ODD and CD are frequently correlated, ODD symptoms often reflect greater difficulties in interpersonal functioning, temperament, or personality, whereas CD symptoms reflect a more behavioural dimension (Burke, Loeber, Lahey, & Rathouz, 2005). The distinction between CD and ODD is an important one, as studies that separate the two disorders find differences in comorbidity rates with depression. For instance, in a representative sample of 1420 children aged 9 to 16 years Costello et al. (2003) found children diagnosed as depressed using the Child and Adolescent Psychiatric Assessment (CAPA) were 16 times more likely to have ODD, compared to non-depressed children. Further, children assessed as depressed were also more than twice as likely to have ADHD and only 0.7 times more likely to have CD (Costello et al., 2003). However, studies combining CD and ODD have found high levels of comorbidity with depression. Further, a temporal relationship has also been found between depression and behavioural disorders in both children and adult samples. In a nationally representative sample of 3,199 adults, Nock, Kazdin, Hiripi and Kessler (2007) found 77.6% of adults with major depression experienced ODD symptoms first, and 75.3% with dysthymia disorder experienced ODD onset first. Additionally, Ezpeleta, Domenech and Angold (2006) found 70.4% of children with comorbid behavioural (CD/ODD) and depressive disorders exhibited disruptive behaviour before the emergence of depressive symptoms. Rohde, Lewinsohn, and Seeley (1991) found similar results, with 71.8% of their sample o f adolescents and adults presenting with symptoms of behaviour disorder before the onset of depression. This temporal relationship between behavioural disorders and depression has also been found in samples exhibiting subthreshold levels of CD/ODD. For example, Gallerani et al. (2010) found subthreshold externalizing disorders predicted later depression (MDD/DD) in a sample of 240 adolescents. Overall, these results suggest behavioural disorders in youth may increase the risk of developing depression indicated by the temporal relationship with behaviour problems preceding depression. Moreover research has demonstrated that behaviour disorders have a robust association with depression as even subthreshold behaviour disorders significantly predicted later depression in adolescents. It is important to note that the relationship between behavioural disorders and depression is complex, as studies have revealed differences between sexes as well as differences between CD and ODD when treated as separate disorders. Boylan, Georgiades, and Szatmari (2010) investigated the relationship between ODD and depression in a longitudinal investigation of boys and girls aged 6 to 7 years old at the beginning of the study. Symptoms of depression and ODD were measured at three time points spanning four years. Boylan et al. reported high levels of oppositional symptoms measured at Time 1 predicted higher levels of depression in boys at Time 2 and Time 3. The reverse relationship was not found, as depression symptoms did not predict ODD symptoms across time periods. These results suggest ODD may place boys at increased risk for developing later depression (Boylan et al., 2010). In contrast, ODD symptoms in girls did not predict later depression. However, depressive symptoms in girls at Time 2 and Time 3 predicted a decrease in ODD symptoms, 11 suggesting a possible inhibitory effect of depression on oppositional behaviour in girls (Boylan et al., 2010). However, conflicting evidence has been found concerning the relationship between oppositional behaviour and depression in girls. In a community sample of 2,451 girls aged 5 to 8 years, Burke, Hipwell, and Loeber (2010) found symptoms of ODD predicted later depression while symptoms of CD did not. In particular, a specific subset of ODD symptoms associated with negative affect such as angry, touchy and spiteful significantly predicted depression while other specific subsets of behavioural symptoms such as arguing, temper and behaving antagonistically did not (Burke et al., 2010). These results suggest comorbidity of ODD and depression may be directly attributable to a specific set of symptoms that are related to the affective dimension of ODD rather than the behavioural dimensions (Burke et al., 2010). Similar results were found in a longitudinal investigation following boys beginning at ages 7 to 12 years until they reached 18 years of age, where ODD predicted later depression while CD did not (Burke et al., 2005). The separation of specific dimensions of ODD as well as sex differences may contribute to conflicting findings among studies investigating the relationship between ODD and depression in children. Researchers have also found depression is highly comorbid with ADHD among children and adolescents in both community as well as clinical samples (Elia, Ambrosini, & Berrettini 2008). An investigation of comorbidity in a sample of clinically referred preschool children aged 4 to 6 years and school aged children aged 7 to 9 years diagnosed with ADHD found comorbid major depression in 42% of the preschool sample and 47% of the school aged sample (Wilens et al., 2002). Further, in a sample of children and adolescents aged 6 to 12 18 years diagnosed with ADHD, Elia et al. (2008) found 21.6% had concurrent mild depression. While studies have shown prevalence of ADHD is higher in boys compared to girls, ADHD in girls is a concern as it is linked to higher rates of lifetime psychopathology (Biederman et al., 2006). A longitudinal investigation of girls ranging from 6 to 18 years followed over five years found girls with ADHD had significantly higher lifetime risk to develop mood and anxiety disorders, substance dependence and disruptive behaviour compared to girls who did not have ADHD (Biederman et al., 2006). The risk o f later development of depression was very high as girls with ADHD were 19 times more likely to develop major depressive disorder by age 16 compared to girls without ADHD (Biederman et al., 2006). High levels of comorbid depression and ADHD has also been found in boys. For example, Drabick, Gadow and Sprafkin (2006) found significant comorbidity of depression and ADHD in a clinic-based sample of 203 boys aged 6 to 10 years. Theories of Depression An important question in depression research is why some individuals are resilient to depression despite major adversity while others respond to relatively minor stressors with intense and prolonged negative affect (Joorman, 2009). This is the question of vulnerability versus resilience in the aetiology of depression (Joormann, 2009). The aetiology of depression is best examined under a stress-diathesis perspective in which the development of depression is viewed as being the result of interactions between individual vulnerabilities and environmental stressors. 13 Genetic theories of depression. In order to investigate genetic influences on the development of depression, researchers have utilized twin, adoption and family studies. Family studies examine clustering of depression within genetically related families (Lau & Eley, 2008). While higher than chance rates of depression in a single family imply heritability, family members also share the same environment, making it difficult for researchers to separate genetic from environmental effects (Lau & Eley, 2008). Twin studies and adoption studies help form a clearer picture of potential genetic and environmental effects on the development of psychopathology. Twin studies are biological quasi­ experiments in which monozygotic twins (MZ) who share all of their genes are contrasted with dizygotic twins (DZ) who share approximately half of their genes (Sullivan et al., 2000). One method of determining the presence of genetic effects is by comparing the concordance rates between MZ twins and DZ twins. If concordance rates of depression are significantly higher among MZ twins than among DZ twins, it is taken as evidence of genetic effects (McGuffin & Katz, 1989). However, it is also important to determine the potential effects of the shared environment. One approach to disentangling genetic and environmental effects involves partitioning the variability into three sources, additive gene effects, unique environmental effects and shared environmental effects (McGuffin & Katz, 1989; Sullivan et al., 2000). This approach has been illustrated by Lau and Eley (2008) and expressed as an equation. MZ twins, who share all genetic material, are represented by (A). DZ twins sharing approximately half their genetic material is represented as (1/2 A). The family environment that is shared by both twins is represented as (C). As both genetic and shared environment contribute to the phenotype of each twin, the resulting equations are expressed to reflect these terms (Lau & 14 Eley, 2008). As a result, correlations between MZ twins are equal to A + C as these twins share all genetics plus their environment. Correlations between DZ are equal to (m A) + C with half their genetics shared in addition to the family environment (Lau & Eley, 2008). A rough estimate of genetic effects (A) is found in the difference in twin correlations between MZ and DZ pairs. Further, any shared resemblance found between MZ pairs that are not accounted for by genetic effects are a result of the shared family environment (Lau & Eley, 2008). Results from twin studies should be considered in context o f their limitations. For example, twin studies assume both types of twins MZ and DZ experience their shared environment in the same way and to the same degree (Lau & Eley, 2008). However, this assumption is vulnerable to violation as MZ twins are more likely to experience a more similar environment in comparison to DZ twins; due not only to the influence o f the shared physical appearance of MZ twins but also as a result of genetically driven behavioural effects on the environment (Lau & Eley, 2008). The argument that MZ twins share a more similar environment as a result of shared genetics comes from studies indicating the social environment is influenced by genetics (Lau & Eley, 2008). For example, in an adoption and twin design Bergeman, Plomin, Pedersen and McCleam (1991) found perceptions of adequacy of social support, measures of depression and life satisfaction receive approximately equal influence by genetics and environment. Consequently, twin studies may artificially inflate heritability as MZ twins share not only genetics but also very similar environments (Lau & Eley, 2008). Adoption studies offer an opportunity to separate the effects of genetic and environmental influences in the development of psychiatric illnesses (Lau & Eley, 2008). In adoptee families, the adopted child shares environment but not genetic factors with the adoptive family, while sharing genetics but not environmental factors with their biological family (Lau & Eley, 2008). Siblings of the adoptee share both environmental and genetics with the adoptive family thus allowing for three different sources of variability, as we have pairings between environment, genetics and environment plus genetics (Lau & Eley, 2008). Comparing correlations between the adoptee and their biological family plus correlations between their adoptive families as well as correlations with their siblings are used to estimate the effects of genetics and environment (Lau & Eley, 2008). In a review of several adoption studies investigating genetic effects on depression, McGuffin and Katz (1989) overall found compelling evidence of genetic effects on the development of depression and an increase in risk of suicidal behaviour among biological relatives. Other researchers have replicated these findings, for example, in a review and meta­ analysis of twin and adoption studies, Sullivan et al. (2000) found strong and consistent evidence of heritability of depression as there is a substantially higher incidence of depression among first-degree relatives of probands diagnosed with depression compared to control participants. However, the extent of heritability of depression is considered to be moderate with concordance rates ranging from r = 0.23 to r = 0.67 for MZ twins and r = 0.14 to r = 0.43 for DZ twins (Sullivan et al., 2000). This lends support to the diathesis-stress perspective as it shows that the environment also contributes to the substantial variance of development of depression (e.g. Bierut et al., 1999; Sullivan et al., 2000). Over the past two decades, specific biological mechanisms in the development of depression have been investigated. Neurobiological studies have found differences in brain functioning, neurochemical, and anatomical differences in the brains of depressed patients compared to controls (Nantel-Vivier & Pihl, 2008). Investigations of potential differences in brain volume and structure between depressed patients and controls is another method of determining possible genetic risk of psychiatric illnesses. For instance, MacMaster et al. (2008) found both children and young adults aged 8 to 21 years old with major depressive disorder had smaller hippocampal volumes compared to matched controls. In order to determine if unaffected high risk individuals with familial history of unipolar depression also had anomalies in hippocampal volumes, Baare et al. (2010) compared healthy twins considered to be high risk due to the occurrence of depression in their co-twin to healthy low risk twins with no known family history of depression. Results found lower hippocampal volumes in healthy high-risk twins compared to low-risk twins (Baare et al., 2010). These studies provide evidence that anomalies in hippocampal volume is likely a diathesis for unipolar depression rather than a symptom or result that only occurs in affected individuals. Researchers have investigated anomalies of the hippocampus in depression research as this structure is involved in affect guidance and regulation (Nantel-Vivier & Pihl, 2008). For example, it has been demonstrated the hippocampus is involved in contextual learning and memory (Fanselow, 2000). These findings are relevant to depression as the disorder may be considered a disorder of the contextual regulation of affect, with inappropriate emotional responding to the situation (Davidson, Jackson, & Kalin, 2000). Intense sadness is appropriate following a significant loss but continued and intense mourning for a year following the loss is not an appropriate emotional response (Davidson, Pizzagalli, Nitschke & Putnam, 2002). According to Davidson et al. (2000) the continued emotional response that persists in absence of acute stressors is considered context-inappropriate and is central to the diagnosis of depression (Davison et al., 2000). In this way, emotional disorders such as 17 depression may be hypothesized as an effect of hippocampal dysfunction (Davison et al., 2002 ). It is also important to note that mood disorders such as depression are likely the result of dysfunction in the neural circuits in the brain which mediate cognitions, feelings and behaviours (Akil, Brenner, Kandel, Kendler, King, & Scolnick et al., 2010). The genetic influence on the functioning of neural circuits is also implicated as thousands of genes are involved in the functioning and regulation of neural circuits, demonstrating a clear link between genetics and the development of mental disorders (Akil et al., 2010). Over the last two decades, research in the field of neuroscience has identified that neurobiological vulnerabilities combined with environmental stress serves as a mechanism for development of depression (Nantel-Vivier & Pihl, 2008). The underlying assumption of neurobiological research is that a causal relationship exists between neurobiological functioning and mood states (Nantel-Vivier & Pihl, 2008). The influence of genetic factors may, however, be different for men and women. In an examination of genetic and environmental influences on depression Bierut et al. (1999) found a significantly different degree of environmental and genetic contributions for men and women. Specifically, the aggregation of depression among female family members was best explained by genetic factors while the aggregation among male family members was less prominent even when accounting for lower prevalence, suggesting the influence of genetics may present a greater liability to the development of depression in women compared to men (Biemt et al., 1999). Cognitive theories of depression. Considerable attention has been given to the role of cognitive processes and their relationship to the development of depression. Cognitive theories of depression investigate the relationship between the mental processes of recognizing, perceiving, reasoning, and judging as these processes have causal implications in the initial development and maintenance of depression (Abela & Hankin, 2008b). According to Ingram, Miranda, and Segal (1998), a central feature of cognitive theories of depression is the diathesis-stress perspective in which depression develops as the result of several different causal factors interacting with the environment. In this view, vulnerability to depression is an internal, stable and latent process that is triggered by a perceived stressful environmental event (Ingram et al., 1998). Consequently certain individuals are predisposed to depression following exposure to negative environmental events (Ingram et al., 1998). Beck’s cognitive theory of depression is also based on the diathesis-stress perspective in which several contributory causes result in depression as a reaction to negative environmental events (Beck, Rush, Shaw, & Emery, 1979). Beck’s cognitive theory of depression focuses largely on the role of negative schemas, or existing memory representations of conceptualizations about the self, the future and the world (Beck et al., 1979). These schemas ultimately act as filtering mechanisms that lead individuals to selectively attend to negative stimuli from the environment. Beck posited that depressogenic schemas are typically characterized by dysfunctional attitudes and beliefs such as “Unless I do everything perfectly I am a failure” (Beck et al., 1979). Depressogenic schemas are driven by feelings of loss, worthlessness, failure and rejection (Beck et al., 1979). Individuals with negatively biased schemas are particularly vulnerable to negative environmental events because they trigger a pattern o f inappropriately 19 biased interpretations or ‘errors in thinking’ about the self, world and future (Abela & Hankin, 2008b). Negative views of the self, world and future form a cognitive triad and result in a cycle of negative processing bias, pessimistic automatic thoughts, and depressed mood (Gotlib & Joormann, 2010). Similar to Beck’s cognitive theory of depression, hopelessness theory is also based on a diathesis-stress perspective and posits that several different causes interact with one another and result in the development of a subtype of depression called hopelessness depression (Abela & Hankin, 2008b). The hopelessness theory examines three distinct cognitive styles. The first is the tendency to attribute negative events to stable, global causes; the second is the tendency to inappropriately perceive that negative events will lead to many disastrous consequences. The third is the tendency to view the self as inherently flawed after negative environmental events occur (Abela & Hankin, 2008b). Cognitive vulnerability and developmental considerations. Empirical tests of the utility of cognitive theories and the aetiology of depression in children and adolescents have yielded mixed results (Abela & Hankin, 2008b; Lakdawalle, Hankin & Mermelstein, 2007). There are several issues regarding the application of cognitive theories of depression for children and adolescence. First, the age at which cognitive vulnerability factors emerge and stabilize remains unknown. Second, it is not clear how various developmental patterns in biological, cognitive, emotional and social domains interact with negative environmental events and influence cognitive vulnerability (Abela & Hankin, 2008b). Finally, it is possible that children and adolescents have not yet developed the capacity to experience the types of stable, negative cognitions that are found in adults (Garber, Weiss & Shanley, 1993; Lakdawalla, et al., 2007). According to Garber et al. (1993) studies comparing negative 20 cognitions such as failure-related helplessness and negative self-evaluations in school aged children up to 9 years old suggested children younger than 9 years may not engage in schematic use of causal concepts. Therefore, the development o f stable negative schematic representations of the self, world and future may not reliably emerge until later childhood or early adolescence. This contention has been made by several researchers who hypothesize cognitive vulnerability factors do not moderate the association between negative environmental events and the development of depression until middle childhood to early adolescence (Abela & Hankin, 2008b). Nevertheless, children who experience depression report more negative cognitions in comparison to non-depressed children, although the strength of relationship may be weaker in children than adults as the association between cognitive vulnerabilities and depression has been shown to increase over age (Garber et al., 1993). In a review of 20 longitudinal studies of depression in children and adolescents, the measure of effect size of the interaction between cognitive vulnerabilities and environmental stress occurring with the development of depression increased as the age of the sample increased (Lakdawalla et al., 2007). More specifically, Lakdawalla et al. (2007) found studies investigating the relation between cognitive-vulnerability and stress in the development o f depression among children yielded a small effect size (d =0.15) while the effect size for adolescents {d = 0.22) was moderately larger. It is important to note that the structure and nature of depression may vary across the lifespan and therefore the causes and consequences of depression may be different for children, adolescents and adults (Lakdawalla, et al., 2007). 21 However, the role o f temperament as a mechanism to increase vulnerability to depression has been investigated in adults as well as in children and adolescents (Compas, Connor-Smith & Jaser, 2004). Temperament is defined as relatively stable emotional, behavioural and cognitive style that emerges early in life and is thought to be reasonably consistent across time and situations (Garber, et al., 2009). Investigation o f two temperamental traits, negative emotionality and positive emotionality, have proven useful in understanding the development of anxiety and depressive disorders (Clark & Watson, 1991; Compas et al., 2004; Garber et al., 2009). Positive emotionality is characterized as the tendency toward sociability, sensitivity to reward, energy, approach, and involvement while negative emotionality is defined as the tendency to experience negative emotions such as fear, sadness, anger, anxiety and low sociability (Garber et al., 2009). In a review of studies investigating the differentiation between depression and anxiety disorders, Clark and Watson (1991) found that while anxiety is mainly a state of high negative emotionality, depression is more complex with both high levels of negative emotionality combined with low levels of positive emotionality. It is important to note that a central tenet of the diagnosis of depression is the absence of positive affect and lack of ability to experience pleasurable events (Clark & Watson, 1991). As a result, temperament itself may serve as a diathesis and when combined with negative environmental events, may increase the likelihood of developing depression (Garber et al., 2009). The social environment. The social environment is an important feature of the diathesis-stress model of depression and can serve as either a protective or risk factor in the aetiology and maintenance of many disorders including depression (Grant et al., 2006). 22 Parent-child and fam ily relationships. Parent-child relationships are particularly important in the development of depression in children and adolescents. Research has reliably demonstrated that rates of depression in children of depressed parents are significantly higher compared to a range of other groups (Goodman & Tully, 2008). Rates of depression in adolescents and school aged children who have depressed mothers have been estimated to range from between 20% and 41% with the wide variability explained by other variables such as socio-demographics, the severity of depression in the mother and whether the father is also depressed (Goodman, 2007; Goodman & Tully, 2008). It is important to note that the majority o f research on parental depression has been focused on maternal depression with little attention paid to paternal depression (Connell & Goodman, 2002). The focus on mothers in parental depression research has occurred for a variety of reasons. First, lifetime rates of depression in women are one and a half to three times higher than lifetime rates of depression in men (Goodman & Tully, 2008). Second, research has shown new mothers have a much higher risk of developing depression in comparison to women who have not recently become mothers, indicating onset of motherhood may be a trigger for depression in some women (Nolen-Hoeksema & Hilt, 2009). Finally, mothers are most often the primary caregivers of children and thus have more influence on the development of the child. Cancian and Meyer (1998) found high rates of divorce and separation lead to large numbers of children raised exclusively by mothers in an American sample. Specifically, 73.7% of children were raised by mothers with sole custody, which is the most common arrangement (Cancian & Meyer, 1998). The rates in Canada are even higher with 80% of cases resulting in mother-sole custody (Department of Justice, 2004). 23 The focus on maternal depression appears to be well founded; however, in a review of 134 independent samples, Connell and Goodman (2002) found exposure to maternal depression is more strongly associated with depression and/or anxiety in children in compared to exposure to paternal depression. Potential mechanisms thought to underlie the development of depression in children of depressed mothers include exposure to the mother’s maladaptive cognitions, negative emotions, and negative behaviour (Goodman, 2007). Moreover, social learning processes such as modeling, observational learning and reinforcement are also thought to play a role in the development o f depression as children of depressed mothers may develop cognitions, negative emotionality and behaviours that resemble the depressed parent’s, thus increasing the risk of developing depression (Goodman, 2007; Goodman & Tully, 2008). In addition to acquiring maladaptive cognitions and behaviours through social learning processes, children of depressed parents are exposed to a variety o f inadequate parenting behaviours. In a review of 46 observational studies Lovejoy, Graczyk, O’Hare and Neuman (2000) found evidence that depressed parents display a wide range of negative parenting behaviours. Specifically, Lovejoy et al. (2000) found depressed parents had a weak association with positive behaviour such as play and pleasant interactions, a moderate association with disengagement from the child and a strong association with irritability and hostility towards the child. However, evidence suggests negative parenting behaviour may be state dependent, as the presence of negative and coercive parenting behaviours were more strongly associated with studies focusing on currently depressed parents in comparison to studies focusing on lifetime depression in parents. These results indicate high levels of negative behaviours may reduce when depressive symptoms are not present in the parent 24 (Lovejoy et al., 2000). However, studies focusing on lifetime depression found more negative parenting behaviours in depressed parents compared to never-depressed parents, indicating that children of depressed parents experience more negative parenting overall (Lovejoy et al., 2000 ). It is important to note that negative family environments have consistently been found in studies of depressed children. This perception of negative family environment is found in both child and parental ratings, indicating the perception of a negative family environment is an accurate representation of reality and not simply biased interpretations of depressed children (Garber et al., 2009). Further, negative parenting is associated with depression in children as Barber (1996) found children’s ratings of their parent’s psychological control (i.e. guilt induction, love withdrawal) predict children’s depressive symptoms. Mothers of depressed children also describe themselves as more rejecting and less affectionate compared to mothers of non-depressed children (Garber et al., 2009). Taken together, these results suggest that negative family environments perceived by both the children as well as their parents are an important factor in the development of depression in children. Depression and marital conflict In romantic relationships, depression is associated with poor functioning in several domains resulting in high levels of negative interactions, significant negative relationship events and poor problem solving (Davila, Stroud & Starr, 2009). For example, in a comparison o f currently depressed, past-depressed and neverdepressed women, Hammen and Brennan (2002) found currently depressed women had significantly more recent stressful events and a higher rate of interpersonal conflict compared to never-depressed women. Additionally, interpersonal problems were not limited to the timing of the depressive episode, as previously depressed women also reported significantly 25 less marital satisfaction and more coercive problem solving tactics occurring within the couple compared to never-depressed women (Hammen & Brennan, 2002). In an analysis of a longitudinal Canadian National Population Health Survey, Bulloch, Williams, Lavorato and Patten (2009) found increased proportions of marital transitions into separation or divorce among couples with a member experiencing depression. Further, Bulloch et al. (2009) also found exposure to marital disruption such as separation or divorce subsequently increased the likelihood of the development of depression in previously non­ depressed couples. These results suggest a bidirectional link between depression and marital disruption (Bulloch et al., 2009). Researchers have also shown that conflict between parents is associated with depression in children (Davila et al., 2009). For example, Fear et al. (2009) found that an increased level of interparental conflict was associated with higher levels of depression and/or anxiety symptoms in children and adolescents. Additionally, self-blame for parental conflict in children was also strongly associated with depression/anxiety symptoms (Fear et al., 2009). In an investigation of specific mechanisms underlying interparental conflict and depression in children, O’Donnell, Moreau, Cardemil, and Pollastri (2010) found depression in children was associated with parental rejection or withdrawal, and parental psychological control. Ambiguous parental withdrawal may lead to self-blaming attributions in children that contribute to the development of depressive symptoms (O’Donnell et al., 2010). Shelton and Harold (2008) also found interparental conflict influenced children’s psychological distress through the child-parent relationship, as parents in conflict tend to withdraw from their children’s lives. Overall, conflict between parents tends to influence parent-child 26 relationships which in turn affect children’s well-being in deleterious ways, therefore increasing the child’s risk to develop depression (O’Donnell et al., 2010) Peer relationships. According to Rudolph, Flynn and Abaied (2008) early family disruption such as parental depression or poor parent-child attachment relationships foster maladaptive interpersonal behaviours. Youth with social behavioural deficits tend to produce disturbances in their peer and family relationships which in turn exacerbate depressive symptoms and heighten the risk for further depressive episodes (Rudolph et al., 2008). Consequently, interpersonal factors are an important part of the development, maintenance and consequence of depression in youth (Rudolph et al., 2008). Evidence suggests that depressed youth often encounter conflict and rejection in their relationships and are perceived by themselves and others as having significant impairment in their social skills (Rudolph et al., 2008). The results of observational studies have indicated depressed youth often illicit more negative reactions from their peers and experience less stable friendships (Rudolph et al., 2008). Studies investigating the impact of negative peer evaluations and peer victimization have found they have a negative impact on children’s global sense of self (Cole, 1991) and chronic negative feedback over time aids in the development of negative self-schemas and leads to heightened risk for depression (Cole, Maxwell, Dukewich, & Yosick, 2010). Cole et al. (2010) investigated the association between covert/relational and overt/physical peer victimization and positive and negative self-cognitions in school children. The utility of covert/relational and overt/physical victimization in the prediction of depression was also examined. Covert or relational victimization is characterized by behaviour intended to exclude the victim from peer activities, spreading rumours and withdrawing friendship. Overt /physical victimization includes physical harm or control through threat of physical harm (Cole et al., 2010). The authors noted that the occurrence of overt/physical victimization without covert/relational victimization was rare and children who were subjected to covert/physical victimization were likely to also be subjected to covert/relational victimization. A high level of covert/relational victimization was a significant predictor o f depression and also demonstrated a positive correlation with negative self-cognitions and a negative correlation with positive self-cognitions (Cole et al., 2010). It is interesting to note that Cole et al. (2010) found no significant differences between genders in the strength of the associations between covert/relational victimization, negative self-cognitions and depression. However, girls were more likely to be the targets of covert/relational victimization. The authors note that the higher likelihood of covert/relational victimization may place girls at a higher risk to develop depression (Cole et al., 2010). Further, Rudolph et al. (2008) note that girls are hypothesized to place greater importance on relationships and therefore might have heightened sensitivity to interpersonal problems. In order to further examine the relationship between peer victimization and maladaptive responses, Graham and Juvonen (1998) investigated the types of subjective appraisals made by children who are victimized by their peers and the differential effects on their motivation and responses. Two types of self-blame were investigated, first, characterological self-blame; which is perceived as uncontrollable and stable in relation to the self, and behavioural self-blame which is perceived to be controllable and unstable in relation to the self (Graham & Juvonen, 1998). Interestingly, Graham and Juvonen also found 28 characterological and behavioural self-blame were highly correlated and hypothesized children who blame their victimization on their character may also blame their own behaviour as well. However, higher levels of maladjustment, loneliness and low self-worth were associated with characterological self-blame. The authors note that while the relationship between victimization and maladaptive outcomes are complex, characterological self-blame partly mediates the outcome between peer victimization and maladaptive responses (Graham & Juvonen, 1998). Summary Research shows that several factors in childhood can lead to depression in adolescence and early adulthood. Current theories of depression allow for the identification of specific risk factors over the course of child and adolescent development. For example, research investigating biological theories of depression has shown that depression may be transmitted across generations through genetic inheritance as depression tends to cluster in families (Lau & Eley, 2008). Children of depressed parents are at an increased risk not only through inherited biology but also through shared family environment (Sullivan et al., 2000). Investigations into cognitive theories of depression have shown children with depression experience more negative cognitions compared to children who are not depressed, and the strength o f the relationship has been shown to increase over time (Garber et al., 1993). Child externalizing behaviour problems have demonstrated utility in predicting depression in early adulthood, (Mason et al. 2004). Finally, the social and family environment is also an important feature in the development and maintenance of depression in children by means of parental conflict, parent-child and peer relationships (Grant et al., 2006). 29 The NLSCY allows for the examination of several factors thought to predict depression across child development. Measures of parental depression provide an indication of potential genetic vulnerabilities which may be inherited by the child. Further, the NLSCY provides data which allows for measurement of the social and family environment. Child emotional and behaviour measures are taken from both the parent and through child selfreport. As a result, analysis of data derived from the NLSCY provides an opportunity to measure a broad range of factors over the course of childhood and adolescence and measure the impact of these factors over time in relation to the development of depression. The Present Study The present study is an investigation of the predictive utility of a range of risk factors for the development of depression in adolescence and early adulthood. Three specific research questions were addressed. (1) Can depression in adolescence and young adulthood be predicted by specific social, psychological and genetic risk factors in a population sample of children? (2) Does the inclusion of parents who are not the biological mothers reduce the predictive utility of parental depression in the development of depression in offspring? (3) Are there gender differences in vulnerability to specific childhood risk factors for depression? To answer these questions, regression analyses was used to predict depression in a longitudinal cohort of children at ages 16 to 20 with predictive measures first taken at ages 4 to 8 years and again at ages 10 to 14 years. Regressions were conducted on a mixed sample of biological mothers and other parents as well as with a sub-sample containing only biological mothers and their children. Regressions were performed separately by gender for the mixed sample and the biological mother and child sample. 30 C hapter 3: Methods Methods for the National Longitudinal Survey of Children and Youth (NLSCY) Survey population for the NLSCY. The NLSCY survey is designed to collect information about several factors thought to influence children’s physical, cognitive and emotional development. Data are collected every two years in order to monitor the impact of these factors over time. The NLSCY began in 1994/95 with a target population o f children living across Canada’s 10 provinces. The sample is comprised of Canada’s non­ institutionalized civilian population. The NLSCY sample excludes members of the Canadian Armed Forces, individuals living on Indian reserves or crown lands, and residents of remote regions. The NLSCY sample frame was based on the monthly Labour Force Survey (LFS) that is designed to collect labour market data as well as basic demographic information from a representative Canadian sample conducted by Statistics Canada. The sample design for the LFS is based on a stratified, multistage design that is based on probability sampling in which households or dwellings are the sample units. Each province in Canada is considered an independent sample that is divided into two parts, large cities and rural areas which included small urban centres. Stratification breaks these parts into clusters of households, for example city blocks from which households are selected. The LFS provides an opportunity to select a representative sample of Canadian children for each province and for Canada as a whole. Examination of households in the LFS determined which had children and served as the main component of the NLSCY sample. Once households were selected to participate in the NLSCY, children who were aged 0 to 11 were selected at random (Statistics Canada, 2007). 31 The original cohort sampled in 1994/95 consisted of children aged 0 to 11 years; this original cohort reached ages 6 to 17 years in Cycle 4 and 12 to 23 years in Cycle 7. The original cohort consisted of 22,831 respondents with a child-level response rate of 86.5%. The cumulative response rate for the longitudinal cohort was 67.8% in Cycle 4 and 56.6% in Cycle 7. The present study used data from Cycles 1, 4 and 7, with ages 4 to 8 years, 10 to 14 years and 16 to 20 years. The NLSCY is a probability survey in which the sample has been selected in order to produce estimates that can be referenced to the population. In order to make inferences from a longitudinal survey in which measures are taken from the same children over time, survey weights are used in order to adjust for changes in the population over time (Statistics Canada, 2007b). A survey weight represents the average number of children in the population that a particular respondent represents in the population. Adjustments were made to account for survey non-response and post-stratification so final weights sum to known counts of children in regard to age, sex and province. For SPSS analysis, the current study used non-funnel weights for children in the longitudinal cohort. Non-funnel weights apply to children who belong to the original longitudinal cohort but may not have responded to every cycle (Statistics Canada, 2007b). As a result, non-funnel weights allow the inclusion of respondents who may have missed a cycle but continued to participate in the survey. While reasonable unbiased estimates of population parameters may be produced using survey weights, they do not account for survey design effects such as sample stratification and clustering. In order to construct design-based variance estimates needed to make inferences, the bootstrap replication method is necessary (Roberts, Kovacevic, Phillips & Gentleman, 1999). Bootstrapping is a re-sampling procedure in which repeated samples are drawn from the original sample with replacement from the original sample in order to estimate characteristics of the population (Fox, 1997). While the survey sampling weight represents the number of respondents that are represented in the population by a single respondent and is used to estimate population parameters, the bootstrap weight is used to estimate the sampling error that is associated with the population parameter (Phillips, 2004). Statistics Canada provides a set of 1000 bootstrap weights for the NLSCY. The present study used bootstrap weights provided by Statistics Canada to calculate variance estimates with the statistical program Wes Var 4.3. Data collection. The NLSCY uses paper questionnaires, computer-assisted personal interviewing (CAPI) and computer assisted telephone interviewing (CATI). The use o f CAPI and CATI helps with data quality as it allows for complex flows and edits to be built into the questionnaire and assures that respondents will only be asked questions appropriate for their situation. Data collection consisted of a household component, child component, adult component, youth component and child self-complete questionnaires. The household component portion of the interview provided a list of a household members and their relationship to one another, record tracing information and demographic information. The person most knowledgeable (PMK) about the child was also identified in the household component. The PMKs provided all information regarding the child component as well as information about themselves and their spouses or partners for the adult component of the interview. The youth component was used for child respondents aged 16 years and older. This portion of the interview was completed by the youth whether he/she was living in the family home or elsewhere. Self-complete paper questionnaires were collected for children aged 10 to 33 17 years. Self-complete questionnaires were administered with four separate booklets and consisted of different topics tailored for each age group (Statistics Canada, 2007). Measures. Many of the concepts in the NLSCY are measured with the use of scales which are a series of items that are designed to measure a concept when calculated as a whole. Statistics Canada’s evaluation of scale data occurred in three major steps. A factor analysis was first performed on all scales in order to evaluate the constructs underlying each scale. Scale scores were then calculated based on the factor structure. Finally, reliability measures were calculated using Cronbach’s alpha. Cronbach’s alpha provides a measure of internal consistency of the items based on the covariance of items within the factor. The NLSCY provides a Cronbach’s alpha score across ages for each factor (Statistics Canada, 2007a). The Present Study. A summary o f predictor variables used in the study is displayed in Table 1. The selection of predictor variables was guided by a literature review of childhood risk factors and predictors of depression and the available measures in the NLSCY. The study aimed to specifically investigate heritability, parenting factors, peer relationships and child internalizing and externalizing symptoms as risk factors for the development of depression. The PMK depression scale in the NLSCY provides a measure of depression symptoms of the child’s primary parental figure. Parenting measures in the NLSCY included in the study were parental reported scales on parental nurturance, parental consistency, hostile/ineffective and punitive parenting in Cycle 1 when the children were aged 4 to 8 years. For the current study, hostile/ineffective and punitive/aversive parenting was combined into one measure as together they provide a measure of negative parenting characteristics. Child behaviour measures in the NLSCY selected for this study included hyperactivity/inattention, and 34 Table 1 Outcome Variable fo r Cycle 7 and Predictor Variables fo r Cycle 1 and Cycle 4. Description Variables Type Outcome Variable Child Depression Scale Child self-report at ages 16 to 20 Years Demographic Variables Age Categorical Child age as of Dec 31,1994 Gender Male, Female Categorical PMK Marital Status Categorical With or Without Partner Cycle 1 Predictor Variables PMK Depression Child Emotional Disorder Child Hyperactivity/Inattention Child Aggression (Indirect Aggression, Physical Aggression, Property damage) Positive Interaction Hostile/Punitive Parenting (Hostile/Ineffective and Punitive/Aversive) Consistent Parenting Biological Parent Status Cycle 4 Predictor Variables PMK Depression Child Emotional Disorder Child Hyperactivity/Inattention Child Aggression (Indirect Aggression, Conduct Disorder Property Damage) Friends General Self-Image Parental Nurturance Parental Rejection Parental Monitoring Scale Scale Scale Scale PMK self-report PMK report PMK report PMK report Scale Scale PMK self-report PMK self-report Scale Categorical PMK self-report Both parents or One/Neither Parents Scale Scale Scale Scale PMK self-report Child self-report Child self-report Child self-report Scale Scale Scale Scale Scale Child self-report Child self-report Child report Child report Child report Note: Cycle 1 measures were taken when the children were aged 4 to 8 years. Cycle 4 measures were taken when the children were aged 10 to 14 years. aggressive behaviours. For this study, scales in the NLSCY designed to measure indirect aggression, physical aggression and property damage were combined to provide a single measure of aggression. The emotional disorder scale in the NLSCY was used for a measure of child internalizing symptoms related to sadness and anxiety. In Cycle 1, when the children were aged 4 to 8 years child behaviour and emotional disorder scales were rated by the PMK. In Cycle 4 when the children were aged 10 to 14 years, child behaviour, child emotional disorder and parenting characteristics were rated by the children. Parenting measures in Cycle 4 included parental nurturance, parental monitoring and parental rejection. Child rated self­ esteem and peer relationships were also selected as predictors in the study as these concepts have been identified as factors in the development of depression in children and adolescents. The child’s biological parent status was selected as a predictor to examine the effects of parental loss or separation on the development of depression in youth. Demographic characteristics. The basic demographic characteristics analysed for the sample included child gender, child age, marital status of the PMK and depression scores of the PMK. The analysis of child gender and age allowed for examination of differences in child gender and age between the final sample and the group of participants who subsequently dropped out of the study. The examination of PMK marital status allowed for the investigation of differences in marital status between families who remained in the study and were included in the final sample and participants who dropped out of the study after Cycle 1. The measures of depression for PMKs were also assessed for differences in order to determine if the PMKs who dropped out of the study differed in levels of depression compared to PMKs who remained in the study. 36 The NLSCY has seven categories for marital status; 1 = Now Married, 2 = Commonlaw, 3 = Living with a partner, 4 = Single (never married) 5 = Widowed, 6 = Separated, 7 = Divorced. For analysis, the marital status variable was collapsed into two categories in which respondents were either with or without a partner. Respondents who answered 1 to 3 were with a partner and respondents who answered 4 to 7 were without a partner Measures for the Present Study Cycle 7 youth depression scale. The outcome variable in Cycle 7 is the depression scale for the longitudinal cohort now aged 16 to 20 years. The depression scale used in the NLSCY is a shortened version of the Center for Epidemiological Studies Depression Scale (CES-D). The CES-D is a 20 item questionnaire developed by L.S. Radloff for the Epidemiology Study Center o f the National Institute o f Mental Health. The CES-D is widely used to measure the frequency of depressive symptoms in the general population (Statistics Canada, 2007b). The CES-D has good psychometric properties and high reliability in population samples (Scott & Melin, 1998). The CES-D is also a valid measure of depressive symptoms in adolescent samples. For example, Mojarrad and Lennings (2002) found the CES-D was useful for identifying both major depressive disorder and dysthymia in an adolescent sample with an adjustment of the cut-off points. While the CES-D is not designed for clinical diagnosis, it is based on clinical symptoms of depression (Radloff, 1977). As a result, the CES-D is useful for discriminating between clinical and population samples with a sufficient level of sensitivity to assess severity o f symptoms (Radloff, 1977). Therefore the CES-D may be used to identify serious illness as well as subclinical or minor depression. The identification of subclinical depression is also important as it is often accompanied by clear 37 depressive symptoms and may serve as a risk factor for the development of more serious illness (Ingram & Siegle, 2009). For the NLSCY, Statistics Canada reduced the CES-D to a 12 item questionnaire. Youths aged 16 and 17 years completed the depression scale in the paper questionnaire while youth aged 18 years and older completed the scale in the computer assisted interview questionnaire. Results for the sample for youth aged 16 to 17 years N= 1,344 had good internal consistency (Cronbach’s a = 0.825). The sample of youth aged 18 to 19 years N = 1,531 had similar results (Cronbach’s a = 0.830). Finally for the sample of youth aged 20 to 21 years N = 1,598 internal consistency was also high (Cronbach’s a = 0.833). Poulin, Hand and Boudreau (2005) investigated validity for the shortened version of the CES-D used in the NLSCY in a sample of 12,990 Canadian adolescents. The authors found the scale had good internal consistency with Cronbach a = 0.85; as well as acceptable discrimination validity with the exception of one item. Specifically, the item “I felt hopeful about the future” demonstrated poor discrimination ability among the elevated, somewhat elevated and minimal categories of depression. Poulin et al. also assessed construct validity and compared the NLSCY shortened CES-D scale items to the diagnostic criteria used in the Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV). The shortened CES-D covers 6 out of the 9 possible depression symptoms covered in the DSM-IV. The authors noted the shortened CES-D scale is most heavily weighted toward affective symptoms of depression and also includes most of the somatic symptoms of depression. However, the shortened scale does not include a measure of irritability. The exclusion of irritability is a noteworthy limitation as the DSM-IV-TR states depressed children and adolescents may present with irritable mood rather than sadness (DSM-IV-TR, 2000). Research has demonstrated 38 irritability is a common characteristic of adolescent depression. For example, in a sample of 121 adolescents attending an outpatient mental health service Crowe, Ward, Dunnachie and Roberts (2006) found irritability was among the most common reported symptoms in depressed boys and girls. The specificity of irritability as a predictor of depression has also been supported. In a cross-sectional investigation of three dimensions of oppositionality in youth Stringaris and Goodman (2009) found irritability reported by parents and teachers was the only dimension of oppositionality associated with emotional disorders in youth. Cycle 1 predictor variables. Cycle 1 measures are collected for children aged 4 to 8 years old. Parental Depression. The person most knowledgeable (PMK) about the child was administered the shortened version of the CES-D depression scale which is comprised of the same items used to measure the outcome variable for youth depression Cycle 7. Statistics Canada’s tests for reliability measures for the shortened CES-D scale in adults yielded good results in for the sample of N = 13,140 PMKs (Cronbach’s a = 0.807). Behaviour scales. Child behaviour scales were developed by Statistics Canada to measure certain aspects o f behaviour in children aged 4 to 11 years. Child behaviour is assessed through parent report for children aged 2 to 11 years. Self-complete questionnaires were used to measure behaviour for children ages 12 and older (Statistics Canada, 2007b). Emotional Disorder/Anxiety scale. Items used for emotional disorder/anxiety included items from the Ontario Child Health Study (OCHS). Analysis conducted by Statistics Canada demonstrated good reliability for children aged 4 to 11 years old (Cronbach’s a = 0.756). Hyperactivity/Inattention scale. The hyperactivity scale is utilized to identify hyperactive, inattentive as well as impulsive behaviours in population based studies (Statistics Canada, 2007a). Items for the hyperactivity/inattention scale were taken from the OCHS and the Montreal Longitudinal Study (Statistics Canada, 2007a). The NLSCY parent reported hyperactivity/inattention scale was evaluated in order to determine the usefulness of the scale to identifying clinically significant Attention Deficit Hyperactivity Disorder (ADHD) in population studies. In an analysis of children aged 6 to 11 in the NLSCY Cycle 1 data, Charach, Lin and To (2010) found high scores on the NLSCY hyperactivity/inattention scale were associated with current methylphenidate use and diagnosed emotional disorder. These findings indicate that the NLSCY scale has utility for identifying ADHD symptoms in large population studies. Statistics Canada tests for reliability showed acceptable internal reliability for children aged 4 to 11 years (Cronbach’s a = 0.838) (Statistics Canada, 2007a). Indirect Aggression scale. Items for the parent reported indirect aggression scale were taken from Lagerspetz, Bjomgvist and Peltonen of Finland (Statistics Canada, 2007a). Reliability analysis conducted by Statistics Canada for children aged 4 to 11 year olds yielded good reliability (Cronbach’s a = 0.781). Physical Aggression - Conduct Disorder scale. Items for the NLSCY physical aggression - conduct disorder scale were taken from the OCHS and also demonstrated good reliability (Cronbach’s a = 0.770) (Statistics Canada, 2007a). Property Damage scale. A separate factor emerged from the factor analysis related to property offences. Reliability analysis conducted by Statistics Canada demonstrated acceptable reliability for 4 to 11 year olds (Cronbach’s a = 0.637) (Statistics Canada, 2007a). Parenting Scales. The parenting scales in the NLSCY were designed to measure positive interaction, hostile/ineffective parenting and consistent parenting. The NLSCY items for positive interaction, ineffectiveness and coherence were based on the work of Dr. K 40 Dodge of Vanderbilt University which was an adaptation of the Parent Practices Scale of Strayhom and Weidman (Statistics Canada, 2007a). In addition, two items designed to measure aversive and non-aversive parenting styles for children aged 2 to 11 years were provided by Dr. M. Boyle of Chedoke-McMaster Hospital (Statistics Canada, 2007a). Positive Interaction scale. Statistics Canada conducted reliability measures across ages 2 to 9 year olds in Cycle 1. Reliabilities were good for each age group; for ages 4 to 5 years, Cronbach’s a = 0.718, for ages 6 to 7, Cronbach’s a = 0.716, and for ages 8 to 9 Cronbach a = 0.721 (Statistics Canada, 2007b). Hostile/Ineffective Parenting scale. Reliability measures for ineffective parenting were acceptable for ages 4 to 5 years (Cronbach’s a = 0.664) and for ages 6 to 7 (Cronbach’s a = 0.610) and for ages 8 to 9 (Cronbach’s a = 0.672) (Statistics Canada, 2007b). Consistent Parenting scale. Reliability measures for consistent parenting were slightly lower; for ages 4 to 5 years old Cronbach’s a = 0.631 for ages 6 to 7, Cronbach’s a = 0.508 and for ages 8 to 9, Cronbach’s a = 0.543 (Statistics Canada, 2007b). Biological Parent Status. This measure provides information on the living situation of the child in respect to his or her biological parents. The NLSCY has three categories for biological parent status; 1 = child lives with both biological parents, 2 = child lives with one biological parent, 3 = child lives with neither biological parent. For this study, biological parent status was recoded into 0 = child lives with both biological parents, 1 = child lives with one, or neither biological parents. Cycle 4 Predictor Variables. Cycle 4 measures were taken from children in the longitudinal cohort who were between the ages of 10 to 14 years old. At Cycle 4 parental depression was assessed in the parent questionnaire with the same items used in Cycle 1. 41 Behaviour scales. Child self-complete questionnaires measured self-reported child behaviour, self-esteem, peer relationships and parental behaviours. Emotional Disorder/Anxiety scale. Reliability was calculated for emotional disorder/anxiety across ages. Reliability for children aged 10 to 11 years was good, (Cronbach’s a = 0.717) (Statistics Canada, 2007a), as was for 12-13 year olds (Cronbach’s a = 0.781) and 14 to 15 year olds (Cronbach’s a = 0.793) (Statistics Canada 2007b). Hyperactivity/Inattention scale. Reliability calculated for children ages 10 to 11 years was good (Cronbach’s a = 0.717) (Statistics Canada, 2007a), as was for 12 to 13 year olds (Cronbach’s a = 0.783) and 14 to 15 year olds (Cronbach’s a = 0.790) (Statistics Canada, 2007b). Indirect Aggression scale. Reliability calculated for the indirect aggression scale for children ages 10 to 11 years was acceptable (Cronbach’s a = 0.657) (Statistics Canada, 2007a), as well as for 12 to 13 year olds (Cronbach’s a = 0.742) and for 14 to 15 year olds (Cronbach’s a = 0.726) (Statistics Canada, 2007a). Conduct Disorder scale. Reliability calculated for the conduct disorder scale for children ages 10 to 11 years was adequate (Cronbach’s a = 0.678) (Statistics Canada, 2007a). Reliability were slightly better for older children; 12 to 13 year olds (Cronbach’s a = 0.759) and for 14 to 15 year olds (Cronbach’s a = 0.817) (Statistics Canada, 2007b). Property Offence scale. Property offence scores were calculated for 4 to 9 year olds and yielded acceptable reliability (Cronbach’s a = 0.612) (Statistics Canada, 2007b). Reliability analysis was also conducted for older children and yielded adequate results for 12 to 13 year olds (Cronbach’s a - 0.672) and for 14 to 15 year olds (Cronbach’s a = 0.768) (Statistics Canada, 2007b). 42 Friends scale. This scale is designed to measure the quality of the child’s peer relationships. Items for the friends sale were taken from the Peer Relations Subscale in the Marsh Self-descriptive Questionnaire (SDQ) developed by H.W. Marsh (Statistics Canada, 2007b). Reliability analysis was performed for children aged 10 to 11 years and demonstrated good internal consistency (Cronbach’s a = 0.779) (Statistics Canada, 2007a). Reliability measures were also good for ages 12 to 13 year olds (Cronbach’s a = 0.824) and for ages 14 to 15 year olds (Cronbach’s a = 0.844) Statistics Canada, 2007b). General Self-Image scale. This scale provides a measure of the youth’s overall self­ esteem. Items for the general self-image scale were taken from the SDQ (Statistics Canada, 2007a). The SDQ was shortened to 4 items for use in the NLSCY and yielded good reliability measures. Analysis was performed for children aged 10 or 11 years. The scale demonstrated adequate internal consistency (Cronbach’s a - 0.728). Reliability was also assessed for ages 12 to 13 years olds (Cronbach’s a = 0.797) and ages 14 to 15 years old (Cronbach’s a = 0.831) (Statistics Canada, 2007b). My Parents and Me scale. This scale was designed to provide information about the child’s perception of his/her relationship with their parents. The scale used in the NLSCY were also used in the Western Australia Child Health Survey (Statistics Canada, 2007a) and was developed by Lempers, Clark-Lempers and Simons (1989) to measure parenting behaviours nurturance, rejection and monitoring (Statistics Canada, 2007a). The parenting scale is based on the work of Schaefer (1965) and Roberts et al (1984) (Statistics Canada, 2007a). Schaefer’s original questionnaire consisted of 29 items and demonstrated good internal consistency (Cronbach’s a = 0.80) for a sample of 622 adolescents (Lempers et al., 1989). The NLSCY parenting questionnaire excluded 10 items from the original 43 questionnaire 6 items for nurturance, 3 for rejection and 1 for monitoring; resulting in a 19 item scale (Rubab, Shapka, Dahinten &01son, 2011). The shortened NLSCY questionnaire consisted of 7 items for nurturing, 7 items for rejection and 5 items for monitoring (Statistics Canada, 2007b). Parental Nurturance. The shortened parental nurturance scale consisted of 7 items and yielded good internal consistency for ages 12 to 13 (Cronbach’s a = 0.893) and for ages 14 to 15 year olds (Cronbach’s a = 0.927). Parental rejection. The parental rejection scale consisted of 7 items and also had good internal consistency for ages 12 to 13 years old (Cronbach’s a = 0.741) and for ages 14 to 15 (Cronbach’s a = 0.757). Parental Monitoring. Parental monitoring contained 5 items. Parental monitoring scale had lower levels of internal consistency for ages 12 to 13 (Cronbach’s a = 0.506) and for ages 14 to 15 years (Cronbach’s a = 0.390) (Statistics Canada 2007b). Chapter 4: Results Missing data analysis was conducted to assess sample representativeness and nonrandom drop out. Descriptive analyses were conducted separately by gender for the mixed sample and the biological mother and child sample. See Appendix D for descriptive analysis for the mixed sample for boys and Appendix E for the mixed sample for girls. Descriptive analyses for the biological mother and child sample for boys are displayed in Appendix F and girls in Appendix G. Correlations between Cycle 1 variables are displayed in Appendix H. Correlations between Cycle 4 variables are displayed in Appendix I. Finally, correlations for Cycle 1 and Cycle 4 variables are displayed in Appendix J. 44 Regression analysis was performed on the mixed sample, followed by separate regressions for each gender. Finally, regressions were conducted for the sub-sample of biological mothers and their children, followed by separate regressions for each gender. The distributions of depression scores in the mixed sample for analysis are displayed for the overall sample, and separately for boys and girls in Appendix C. Sample Characteristics and Representativeness For the regression analysis, only participants with complete data on all variables were included in the sample. Respondents who had dropped out of the study before Cycle 7 or had missing data on the outcome variable or any predictor variables were not included in the analysis. To examine for differences between the sample used for analysis (n = 1715) and participants missing from the analysis (n = 7140) Chi square tests were performed on child age, child gender, and PMK marital status for Cycle 1 to assess sample representativeness and non-random drop out of participants. Marital status was examined as researchers investigating sample attrition in longitudinal studies found participants who were not cohabitating with a steady partner were more likely to be lost to follow up for a second wave of data collection (Graaf, Bijl, Smit, Ravelli, & Vollebergh, 2000; Radler & Ryff, 2010). Analysis for differences in depression scores between participants in the complete sample and participants who were missing was also conducted as PMK depression is an important predictor variable in the current study. Further, research has demonstrated participants with higher depression scores at the beginning of a longitudinal study are more likely to be lost for subsequent waves of data collection (Farmer, Locke, Liu, Moscicki et al, 1994). 45 Results for the missing data analysis are displayed in Table 2. Chi Square analysis was performed to assess for differences in child gender, child age and PMK marital status in Cycle 1. The chi square for child age revealed that the complete sample was slightly older than the missing sample with proportionately more 7 and 8 year olds remaining in the study compared to 4, 5, and 6 year olds. The analysis for child gender revealed that the complete sample was made up of slightly more girls compared to boys. Analysis of marital status indicated proportionately more PMKs in the complete sample were with a partner compared to PMKs who were without a partner (see Table 2). A t test was performed to investigate potential differences in PMK depression scores between participants who were missing from the sample and participants who made up the complete sample. Results indicated there were no significant differences in depression scores between PMKs who were missing from the sample, 95% Cl [0.57, 0.61] and PMKs in the complete sample, 95% Cl [0.55, 0.61], Overall, the sample retained for analysis is limited in generalizability to the Canadian population as it contains an overrepresentation o f older children, girls and married PMKs compared to the overall representative sample. 46 Table 2 Child Age, Child Gender, PMK Marital Status and PMK Depression for Missing and Complete Samples. Missing Complete Proportions 95% Cl Proportions 95% Cl 21.70% 20.79% 20.17% 18.67% 18.66% [21.06, 22.35] [20.10,21.48] [19.52, 20.82] [18.01, 19.34] [17.95, 19.38] 15.96% 18.45% 17.57% 23.28% 24.74% [13.45, 18.47] [15.68,21.22] [15.01,20.13] [20.57, 25.99] [21.88, 27.60] 52.55% 47.45% [51.7, 53.4] [46.6, 48.3] 45.7% 54.3% [42.35, 49.05] [50.95, 57.65] 83.38% 16.62% [81.75,85.01] [14.99, 18.25] 86.86% 13.14% [83.98, 89.74] [10.26, 16.02] Characteristics Child Age 4 5 6 7 8 Child Gender Boys Girls PMK Marital Status With Partner Without Partner Note. Proportions are shown for categorical variables. Cl = confidence interval. Respondents with complete data had data on all predictor variables and the dependent variable (n = 1715). Respondents with missing data are those who dropped out of the study after Cycle 1 or remained in the study but did not have data on all predictor variables or the dependent variable (n = 7140). 47 Regression Analysis. Regression analysis was first performed on the mixed sample which included PMKs who were biological mothers mixed with PMKs who were not biological mothers. The first step of the regression analysis indicated gender was a significant predictor o f depression. As a result, separate regressions were conducted for each gender. Results for the first step of the regression for the mixed sample are discussed followed by regression results conducted separately for boys and girls. Results for the regression analysis for the biological mother and child sub-sample are discussed for the first step of the regression followed by separate results for boys and girls. Evaluation of assumptions was performed using SPSS Explore. The results led to the decision to transform the outcome variable youth depression and several of the predictor variables to reduce skewness and improve normality, linearity and homoscedasticity of residuals. The outcome measure of youth depression was positively skewed with the majority of scores on the lower end of the scale. A square root transformation resulted in acceptable normality of the distribution. Logarithmic transformation was performed on the PMK depression scales for Cycle 1 and Cycle 4 as well as the PMK-rated child emotional disorder scale for Cycle 1 due to positive skeweness. A logarithmic transformation was also performed on the combined scores for indirect aggression, and property damage and Conduct Disorder for Cycle 1 and Cycle 4. The friends and self-esteem scales in Cycle 4 were negatively skewed and were reflected and logarithmic transformed. Square root transformations were performed on hyperactivity scores for Cycle 1 and child-rated emotional disorder scales for Cycle 4. Lastly, child rated parental consistency 48 for Cycle 1 and parental rejection scores for Cycle 4 were moderately negatively skewed and were reflected and square-root transformed resulting in acceptable normality. Mixed sample regressions. The first step of the hierarchical regression model for the mixed sample (N = 1715) revealed youth depression was predicted by gender P = 0.22, p = .001, 95% Cl [0.38, 0.64] and age p = -0.26, p = 001, 95% Cl [-0.26, -0.16] indicating depression rates are higher for adolescents compared to the young adults; and girls experience more symptoms of depression compared to boys. Results for the mixed sample regression model not separated by gender are displayed in Appendix A. Results for the hierarchical regression for boys (n = 796) are displayed in Table 3. For boys, 24% of the overall variance in depression was explained by predictors in the model (R2 = 0.24). Age significantly predicted depression (P = -0.34,/? < .001), 95% Cl [-0.35, -0.19] indicating within the age range of 16 to 20 years, depression scores for boys were higher in the adolescent age range compared to the young adults. The second step added Cycle 1 variables to the model and significantly predicted depression. When the boys were aged 4 to 8 years old higher levels of parent-reported child emotional disorder scores predicted higher depression scores when the boys were aged 16 to 20, P = 0.13,/? = .008, 95% Cl [0.14, 0.91]. The third step added Cycle 4 variables and significantly predicted depression. In Cycle 4, when boys were aged 10 to 14 years lower self-esteem scores predicted higher depression scores when the boys were aged 16 to 20 (p = 0.13,/? = .044), 95% Cl [0.01, 0.96]. Higher child emotional disorders also predicted higher depression scores for boys P = 0.26,/? < .001, 95% Cl [0.18, 0.46], 49 Table 3 Results fo r hierarchical multiple regression fo r predicting youth depression in the mixed sample fo r Boys. Predictor Variable R2 R2change Step 1 Age 0.11 0.11 B fi sr2 Fchangfdf) 102.43*** (1,794) -0.27 -0.34*** -0.34 Step 2 0.14 0.02 Age Cl PMK Depression (L) Cl Hyperactivity (SR) Cl Emotional Disorder (L) Cl Aggression (L) Cl Positive Interaction Cl Parental Consistency (RSR) Cl Hostile Punitive Cl Biological Parent 13.77*** (9,786) -0.28 -0.07 -0.08 0.47 0.19 -0.01 0.02 -0.006 0.11 -0.34*** 0.02 -0.07 0.13** 0.06 -0.03 0.01 -0.07 0.04 -0.32 -0.02 -0.05 0.11 0.05 -0.02 0.01 -0.05 0.04 Step 3 0.24 0.10 Age Cl PMK Depression (L) Cl Hyperactivity (SR) C l Emotional Disorder (L) Cl Aggression (L) Cl Positive Interaction Cl Parental Consistency (RSR) Cl Hostile Punitive Cl Biological Parent C4 PMK Depression (L) C4 Friends (RL) C4 General Self (RL) C4 Emotional Disorder (SR) C4 Hyperactivity (SR) C4 Aggression/CD (L) C4 Parent Nurturance (SR) C4 Parent Monitoring (SR) C4 Parental Rejection C4 Biological Parent -0.26 -0.04 -0.10 0.40 0.16 -0.01 0.02 -0.01 0.12 0.02 0.05 0.48 0.32 -0.05 -0.06 -0.04 0.10 0.02 -0.05 -0.33*** -0.02 -0.08 0.11* 0.05 -0.43 0.01 -0.07 0.04 0.01 0.01 0.13* 0.26*** -0.04 -0.02 -0.03 0.06 0.07 -0.02 -0.23 -0.01 -0.07 0.09 0.04 -0.03 0.01 -0.05 0.03 0.01 0.01 0.10 0.21 -0.03 -0.01 -0.03 0.05 0.06 -0.01 12.74*** (19,776) 50 Notes: Cl = Cycle 1 variables, C4 = Cycle 4 variables. SR = square root transformation. RSR = reflect and square root transformation. L = logarithmic transformed. RL = reflect and logarithmic transformed. * p < .05 ** p < .01 * * * p < .001 51 Regression results for girls in the mixed sample (n = 919) are displayed in Table 4. The overall variance explained by the model for girls was 15% (R2 = 0.15). The first step of the model demonstrated age significantly predicted depression (P = -0.20, p = .001), 95% Cl [-0.23, -0.09] indicating the younger girls in the age range 16 to 20 reported higher depression scores compared to the older girls. The second step added Cycle 1 variables to the model and significantly predicted depression. When the girls were aged 4 to 8 years old higher levels of PMK depression significantly predicted higher depression symptoms for girls 12 years later when they were aged 16 to 20 years (p = 0.11 ,p = .025), 95% Cl [0.04, 0.59]. Girls who were not living with both biological parents by ages 4 to 8 years had an increased risk of higher depression scores (P = 0.12, p = .008), 95% Cl [0.09, 0.62]. The third step o f the model added Cycle 4 variables and significantly predicted depression. In Cycle 4 when the girls were aged 10 to 14 years higher emotional disorder scores predicted higher depression scores at ages 16 to 20 years (P = 0.13, p = .014), 95% Cl [0.03, 0.3]. Finally, girls with higher self-rated aggression and conduct disorder scores were at increased risk to develop depression (P = 0.08,p = .018), 95% Cl [0.01, 0.07]. 52 Table 4 Results fo r hierarchical multiple regression fo r predictin g youth depression in the mixed sample fo r girls. Predictor Variable R2 R2 change Step 1 Age 0.04 0.04 Step 2 0.08 0.04 Age Cl PMK Depression (L) C 1 Hyperactivity (SR) Cl Emotional Disorder (L) Cl Aggression (L) Cl Positive Interaction Cl Parental Consistency (RSR) Cl Hostile Punitive Cl Biological Parent Step 3 0.15 0.07 Age Cl PMK Depression (L) Cl Hyperactivity (SR) Cl Emotional Disorder (L) Cl Aggression (L) Cl Positive Interaction Cl Parental Consistency (RSR) Cl Hostile Punitive Cl Biological Parent C4 PMKs Depression (L) C4 Friends (RL) C4 General Self (RL) C4 Emotional Disorder (SR) C4 Hyperactivity (SR) C4 Aggression/CD (L) C4 Parent Nurturance (SR) C4 Parent Monitoring (SR) C4 Parental Rejection C4 Biological Parent B P sr2 Fchange(<#) 38.67*** (1,917) -0.16 -0.20*** -0.20 9.07*** (9,909) -0.17 0.31 0.02 0.09 -0.03 -0.01 -0.06 0.006 0.35 -0.21*** 0.11* 0.02 0.02 -0.01 -0.04 -0.04 0.03 0.12** -0.20 0.10 0.02 0.02 -0.01 -0.03 -0.04 0.02 0.12 8.58*** (19,899) -0.23 0.22 -0.005 0.08 -0.08 -0.007 -0.06 -0.002 0.30 0.28 0.06 0.18 0.17 -0.04 0.27 -0.06 0.15 0.02 0.01 -0 29*** 0.08 -0.004 0.02 -0.03 -0.02 -0.04 -0.009 0.12 0.10 0.02 0.05 0.13** -0.03 0.08** -0.05 0.09 0.10 0.005 -0.24 0.06 -0.01 0.02 -0.02 -0.02 -0.04 -0.01 0.08 0.09 0.01 0.04 0.10 -0.03 0.07 -0.04 0.08 0.08 0.01 53 Notes: Cl = Cycle 1 variables, C4 = Cycle 4 variables. SR = square root transformation. RSR = reflect and square root transformation. L = logarithmic transformed. RL = reflect and logarithmic transformed. * p < .05 * *p < .01 *** p < .001 54 Biological mother and child regressions. In order to investigate potential hereditability effects of maternal depression on offspring, a separate regression analysis was performed with a sub-sample o f children whose PMK was their biological mother in both Cycle 1 and Cycle 4 (N = 1483). The first step of the hierarchical regression model for the biological mother and child sample revealed depression was predicted by gender (p = 0.24, p > .001), and age (P = -0.28, p < .001) indicating girls reported higher rates of depression compared to boys and depression symptoms were higher for adolescents compared to young adults. Results for the entire biological mother and child sample are displayed in Appendix B. Regression analysis was again conducted separately for each gender. For boys (n = 686) findings were similar to the mixed sample except child-reported parental rejection when boys were aged 10 to 14 years also predicted depression (P = 0.12, p = .02), 95% Cl [0.01, 0.05] with higher rejection scores predicting higher depression scores (see Table 5). Slightly more variance was explained by the model for depression scores in boys in the biological mother and child sample compared to the mixed sample (R = 0.25). 55 Table 5 Results fo r hierarchical multiple regression fo r predicting youth depression in biological mother-child dyads fo r boys. Predictor Variable R2 R2 change Step 1 Age 0.12 0.12 Step 2 0.14 0.03 Age Cl PMK Depression (L) Cl Hyperactivity (SR) Cl Emotional Disorder (L) C l Aggression (L) Cl Positive Interaction Cl Parental Consistency (RSR) Cl Hostile Punitive Cl Biological Parent Step 3 0.24 0.10 Age Cl PMK Depression (L) Cl Hyperactivity (SR) Cl Emotional Disorder (L) C 1 Aggression (L) Cl Positive Interaction Cl Parental Consistency (RSR) C 1 Hostile Punitive C 1 Biological Parent C4 PMK Depression (L) C4 Friends (RL) C4 General Self (RL) C4 Emotional Disorder (SR) C4 Hyperactivity (SR) C4 Aggression/CD (L) C4 Parent Nurturance (SR) C4 Parent Monitoring (RSR) C4 Parental Rejection C4 Biological Parent B P sf -0.27 -0 34*** -0.34 -EchangeO^O 88.56*** (1,684) 12.60*** (9,676) -0.28 -0.12 -0.08 0.56 0.24 -0.001 0.004 -0.02 0.10 -0.35*** -0.04 -0.07 0.16** 0.08 -0.004 0.002 -0.07 0.03 -0.32 -0.04 -0.06 0.13 0.06 -0.01 0.01 -0.05 0.03 11.85*** (19,666) -0.25 -0.08 -0.10 0.48 0.21 -0.01 0.02 -0.02 0.18 -0.01 0.07 0.56 0.31 -0.07 -0.16 -0.04 0.04 0.03 -0.12 -0.31*** -0.03 -0.08 0.13 0.07 -0.02 0.01 -0.08 0.06 -0.003 0.02 0.15* 0.25*** -0.05 -0.05 -0.03 0.02 0.12* -0.05 -0.27 -0.02 -0.07 0.11 0.05 -0.01 0.01 -0.06 0.04 -0.01 0.02 0.12 0.20 -0.04 -0.04 -0.02 0.02 0.10 -0.03 56 Notes: Cl = Cycle 1 variables, C4 = Cycle 4 variables. SR = square root transformation. RSR = reflect and square root transformation. L = logarithmic transformed. RL = reflect and logarithmic transformed. * p < . 05 ** p < .01 * * * p < .001 For girls, the biological mother and child sample (n = 797) revealed additional predictors o f depression (see Table 6). The model explained 21% of the overall amount of variance in depression scores for girls. Higher maternal depression scores emerged as a significant predictor for depression in girls when they were aged 4 to 8 years (P = 0.12,/? = .03), 95% Cl [0.03, 0.64] and 10 to 14 years (p = 0.14, p = .015), 95% Cl [0.07, 0.67], Additionally, at ages 10 to 14 years higher child-rated parental rejection scores (P = 0.10, p = .047), 95% Cl [0.00, 0.05] and lower child-rated parental monitoring scores (P = 0.14, p = .002), 95% Cl [0.09, 0.40] increased girls risk for depression at ages 16 to 20 years. Summary. Overall results indicate that age is a significant predictor for both boys and girls with adolescents reporting more depression compared to young adults. Internalizing symptoms of depression and anxiety emerged as an important risk factor for boys in both childhood and adolescence. Symptoms of depression and anxiety predicted depression for girls only at ages 10 to 14 years. For girls, the loss of a biological parent in early childhood predicted depression in both the mixed and biological sample. Some differences were found between the mixed sample and the biological mother and child sample. Specifically, parental depression predicted depression for girls however, in the biological mother and child sample maternal depression emerged as a stronger predictor o f depression for girls. Parental characteristics predicted depression in the biological mother and child sample with higher parental rejection predicting depression for both boys and girls and lower parental monitoring scores predicting depression for girls. It is interesting to note results indicate predictors of depression better explain the variance in depression scores for boys compared to girls. Interestingly, there is no change in variance explained for boys in the mixed sample compared to the biological mother and child sample. The overall amount of variance explained for girls is lower in both samples. However, the biological mother and child sample better explains the variance in depression for girls compared to the mixed sample. Overall, depression in boys is better explained by predictors in the model, and depression in girls has stronger ties to heredity. 59 Table 6 Results fo r hierarchical multiple regression fo r predicting youth depression in biological mother-child dyads fo r girls. Predictor Variable R2 R2 change Step 1 Age 0.06 0.06 Step 2 0.11 0.05 Age Cl PMK Depression (L) Cl Hyperactivity (SR) Cl Emotional Disorder (L) Cl Aggression (L) Cl Positive Interaction C l Parental Consistency (RSR) Cl Hostile Punitive C l Biological Parent Step 3 0.21 0.10 Age C 1 PMK Depression (L) Cl Hyperactivity (SR) Cl Emotional Disorder (L) Cl Aggression (L) C l Positive Interaction C l Parental Consistency (RSR) Cl Hostile Punitive Cl Biological Parent C4 PMK Depression (L) C4 Friends (RL) C4 General Self (RL) C4 Emotional Disorder (SR) C4 Hyperactivity (SR) C4 Aggression/CD (L) C4 Parent Nurturance (SR) C4 Parent Monitoring (RSR) C4 Parental Rejection C4 Biological Parent B (3 sr2 Fchangfdf) 50.89*** (1,795) -0.55 0.24*** -0.25 11.25*** (9,787) -0.20 0.34 -0.03 0.25 -0.01 -0.03 -0.01 0.002 0.37 -0 28*** 0.12* -0.02 0.07 0.003 -0.06 -0.01 0.01 0.13** -0.25 0.10 -0.02 0.06 -0.01 -0.06 -0.01 0.01 0.13 10.85*** (19,777) -0.23 0.21 -0.06 0.28 -0.06 -0.02 0.01 -0.01 0.40 0.34 0.14 0.18 0.25 -0.11 0.22 -0.08 0.24 0.03 -0.10 -0.35*** 0.07* -0.05 0.08 -0.02 -0.05 0.01 -0.03 0.14 0.14** 0.04 0.05 0.18** -0.08 0.07* -0.07 0.14** 0.10* -0.04 -0.29 0.06 -0.04 0.07 -0.02 -0.04 0.01 -0.03 0.09 0.12 0.03 0.04 0.14 -0.06 0.06 -0.05 0.12 0.09 -0.02 60 Notes: Cl = Cycle 1 variables, C4 = Cycle 4 variables. SR = square root transformation. RSR = reflect and square root transformation. L = logarithmic transformed. RL = reflect and logarithmic transformed. * p < .05 ** p < .01 *** p < .001 61 Chapter 5: Discussion The purpose of this study was to investigate a wide range of risk factors from early childhood to young adulthood that are thought to predict depression in a population sample of children and youth. The study also investigated gender differences in the importance and relevance of predictors of depression. In addition, analyses were conducted separately to examine for potential differences between a biological sample of mother-child dyads compared to a sample of biological mothers mixed with other caregivers. Overall, results indicate a range of factors across childhood are useful indicators of risk for the development of adolescent and early adult depression. Gender differences and similarities in the importance of risk factors for depression were found. Results also revealed differences in the effect of predictors of depression between the biological mother-child sample and the mixed sample of caregivers. Significant findings for the mixed sample are discussed for both boys and girls followed by the discussion of findings for boys and girls in the biological mother and child sample. Mixed Sample Analyses Age. For both genders, depression scores were higher in the lower age ranges of 16 to 20 years. These results are similar to past research investigating rates of depression in adolescents. For example, in a 10 year longitudinal investigation, Hankin, Abramson, Moffitt, Silva, McGee and Angell (1998) found the highest increase for new cases of depression and overall rates of depression peaked for both boys and girls between the ages of 15 to 18 years. These results indicate mid to late adolescence may be a particularly vulnerable period for the development of depression for both boys and girls. 62 Depression and anxiety. Boys who experience higher levels of depression and anxiety at ages 4 to 8 years and at ages 10 to 14 years were at increased risk to develop depression at ages 16 to 20 years. These results indicate boys who experience elevated levels of anxiety and depression symptoms across childhood and adolescence are particularly vulnerable to develop later depression. These results are in line with a recent study comparing gender differences in the effects of anxiety disorders and risk of depressive episodes. For example, Hettema, Kuhn, Prescott and Kendler (2006) found males with generalized anxiety disorder had a greater increase in risk for depressive episodes compared to women, especially when coupled with stressful life events. Self-rated emotional disorder scores also emerged as a predictor for girls at ages 10 to 14 years indicating anxiety symptoms across childhood may be a potential pathway to the development o f depression for both genders. Self-Esteem. Lower levels of self-esteem at ages 10 to 14 years predicted higher depression scores 6 years later for boys but not for girls. There is some evidence to suggest boys who experience internalizing symptoms and low self-esteem may be particularly vulnerable to depression. For example Derdikman-Eiron, Indredavik, Bratberg, Taraldsen, Bakken and Colton (2011) found associations between symptoms of anxiety, lower self­ esteem, and lower psychosocial functioning were larger for adolescent boys compared to adolescent girls. However, Derdikman-Eiron et al. (2011) conducted a cross-sectional investigation which does not allow for inference of causality. The current study provides prospective evidence linking low self-esteem to later depression in adolescent boys but not girls. Past research has revealed similar findings. For example in a comparison o f multiple risk factors as predictors of depression Kendler, Gardner and Prescott (2006) found low self­ 63 esteem predicted depression in men. However, in an earlier parallel investigation in a sample of women, low self-esteem did not predict depression (Kendler, Gardner, & Prescott 2002) Several other studies have found low self-esteem predicts depression in both genders (e.g. Boden, Fergusson, & Horwood 2008; Orth, Robins, & Roberts, 2008). In considering the causal effect of low self-esteem on depression, it is important to note that some researchers argue low self-esteem should be viewed as a risk marker that reflects other factors influencing the development of depression. In a longitudinal study Boden, Fergusson and Horwood (2008) found low self-esteem at age 15 predicted later depression. Further investigation revealed low self-esteem tended to be more common in individuals who had lower IQ levels, previous mental health problems, higher levels of neuroticism, lower socioeconomic status, increased family dysfunction as well as childhood adversities such as poor parental bonding, physical and sexual abuse (Boden et al., 2008). It is also worth noting that studies examining self-esteem in adolescents often find boys tend to report higher levels of self-esteem compared to girls (e.g Boden, Fergusson, & Horwood 2008; Derdikman-Eiron et al. 2011; Orth et al., 2008). Speculation o f the current findings in light of past research suggests that because boys tend to score higher on self­ esteem measures compared to girls, boys who score low on self-esteem may be especially vulnerable due to exposure to higher levels of other risk factors for depression. However, further research is needed to clarify gender differences in the effects of low self-esteem and risk for depression. Biological parent status. The loss of a parent from the household emerged as a significant risk factor for girls but not for boys. Girls who were not living with both biological parents by ages 4 to 8 years were at higher risk for increased depression symptoms 64 compared to girls whose families remained intact. The current study did not differentiate between death, separation or divorce as the cause for the loss o f a parent from the household and only indicated whether the child resides with both, one, or neither biological parents. However, past research has indicated parental absence due to a variety of reasons has been associated with the development of depression (e.g. Amato, 1991). Several explanations have been offered for the link between parental absence in childhood and the development of depression in offspring. For example, Strohschein (2005) found that compared to families that remain intact, families that divorce or separate have higher levels of parental depression, lower levels of family function and lower marital satisfaction. In addition, the effects of parental divorce have been found to differ between genders in respect to the development o f depression. Oldehinkel, Ormel, Veenstra, De Winter and Verhulst (2008) found depressive symptoms for adolescent girls increased if they experienced parental divorce while adolescent boys reported fewer depressive symptoms regardless of their membership in intact or divorced families. Additionally, in an analysis of depression in young adults McCabe (1997) found young women whose parents had divorced had significantly higher levels of depression compared to young men whose parents had divorced. However, it is unlikely boys are unaffected by the loss of a parent. Research has indicated the loss of a parent may place boys at increased risk for a variety of negative outcomes. For example, Kendler et al. (2006) found the loss of a parent in a sample of males predicted neuroticism, low self-esteem, early-onset anxiety and conduct disorder. Overall, research indicates the loss of a parent in childhood likely has differential effects on the outcomes of boys and girls. 65 Child aggression. Parent-rated child physical aggression, indirect aggression and property damage at ages 4 to 8 years was not shown to be a useful predictor of depression. However, higher levels o f self-reported aggression and conduct disorder scores at ages 10 to 14 years predicted depression for girls but not for boys. Similar results have been found in prior longitudinal studies; for example, in a population sample of children aged 9 to 13 years Costello et al. (2003) found significant levels of comorbidity between CD and depression for girls but not boys. Further, Wiesner and Kim (2006) found links between delinquent behaviour and depression higher for girls than for boys. While research has indicated behaviour disorders are predictive of depression in both boys and girls; the relationship between behaviour disorders and depression is complex and methodological issues have led to different findings across studies. For instance, many studies combine ODD and CD into a single category due to small sample size when the two disorders are separated. However, important differences have been found when ODD and CD are separated and examined in relation to depression. In a longitudinal study, Boylan et al. (2010) found ODD predicted later depression in boys but not girls. Burke et al. (2010) found ODD was predictive of depressive disorders in a longitudinal study o f preadolescent girls while CD was not. Additionally, Burke et al. found a specific set of ODD symptoms reflecting negative affect was associated with later depression symptoms in girls, while symptoms reflecting the behavioural dimension did not. It is important to note the NLSCY conduct disorder and aggression measures do not incorporate items specific to the affective dimension of ODD. It is possible the relationship between conduct problems and depression may have the opposite predictive effect for boys compared to girls. For example in a longitudinal examination of boys aged 13.5 to 17.5 Beyers and Loeber (2003) found after controlling for common risk 66 factors depressed mood significantly predicted delinquency while the effect of delinquency on depressed mood was not significant. Given the inconsistent findings of studies investigating the relationship between behaviour disorders and depression, further research is required to elucidate the complex relationship between behaviour disorders, gender, and depression. PMK depression. In the mixed sample, higher levels of parental depression when the child was aged 4 to 8 years predicted higher depression scores for girls but not boys. The predictive utility of maternal depression was lower in the mixed sample compared to the biological sample. While the majority of the mixed sample consisted of biological mothers and their children; the analysis of the sample containing only biological mothers and their children demonstrated a stronger predictive effect for maternal depression and the risk of depression in girls. These results are interesting as Connell and Goodman (2002) found no significant differences in the magnitude of effect of association between parental psychopathology and child psychopathology in a comparison of studies using biological samples and studies using a mix of biological and non-biological parents. However, the authors caution in the interpretation of results as the sub-samples were not found to be homogeneous (Connell & Goodman, 2002). This study provides evidence of stronger effects of maternal depression in relation to the development of depression for girls in an exclusively biological sample. Biological Mother and Child Analyses Analysis of the sub-sample in which only children whose PMK for Cycle 1 and Cycle 4 was the child’s biological mother revealed similarities and differences in results for both 67 boys and girls. Notably, maternal depression and child-reported parenting measures emerged as risk factors in the biological sample. M aternal Depression. Maternal depression when girls were aged 4 to 8 years and 10 to 14 years predicted depression at ages 16 to 20 years. These results support the hypothesis for heritability effects wherein the child inherits from the mother a genetic predisposition to develop depression (Goodman & Gotlib, 1999). Further, this study found maternal depression predicted depression for girls but not boys. Researchers investigating the heritability of depression have drawn inconsistent conclusions regarding the genetic liability of depression for boys and girls. For example, in a comparison between genetic and environmental contributions to depression Bierut et al. (1999) found the contribution of genetic factors best accounted for the aggregation among female but not male family members. Conversely, some studies have found no significant gender differences in heritability for major depression (i.e. Janzing, Graaf, Have, Vollebergh, Verhagen, & Buitelaar, 2009; Kendler & Prescott, 1999). Inconclusive results obtained by studies investigating the heritability of depression in males may be due to differences in the samples used for investigations. Evidence suggests heritability of depression may be greater in same sex parent-child dyads. For example Eberhart, Shih, Hammen, and Brennan (2006) found adolescent boys whose fathers had a history of depression were more likely to report depression while fathers’ depression was unrelated to depression in adolescent girls. It is possible parental depression has differential heritable effects depending on the gender of the child. Additionally, greater risk of depression in same sex offspring may also be due to the child’s identification with the same-sex parent. As a result, children may be more likely to model depressive behaviours and affect of their 68 same sex parent. Further research is needed to untangle the effects of maternal and paternal depression and differential effects on boys and girls. While this study provides evidence of heredity of maternal depression and the development of depression in female offspring, it is important to also take into consideration the effects of the environment the child experiences as a result of having a depressed mother. Research has demonstrated children of depressed mothers may be exposed to more negative parenting practices as depression is associated with maladaptive behaviour, affect and cognitions (Goodman, 2007). As a result, the association between maternal depression and depression in girls is likely due to the interaction between genetics and environmental effects. Parental rejection. In the biological mother and child sample, a higher level of child­ rated parental rejection when the children were 10 to 14 years predicted depression symptoms 6 years later for both boys and girls. These results are not surprising as the link between parental rejection and depression and anxiety in children and adolescents has been well established. For example van der Bruggen, et al. (2010) found maternal rejection when the children were aged 3.5 years was significantly related to children’s depression and anxiety symptoms at follow-up one year later. Additionally, van der Bruggen et al. found maternal rejection mediated the relationship between children’s negative emotionality and later depression and anxiety symptoms. Magaro and Weisz (2006) found perceived parental rejection was strongly linked with depression for both genders in children aged 11 and younger as well as adolescents aged 12 and older. In a review of several studies investigating the effects of parenting factors and the development o f anxiety and depression in children, Rapee (1997) also found consistent evidence linking parental rejection with depression in offspring. 69 Parental monitoring. The current study found perceptions of low levels of parental monitoring at ages 10 to 14 years predicted higher symptoms of depression at ages 16 to 20 years for girls but not for boys. While these results are similar to those found in other studies, findings associating parental monitoring with childhood and adolescent depression symptoms have been inconsistent. For example, Simpkins and Parke (2002) found higher levels of maternal supervision were related to feelings of depression in 6th grade boys. Berg-Nielsen, Vikan and Dahl (2003) found low levels of parental monitoring with specific measures of parental interest and efforts to know who the adolescent was associating and of their whereabouts found low parental interest was related to increased levels of expressed anger in both boys and girls. However, low parental interest was not associated with child worries or mood problems (Berg-Nielsen, et al., 2003). It is important to note parental monitoring is a complex and bidirectional process which occurs between children and parents. The process of parental monitoring is affected by several aspects including child disclosure (Kerr & Stattin, 2000; Stattin & Kerr, 2000) and parental interest and efforts to know about the child’s activities, peers and whereabouts (Berg-Nielsen et al., 2003). The inconsistency of findings linking parental monitoring to youth depression may be due to methodological differences in how the construct is defined and measured. Parental monitoring has been defined as deliberate parental attention and behaviours designed to track children’s whereabouts and activities (Dishion & McMahon, 1998). However, Stattin and Kerr (2000) argue that parental monitoring defined as an action is flawed; many studies ask parents questions concerning their level of knowledge of their child’s activities and do not ask about specific tracking and surveillance strategies used by the parents. This distinction is important as research suggests the main source o f knowledge of 70 children’s whereabouts and activities is through child disclosure (Kerr & Stattin, 2000; Stattin & Kerr, 2000). Further, Stattin and Kerr argue parenting factors that influence child disclosure are important as children are active agents in the parental monitoring process. For example, when the child discloses something, the parent’s reaction in terms of warmth and understanding may play a role in the likelihood of future child disclosure (Kerr & Stattin, 2000). This explanation is supported by Fletcher, Steinberg, and Williams-Wheeler (2004) who found parents who scored higher on warmth measures were also more knowledgeable about their children’s friendships, activities and whereabouts. The reciprocal nature o f parental monitoring may explain why low levels of parental monitoring are a predictor for depression in girls but not boys. Studies have reported girls tend to disclose more information to parents than boys (e.g. Kerr & Stattin, 2000). Further, Laird, Pattit, Bates and Dodge (2003) found over time, levels of adolescent reported knowledge to parents remain stable for girls but decrease for boys. These results indicate girls may require closer relationships to parents during adolescence compared to boys. There is evidence to support this conclusion, as adolescent girls have been shown to demonstrate a higher proclivity for communion, expressed through interpersonal connectedness (Davies & Lindsay, 2004). As a result, the association between girls’ depression and low monitoring scores may reflect a lack of connectedness with parents and subsequent risk for depression. In summary, the relationship between parental monitoring and the development of child and adolescent depression is complex as parental monitoring is a multifaceted construct that is influenced by many factors such as the quality of relationship between the parents and child indicated by levels of parental warmth, gender of the child and willingness for disclosure on the part of the child. Parental efforts and interest in gaining knowledge as well 71 as incorporating developmentally appropriate control strategies are important factors in considering the effects of parental monitoring on youth depression. The significance of parental monitoring and parental rejection as predictors of depression in the biological sample but not the mixed sample is open to speculation. It is possible children who were not included in the biological sample had a change o f guardian between Cycle 1 and Cycle 4 and therefore received different parenting characteristics over their childhood. It is also possible the significant effects of parenting variables in the biological sample may be due to genetic mediation of environmental influences. According to Rutter, Pickles, Murray and Eaves (2001) research has consistently demonstrated associations between family characteristics and child behaviour are stronger in biological families. As a result, many environmental measures have a degree of genetic mediation (Rutter et al., 2001). The implication is the environmental influences on the child such as parent characteristics likely reflect a reciprocal interaction between environment and genetics as research has shown that the environment is influenced by genetics. For example, Bergeman et al. (1991) found perceptions of social support, life satisfaction and measures of depression receive nearly equal influence by environment and genetics. As a result, it is important to consider the effects of parental characteristics such as monitoring and rejection involve interplay between genetics and environment. Further, the parental measures in Cycle 4 used in this study are reported by the child. As perceptions of the environment are influenced by genetics, it may be that children prone to depression are more likely to rate their parents as lower in monitoring or higher in rejection. 72 Limitations and Strengths of the Study This study contains some noteworthy strengths and limitations. The generalization of findings in a longitudinal study is limited due to sample attrition and changes occurring in the general population over time (van der Kamp & Bijleveld, 1998). The sample used in this study was the original cohort of the NLSCY and is representative of children aged 0 to 11 years in 1994/95 (Statistics Canada, 2007b). Additionally, representativeness of the current study’s sample is reduced due to sample attrition over 12 years. It is also important to consider the absence of certain measures in this study. While the study included a wide range of childhood and adolescent predictors of depression, data were not available for the occurrence of sexual abuse, physical abuse or neglect. It is well established that childhood abuse and neglect is associated with increased risk for the development of psychopathology (e.g. Brown, Cohen, Johnson, and Smailes, 1999; Putnam, 2003; Silverman, Reinherz & Giaconia, 1996). Brown et al. (1999) found adolescents and adults with childhood histories of maltreatment were three times more likely to develop depression or become suicidal, with sexual abuse emerging as the largest independent risk factor. An analysis of the effects of physical abuse, neglect and sexual abuse histories in a sample of adolescents, found elevated levels of depression in all three groups (Cohen, Brown & Smailes, 2001). Given the strong relationship between childhood abuse and neglect with depression, the inclusion of a measure of abuse and neglect would have allowed for a more mixed assessment of the predictors of depression in adolescents and young adults. Additionally, the measure of parental depression has important limitations as it only provides a measure of symptoms occurring over the past week and does not examine depression on an on-going basis or provide information on the history of depression symptoms. This is an important limitation to consider as depression is episodic and recurrent and a snapshot in time does not capture the extent of illness in an individual. Further, the design o f this study did not include measurement of maternal depression occurring when the children were younger than 4 to 8 years of age. Analysis of the impact of maternal depression on very young children is important as research has shown that children exposed to maternal depression at younger ages are more vulnerable to internalizing and externalizing disorders (Connell & Goodman, 2002) as well as psychopathology and emotional disorders (Goodman, 2007; Goodman et al., 2011; Goodman & Gotlib, 1999). Additionally, all measures used in the NLSCY were shortened versions of the original measures with established reliability and validity. Most of the instruments yielded adequate measures of reliability when analyzed with the NLSCY sample. However, caution should be used when considering results from the childhood aggression and conduct disorder measures as well as parent rejection and parent monitoring as these instrument yielded lower measures of reliability compared to other measures. This study also has several strengths to consider. All predictor variables were examined separately by gender within a genetically informed design. The inclusion of a genetically informed sample containing only biological mothers and children is beneficial for the investigation of the heritability of depression. Further, a mixed sample and a biological sample provided the opportunity to investigate any differences in risk factors that may be found depending on the type o f sample. The inclusion of both parent reported and child reported symptoms of internalizing symptoms and behaviour problems lend further validity to the findings. This is an important strength of the study as the use o f both parent reported and child reported symptoms have been shown to be useful in predicting depression in 74 longitudinal samples (Kosterman et al., 2010; Mason et al., 2004). Finally, while all longitudinal studies have limitations in generalization due to changes in the sample and population as a whole, the study used a large population sample on Canadian children and youth, increasing the generalizability of the results. Implications This study offers several important findings as there are few longitudinal studies with genetically informed designs investigating gender differences in childhood risk factors for the development of depression. The findings inform prevention efforts designed to reduce risk for depression as it provides evidence of the relative importance of specific predictors across development and by gender. Researchers in the field o f prevention have expressed the need for studies to inform prevention efforts so they may design targeted, specific and personalized interventions (Reynolds, 2009). These are important implications for public health as research shows that preventive efforts for at risk youth are effective in preventing depression (Garber et al., 2009). Findings of the study suggest boys with internalizing symptoms as early as 4 years old are at increased risk for depression. Girls who experience the loss of a biological parent are also at increased risk to develop depression. Risk for depression in girls seems to be closely tied to genetic risk as evidenced by the strong predictive utility of depression in the biological mother. The study also indicated internalizing symptoms predict depression for girls but this risk factor did not emerge until late childhood/early adolescence. Further, parenting factors such as parental rejection are shown to increase risk for both boys and girls while parental monitoring is a risk factor only for girls. Overall, this study provides further evidence regarding gender specific risk factors for the development of depression across childhood. 75 Knowledge of the specificity of risk helps enable prevention programs to target at risk children and better tailor prevention efforts and thereby reduce or prevent the development of depression in vulnerable children. Summary The study was a longitudinal investigation into the predictors of depression in a population sample o f children and youth. The study was designed to help fill the gap in the literature investigating a wide range of childhood risk factors that may be predictive o f adult depression. Gender differences in risk were examined within a genetically informed study design. Several important findings of the study should be considered. First, maternal depression is an important risk factor for girls and the effect of maternal depression is stronger in a biological mother and child sample compared to a sample that mixed biological mothers with other parental figures. This finding is important as it demonstrates girls may have increased genetic liability to develop depression compared to boys. Further, researchers should use genetically informed designs as differences in effect may be present depending on the makeup of the sample. Second, the loss o f a biological parent from the household places girls at increased risk for depression; however, this risk does not seem to extend to boys. Third, anxiety and depression symptoms are an important risk factor for the development of depression in boys as they predict depression in early childhood as well as in adolescence. Fourth, aggression and conduct disorder symptoms in girls are an important risk factor for depression in girls but not boys. Fifth, parental rejection is a risk factor for both boys and girls while low levels of parental monitoring increases risk only for girls. Overall, the study provided evidence of gender specific risk factors for depression across childhood in a population sample. The findings from this study aid in informing 76 prevention efforts by providing knowledge of genetic and gender specific risk factors for depression occurring in childhood. 77 References Abela, J. R. J., & Hankin, B. L. (2008a). Depression in children and adolescents: Causes, treatment, and prevention. In J.R.Z Abela & B.L. Hankin (Eds.) The handbook o f depression in children and adolescents, New York: Guilford Press. Abela, J. R. Z., & Hankin, B. L. (2008b). Cognitive vulnerability to depression in children and adolescents: A developmental psychopathology perspective. In J.R.Z Abela & B.L. Hankin (Eds.) The handbook o f depression in children and adolescents, New York: Guilford Press. Akil, H., Brenner, S., Kandel, E., Kendler, K. S., King, M., Scolnick, E., Watson, J. D., & Zoghbi, H. Y. (2010). The future of psychiatric research: Genomes and neural circuits. Science, (327), 1580-1581. doi.l 0.1126/science. 1188654 Amato, P. R. (1991). Parental absence during childhood and depression in later life. The Sociological Quarterly, 32(4), 543-556. American Psychiatric Association. (2000). Diagnostic and statistical manual o f mental disorders (4th ed., text rev.). Washington, DC: Author. Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. The Journal o f Child Psychology and Psychiatry, 40( 1), 57-87. doi:10.1111/1469-7610.00424 Avenevoli, S., Knight, E., Kessler, R.C., & Merikangas, K. R. (2008). Epidemiology of depression in children and adolescents. In J.R.Z Abela & B.L. Hankin (Eds.) The handbook o f depression in children and adolescents, New York: Guilford Press. Baare, W. F. C., Vinberg, M., & Knudsen, G. M., & Paulson, O. B., Langkilde, A. R., & Jemigan, T. L., Kessing, L. V. (2010). Hippocampal volume changes in healthy 78 subjects at risk of unipolar depression. Journal o f Psychiatric Research. 44 655-662. doi: 10.1016/j .jpsychires.2009.12.009 Barber, B. K. (1996). Parental psychological control: Revisiting a neglected construct. Child Development, 67(6), doi:3296-3319. 10.2307/1131780 Beck, A. T., Rush, A. J., Shaw, B. F., Emery, G. (1979). Cognitive therapy o f depression. New York: The Guilford Press. Bergeman, C. S., Plomin, R. Pedersen, N. L., & McCleam, G. E. (1991). Genetic mediation of the relationship between social support and psychological well-being. Psychology and Ageing, 6(4), 640-646. doi: 10.1037/0882-7974.6.4.640 Berg-Nielsen, T, S., Vikan, A., & Dahl, A. A. (2003). Specific parenting problems when adolescents have emotional and behavioural disorders. Nordic Journal o f Psychiatry, 57, 139-146. Beyers, J. M., & Loeber, R. (2003). Untangeling developmental relations between depressed mood and delinquency in male adolescents. Journal o f Abnormal Child Psychology, 31(3), 247-266. Bhardwaj, A., & Goodyer, I, M. (2009). Depression and allied illness in children and adolescents: Basic facts. Psychoanalytic Psychotherapy, 2J(3), 176-184. doi: 10.1080/02668730903227206 Biederman, J., Monuteaux, M. C., Mick, E., Spencer, T., Wilens, T. E., Klein, K. L., Price, J. E., Faraone, S.V. (2006). Psychopathology in females with AttentionDeficit/Hyperactivity Disorder: A controlled, five-year prospective study. Biological Psychiatry, 60(10), 1098-1105. doi:10.1016/j.biopsych.2006.02.031 Bierut, L. J., Heath, A. C., Bucholz, K. K., Dinwiddie, S. H., Madden, P. A. F., & Statham, D. J., et al., (1999). Major depressive disorder in a community-based twin sample: Are there different genetic and environmental contributions for men and women? Archives o f General Psychiatry, 56, 557-562. doi:10.1001/archpsyc.56.6.557 Boden, J. M., Fergusson, D. M., Horwood, L. J. (2008). Does adolescent self-esteem predict later life outcomes? A test of the causal role o f self-esteem. Development and Psychopathology, 20, 319-339. Boylan, K., Georgiades, K., & Szatmari, P. (2010). The longitudinal association between oppositional and depressive symptoms across childhood. Journal o f the American Academy o f Child & Adolescent Psychiatry, 49(2), 152-161. doi: 10.1097/00004583201002000-00009 Brown, J., Cohen, P., Johnson, J. G., & Smailes, E. M. (1999). Childhood abuse and neglect: Specificity of effects on adolescent and young adult depression and suicidality. Journal o f the American Academy o f Child and Adolescent Psychiatry, 35(12), 14901496. Bulloch, A. G., Williams, J. V., Lavorato, D. H., & Patten, S. B. (2009). The relationship between major depression and marital disruption is bidirectional. Depression and Anxiety, 26, 1172-1177. doi:10.1002/da.20618 Burke, J. D., Hipwell, A. E., & Loeber, R. (2010). Dimensions of oppositional defiant disorder as predictors o f depression and conduct disorder in preadolescent girls. Journal o f the American Academy o f Child & Adolescent Psychiatry, 49(5), 484-492. Burke, J. D., Loeber, R., Lahey, B. B., & Rathouz, P. J. (2005). Developmental transitions among affective and behavioural disorders in adolescent boys. Journal o f Child 80 Psychology and Psychiatry, 46(11), 1200-1210. doi: 10.111 l/j.14697610.2005.00422.x Cancian, M., & Meyer, D. R. (1998). Who gets custody? Demography, 35(2), 147-157. Charach, A., Lin, E., & To, T. (2010). Evaluating the hyperactivity/inattention subscale of the National Longitudinal Survey of Children and Youth. Component o f Statistics Canada Catalogue no. 82-003-X Health Reports vol. 21 no 2. Clark, L. A., & Watson, D. (1991). Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications. Journal o f Abnormal Psychology, 100(3), 316336. doi: 10.1037/0021-843X. 100.3.316 Cohen, P., Brown, J., & Smailes, E. (2001). Child abuse and neglect and the development of mental disorders in the general population. Development and Psychopathology, 13, 891-999. Cole, D. A. (1991). Change in self-perceived competence as a function of peer and teacher evaluation. Developmental Psychology, 27(4), 682-688 doi: 10.1037/00121649.27.4.682 Cole, D. A., Maxwell, M. A., Dukewich, T. L., & Yosick, R. (2010). Targeted peer victimization and construction of positive and negative self-cognitions: Connections to depressive symptoms in children. Journal o f Clinical Child & Adolescent Psychology, 39(3), 421-435. doi: 10.1080/15374411003691776 Colletti, C. J. M., Forehand, R., Garai, E., Rakow, A., McKee, L., Fear, J. M., & Compas, B. E. (2009). Parent depression and child anxiety: An overview of the literature with clinical implications. Child Youth Care Forum, 38, 151-160. doi: 10.1007/sl0566-0099074-x 81 Compas, B. E., Connor-Smith, J., & Jaser, S. S. (2004). Temperament, stress reactivity, and coping: Implications for depression in childhood and adolescents. Journal o f Clinical Child & Adolescent Psychology, 33(1), 21-31. Connell, A. M., & Goodman, S. H. (2002). The association between psychopathology in fathers versus mothers and children’s internalizing and externalizing behavior problems: Ameta analysis. Psychological Bulletin, 128(5), 746-773. doi: 10.1037/0033-2909.128.5.746 Costello, E. J., Erkanli, A., & Angold, A. (2006). Is there an epidemic of child and adolescent depression? Journal o f Child Psychology and Psychiatry, 47(12), 1263-1271. doi:10.1001/archpsyc.60.8.837 Costello, E. J., Mustillo, S., Erkanli, A., Keeler, G., & Angold, A. (2003). Prevalence and development of psychiatric disorders in childhood and adolescence. Archives o f General Psychiatry, 60, 837-844. Crowe, M., Ward, N., Dunnachie, B., & Roberts, M. (2006). Characteristics of adolescent depression. International Journal o f Mental Health Nursing, 15, 10-18. doi: 10.1111/j. 1447-0349.2006.00399.x Davidson, R. J., Jackson, D. C., & Kalin, N. H. (2000). Emotion, plasticity, context, and regulation: perspectives from affective neuroscience. Psychological Bulletin, 126(6), 890-909. doi: 10.1037/0033-2909.126.6.890 Davidson, R. J., Pizzagalli, D,, Nitschke, J. B., & Putnam, K. (2002). Deppression: Perspectives from affective neuroscience. Annual Review o f Psychology, 53, 545-574. doi: 10.1146/annurev.psych.53.100901.135148 82 Davies, P. T., & Lindsay, L. L. (2004). Interparental conflict and adolescent adjustment: Why does gender moderate early adolescent vulnerability? Journal o f Family Psychology, 75(1), 160-170. doi: 10.1037/0893-3200.18.1.160 Davila, J., Stroud, C. B., & Starr, L. R. (2009). Depression in couples and families. In I. H. Gotlib & C. L. Hammen. (Eds.) Handbook o f depression, New York: The Guilford Press. Department of Justice (2004). Child custody arrangements: Their characteristics and outcomes. Retrieved July 24, 2010 from http://www.iustice.gc.ca/eng/pi/fcv-fea/libbib/rep-rap/2004/2004 3/char-car.html#ftn 12 Derdikman-Eiron, R., Indredavik, M. S., Bratberg, G. H., Taraldsen, G., Bakken, I. J., & Colton, M. (2011). Gender differences in subjective well-being, self-esteem and psychosocial functioning in adolescents with symptoms of anxiety and depression: Findings from the Nord-Trondelang health study. Scandinavian Journal o f Psychology, 52, 261-267. Dishion, T. J., & McMahon, R. J. (1998). Parental monitoring and the prevention of child and adolescent problem behaviour: A conceptual and empirical formulation. Clinical Child and Family Psychology, 7(1), 61-75. Drabick, D. A. G., Gadow, K. D., & Sprafkin, J. (2006). Co-occurrence of conduct disorder and depression in a clinic-based sample of boys with ADHD. Journal o f Child Psychology and Psychiatry, 47(8), 766-774. doi:10.111/j.1469-7610.2006.01625.x Eberhart, N. K., Shih, J. H., Hammen, C. L., & Brennan, P. A. (2006). Understanding the sex differences in vulnerability to adolescent depression: An examination of child and 83 parent characteristics. Journal o f Abnormal Child Psychology, 34(4), 495-508. doi: 10.1007/s 10802-006-9020-4 Elgar, F. J., Mills, R. S. L., McGrath, P. J., Waschbusch, D. A., & Brownridge, D. A. (2007). Maternal and paternal depressive symptoms and child maladjustment: The mediating role of parental behavior. Journal o f Abnormal Child Psychology, 35, 943-955. doi: 10.1007/s 10802-007-9145-0 Elia, J., Ambrosini, P., & Berrettini, W. (2008). ADHD characteristics: 1. Concurrent co­ morbidity patterns in children and adolescents. Child and Adolescent Psychiatry and Mental Health, 2(15). doi:10.1186/1753-2000-2-15 Ezpeleta, L., Domenech, J. M., & Angold, A. (2006). A comparison of pure and comorbid CD/ ODD and depression. Journal o f Child Psychology and Psychiatry, 47(1), 704712. DoilO.l 111/j. 1469-7610.2005.01558.x Fanselow, M. S. (2000). Contextual fear, gestalt memories, and the hippocampus. Behavioural Brain Research, 110, 73-81.doi:10.1016/S0166-4328(99)00186-2 Farmer, M. E., Locke, B. Z., Liu, I. Y., Moscicki, E. K.; et al; (1994). Depressive symptoms and attrition: the NHANES I epidemiologic follow-up study. International Journal o f Methods in Psychiatric Research. 4(1), 19-27. Fear, J. M., Champion, J. E., Reeslund, K. L., Forehand, R., Colletti, C., Roberts, L., & Compas, B. E. (2009). Parental depression and interpersonal conflict: Children and adolescents’ self-blame and coping responses. Journal o f Family Psychology, 25(5), 762-766. doi:10.1037/a0016381 84 Feng, X. Shaw, D. S., & Silk, J. S. (2008). Developmental trajectories of anxiety symptoms among boys across early and middle childhood. Journal o f Abnormal Psychology, 777(1), 32-47. doi: 10.1037/0021-84X.117.1.32 Fletcher, A. C., Steinberg, L., & Williams-Wheeler, M. (2004). Parental influences on adolescent problem behaviour: Revisiting Stattin and Kerr. Child Development, 75(3), 781-796. Fox, J. (1997). Assessing sampling variation: Bootstrapping and cross-validation, la Applied regression analysis, linear models, and related methods, (pp. 493-520). Thousand Oaks, CA: SAGE. Galambos, N. L., Leadbeater, B. J., & Barker, E. T. (2004). Gender differences in and risk factors for depression in adolescence: A 4-year longitudinal study. International Journal o f Behavioral Development, 25(1), 16-25.doi:10.1080/01650250344000235 Gallerani, C. M., Garber, J., & Martin, N. C. (2010). The temporal relation between depression and comorbid psychopathology in adolescents at varied risk for depression. Journal o f Child Psychology and Psychiatry, 57(3), 242-249. doi: 10.1111/j. 1469-7610.2009.02155x Garber, J., Clarke, G. N., Weersing, V. R., Beardslee, W. R., Brent, D. A., Gladstone, T. R. G...Lyengar, S. (2009). Prevention of depression in at-risk adolescents: A randomized controlled trial. The Journal o f the American Medical Association, 507(21), 22152224. Garber, J., Weiss, B., & Shanley, N. (1993). Cognitions, depressive symptoms, and development in adolescents. Journal o f Abnormal Psychology, 702(1), 47-57. doi: 10.103 7/0021-843X. 102.1.47 Garber, J., Gallerani, C. M., & Frankel, S. A. (2009). Depression in children. In I. H. Gotlib & C. L. Hammen. (Eds.) Handbook o f depression, New York: Guilford Press. Goodman, S. H. (2007). Depression in mothers. Annual Review o f Clinical Psychology, 3, 107-135. doi: 10.1146/annurev.clinpsy.3.022806.901401 Goodman, S. H., & Gotlib, I, H. (1999). Risk for psychopathology in the children of depressed mothers: A developmental model for understanding mechanisms of transmission. Psychological Review, 106(3), 458-490. Goodman, S. H., Rouse, M. H., Connell, A. M., Broth, R. M., Hall, C. M., & Heyward, D. (2011). Maternal depression and child psychopathology: A meta-analytic review. Clinical Child and Family Psychology Review, 14, 1-27. doi:10.1007/sl0567-0100080-1 Goodman, S. H., & Tully, E. (2008). Children of depressed mothers: Implications for the etiology, treatment, and prevention of depression in children and adolescents. In J.R.Z Abela & B.L. Hankin (Eds.) The Handbook o f Depression in Children and Adolescents, New York: Guiliford Press. Gotlib, I. H., & Joormann, J. (2010). Cognition and depression: Current status and future directions. Annual Review o f Clinical Psychology, 6, 285-312. doi: 10.1146/annurev.clinpsy. 121208.131305 Graaf, R., Bijl, R. V., Smit, F., Ravelli, A., & Vollebergh, A. M. (2000). Psychiatric and sociodemographic predictors of attrition in a longitudinal study: The Netherlands mental health survey and incidence study (NEMESIS). American Journal o f Epidemiology, 752(11), 1039-1047. 86 Graham, S., & Juvonen, J. (1998). Self-blame and peer victimization in middle school: An attributional analysis. Developmental Psychology, 34(3), 587-599. Doi: 10.1037/00121649.34.3.587 Grant, K. E., Compas, B. E., Thurm, A. E., McMahon, S. D., Gipson, P. Y., Campbell, A. J., ... Westerholm, R. I. (2006). Stressors and child and adolescent psychopathology: Evidence of moderating and mediating effects. Clinical Psychology Review, 26, 257283. doi:10.1016/j.cpr.2005.06.011 Hammen, C. & Brennan, P. A. (2002). Interpersonal dysfunction in depressed women: impairments independent of depressive symptoms. Journal o f Affective Disorders, 72, 145-156. doi: 10.1016/S0165-0327(01)00455-4 Hankin, B. L., Abramson, L.Y., Moffitt, T. E., Silva, P. A., McGee, R., & Angell, K. E. (1998). Development of depression from preadolescence to young adulthood: Emerging gender differences in a 10-year longitudinal study. Journal o f Abnormal Psychology, 107(1), 128-140. Hettema, J. M., Kuhn, J. W., Prescott, C. A., & Kendler, K. (2006). The impact of generalized anxiety disorder and stressful life events on risk for major depressive episodes. Psychological Medicine, 36, 789-795. Ingram, R. E., Miranda, J., & Segal, Z. V. (1998). Cognitive vulnerability to depression. New York: The Guilford Press. Ingram, R. E., & Siegle, G. J. (2009). Methodological issues in the study o f depression. In I. H. Gotlib & C. L. Hammen. (Eds.) Handbook o f Depression, New York: The Guilford Press. 87 Janzing, J. G. E., de Graaf, R., ten Have, M., Vollebergh, W. A., Verhagen, M., & Buitelaar, J. K. (2009). Soc Psychiat Epdemiol, 44, 1067-1074. Joormann, J. (2009). Cognitive aspects of depression. In I. H. Gotlib & C. L. Hammen. (Eds.) Handbook o f Depression, New York: The Guilford Press. Kendler, K. S., Gardner, C. O., & Prescott, C. A (2002). Toward a comprehensive developmental model for major depression in women. American Journal o f Psychiatry, 159(1), 1133-1145. doi: 10.1176/appi.ajp.l59.7.1133 Kendler, K. S., Gardner, C. O., & Prescott, C. A (2006). Toward a comprehensive developmental model for major depression in men. American Journal o f Psychiatry, 163(1), 115-124. Kendler, K. S., & Prescott, C. A. (1999). A population-based twin study of lifetime major depression in men and women. Archives o f General Psychiatry, 56, 39-44. Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders for men and women. Archives o f General Psychiatry, 60, 929-937. doi: 10.1001 /archpsyc.60.9.929 Kerr, M., & Stattin, H. (2000). What parents know, how they know it, and several forms of adolescent adjustment: Further support for a reinterpretation of monitoring. Developmental Psychology, 36(3), 366-380. Kosterman, R., Hawkins, J. D,.Mason, W. A., Herrenkohl, T. I., Lengua, L. J., & McCauley, E. (2010). Assessment o f behaviour problems in childhood and adolescence as predictors of early adult depression. Journal o f Psychopathology and Behavioural Assessment, 32, 118-127. Laird, R. D., Pettit, G. S., Bates, J. E., & Dodge, K. A. (2003).Parents’ monitoring-relevant knowledge and adolescents’ delinquent behavior: Evidence of correlated developmental changes and reciprocal influences. Child Development, 74(3), 752-768. Lakdawalla, Z., Hankin, B. L., & Mermelstein, R. (2007). Cognitive theories of depression in children and adolescents: A conceptual and quantitative review. Clinical and Family Psychology Review, 10( 1), 1-24. doi:10.1007/sl0567-006-0013-l Lau, J. Y. F., & Eley, T. C. (2008). New behavioral genetic approaches to depression in childhood and adolescence. In J.R.Z. Abela & B.L. Hankin (Eds.) The Handbook o f Depression in Children and Adolescents, New York: Guiliford Press. Lempers, J. D., Clark-Lempers, D., & Simons, R. L. (1989). Economic hardship, parenting, and distress in adolescence. Child Development, 60, 25-39. Lovejoy, M. C., Graczyk, P. A., O’Hare, E., & Neuman, G. (2000). Maternal depression and parenting behaviour: A meta-analytic review. Clinical Psychology Review, 20(5), 561592. doi:10.1016/S0272-7358('98100100-7 MacMaster, F. P., Mirza, Y., Szeszko, P. R., Kmiecik, L. E., Easter, P. C., Taormina, S. P.,...Rosenberg, D. R. (2008). Amygdala and hippocampal volumes in familial early onset major depressive disorder. Biological Psychiatry, 63(4), 385-390. doi:10.1016/j.biopsych.2007.05.005 Magaro, M. M., & Weisz, J. R. (2006). Perceived control mediates the relation between parental rejection and youth depression. Journal o f Abnormal Child Psychology, 34, 867-876. doi: 10.1007/sl0802-006-9072-5). Mason, W. A., Kosterman, R., Hawkings, D., Herrenkohl, T., Lengua, L. J., McCauley, E. (2004). Predicting depression, social phobia, and violence in early adulthood from 89 child behaviour problems. Journal o f American Academy o f Child and Adolescent Psychiatry, 43{3), 307-315. McCabe, K. M. (1997). Sex differences in the long term effects of divorce on children. Journal o f Divorce & Remarriage, 27(1), 123-135. McCarty, C. A., Vander Stoep, A., McCauley, E. (2007). Cognitive features associated with depressive symptoms in adolescence: Directionality and specificity. Journal o f Clinical Child and Adolescent Psychology, 36(2), 147-158. McGee, R., Feehan, M., Williams, S., Partridge, F., Silva, P. A., & Kelly, J. (1990). DSM-III disorders in a large sample of adolescents. Journal o f the American Academy o f Child and Adolescent Psychiatry, 29(4), 611-619. doi: 10.1097/00004583-199007000-00016 McGuffin, P., & Katz, R. (1989). The genetics of depression and manic-depressive disorder. British Journal o f Psychiatry, 155, 294-304. Mojarrad, T., & Lennings, C. J. (2002). Examination of the center for epidemiological studies depression scale (CES-D) in an adolescent mental health sample. Journal o f Applied Health Behaviour, 4(1 &2), 1-6. Nantel-Vivier, A., & Pihl, R. O. (2008). Biological vulnerability to depression. In J.R.Z Abela & B.L. Hankin (Eds.) The Handbook o f Depression in Children and Adolescents, New York: Guiliford Press. Nock, M. K., Kazdin, A. E., Hiripi, E., & Kessler, R. C. (2007), Lifetime prevalence, correlates, and persistence of oppositional defiant disorder: results from the National Comorbidity Survey Replication. Journal o f Child Psychology and Psychiatry, 48(1), 703-713. doi: 10.111/j. 1469-7610.2007.01733.x 90 Nolen-Hoeksema, S., & Hilt, L. M. (2009). Gender differences in depression. In I. H. Gotlib & C. L. Hammen. (Eds.) Handbook o f Depression, New York: The Guilford Press. O’Donnell, E. H., Moreau, M., Cardemil, E. V., & Pollastri, A. (2010). Interparental conflict, parenting, and childhood depression in a diverse urban population: The role of general cognitive style. Journal o f Youth and Adolescents, 39, 12-22. doi: 10.1007/s 10964008-9357-9 Oldehinkel, A. J., Ormel, J., Veenstra, R., De Winter, A. F. Verhulst, F. C. (2008). Parental divorce and offspring depressive symptoms: Dutch developmental trends during early adolescence. Journal ofMarriage and family, 70, 284-293. Oltmanns, E. F., Emery, R, E., & Taylor, S. (2006). Abnormal Psychology. (2nd ed.). Toronto. Ontario. Orth, U., Robins, R. W., & Roberts, B. W. (2008). Low self-esteem prospectively predicts depression in adolescence and young adulthood. Journal o f Personality and Social Psychology, 95(3), 695-708. doi: 10.1037/0022-3514.95.3.695 Phillips, O. (2004). Using bootstrap weights with Wes Var and SUDAAN. Information and Technical Bulletin, 1(2), 6-15. (Statistics Canada No. 12-002-XIE) Retrieved from http://publications.gc.ca/Collection/Statcan/12-002-XIE/12-002-XIE2004002.pdf Poulin, C., Hand, D., & Boudreau, B. (2005). Validity of a 12-item version of the CES-D used in the National Longitudinal Study o f Children and Youth. Chronic Diseases in Canada, 26, 2-3, 65-72. Putnam, F. W. (2003). Ten-year research update review: Child sexual abuse. Journal o f the American Academy o f Child and Adolescent Psychiatry, 42(2), 269-278. Radler, B. T., & Ryff, C. D. (2010). Who participates? Accounting for retention in the MIDUS National Study of Health and Well-Being. Journal o f aging and Health, 22(3), 307-331. Radloff, L. S. (1977). The CES-D scale: a self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 285-401. doi: 10.1177/014662167700100306 Rapee, R. M. (1997). Potential role of childrearing practices in the development of anxiety and depression. Child Psychology Review, 17, 47-67. Reynolds, C. F. (2009). Prevention of depressive disorders: A brave new world. Depression and Anxiety, 26, 1062-1065. Roberts, G. Kovacevic, M., Phillips, O., & Gentleman, J. (1999). Bridging the gap between the theory and practice of analysis of data from complex surveys - Some Statistics Canada experiences. Federal Committee on Statistical Methodology conference papers. Retrieved from http://www.statcan.gc.ca/pub/! 1-522-x/l l-522-x2005001eng.htm Rohde, P., Lewinsohn, P. M., & Seeley, J. R. (1991). Comorbidity of unipolar depression: II. Comorbidity with other mental disorders in adolescents and adults. Journal o f Abnormal Psychology, 100(2), 214-222. Rubab, G. A., Shapka, J. D., Dahinten, V. S., & Olson, B. F. (2011). Evaluation of the factor structure of the child-reported parenting questionnaire in the National Longitudinal Survey of Children and Youth. Component of Statistics Canada Catalogue no. 82003-X Health Reports vol. 22 No. 1. 92 Rudolph, K. D., Flynn, M., & Abaied, J. L. (2008). A developmental perspective in interpersonal theories of youth depression. In J.R.Z. Abela & B.L. Hankin (Eds.) The handbook o f depression in children and adolescents, New York: Guilford Press. Rutter, M., Pickles, A., Murray, R., & Eaves, L. (2001). Testing hypotheses on specific environmental causal effects on behaviour. Psychological Bulletin, 127(3), 291-324. Scott, B., & Melin, L. (1998). Psychometric properties and standardised data for questionnaires measuring negative affect, dispositional style and daily hassles. A nation-wide sample. Scandinavian Journal o f Psychology, 39, 301-307. doi: 10.1111/1467-9450.00088 Seligman, L. D., & Ollendick, T. H. (1998). Comorbidity of anxiety and depression in children and adolescents: An integrative review. Clinical Child and Family Psychology Review, 1(2), 125-144. doi:10.1023/A:1021887712873 Shelton, K. H., & Harold, G. T. (2008). Interparental conflict, negative parenting, and children’s adjustment: Bridging links between parents’ depression and children’s psychological distress. Journal o f Family Psychology, 22(5), 712-724. doi:10.1037/a0013515 Silverman, A. B., Reinherz, H. Z. & Giaconia, R. M. (1996). The long-term sequelae of child and adolescent abuse: A longitudinal community study. Child Abuse & Neglect, 20(8), 709-723. Simpkins, S, D., & Parke, R. D. (2002). Maternal monitoring and rules as correlates of children’s social adjustment. Merrill-Palmer Quarterly, 48(A), 360-377. Statistics Canada (2007a). Microdata user guide. National Longitudinal Survey of Children and Youth. Cycle 1 93 Statistics Canada (2007b). Microdata user guide. National Longitudinal Survey o f Children and Youth Cycle 7, September 2006 to 2007. Stattin, H., & Kerr, M. (2000). Parental monitoring: A reinterpretation. Child Development, 77(4), 1072-1085. Strauss, C. C., Last, C. G., Hersen, M., & Kazdin, A. E. (1988). Association between anxiety and depression in children and adolescents with anxiety disorders. Journal o f Abnormal Child psychology, 76(1), 57-68. Strauss, C. C., Lease, C. A., Last, C. G., & Francis, G. (1988). Overanxious disorder: An examination of developmental differences. Journal o f Abnormal Child Psychology, 76(4), 433-443. doi:10.1007/BF00914173 Stringaris, A., & Goodman, R. (2009). Three dimensions of oppositionality in youth. Journal o f Child Psychology and Psychiatry, 50(3), 216-223. doi: 10. I ll 1/j.. 14697610.2008.01989.x Strohschein, L. (2005). Parental divorce and child mental health trajectories. Journal o f Marriage and Family, 67, 1286-1300. Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic epidemiology of major depression: Review and meta-analysis. The American Journal o f Psychiatry, 757(10), 1552-1562. doi:10.1176/appi.ajp.l57.10.1552 van der Bruggen, C. O., Stams, G, J. J. M, Bogels, & S. M., Paulussen-Hoogeboom, M. C. (2010). Parenting behavior as a mediator between children’s negative emotionality and their anxiety/depression. Infant and Child Development, 19, 354-365. doi: 10.1002/icd.665 van der Kamp, L. J. Th., & Bijleveld, C. C. J. H. (1998). Methodological issues in longitudinal research. In C.C.J. H. Bijleveld, L. J. Th. van der Kamp., Mooijaart, A. van der Kloot, W. A., van der Leeden, R., & van der Burg, E. Longitudinal Data Analysis. Designs, Models and Methods, (pp. 1-45). London: SAGE Publications. Wiesner, M., & Kim, H. K. (2006). Co-occurring delinquency and depressive symptoms of adolescent boys and girls: A dual trajectory modeling approach. Developmental Psychology, 42(6), 1220-1235. Wilens, T. E., Biederman, J., Brown, S., Tanguay, S., Monuteaux, M. C., Blake, C., & Spencer, T. (2002). Psychiatric comorbidity and functioning in clinically referred preschool children and school-aged youths with ADHD. Journal o f the American Academy o f Child Psychiatry, 41(3), 262-268. doi:10.1097/00004583-20020300000005 World Health Organisation (2010). Depression. Retrieved May 11, 2010 from http:/ /www. who.int/mental_health/management/depression/definition/en/ index.html. 95 Appendix A Results o f the hierarchical multiple regression model fo r depression. Predictor Variable Step 1 Gender Age R2 R 2 change 0.12 0.12 Step 2 0.14 0.02 Gender Age Cl PMK Depression (L) Cl Hyperactivity (SR) Cl Emotional Disorder (L) Cl Aggression (L) C l Positive Interaction Cl Parental Consistency (RSR) Cl Hostile Punitive Cl Biological Parent Step 3 0.22 0.08 Gender Age C l PMK Depression (L) C 1 Hyperactivity (SR) C l Emotional Disorder (L) C l Aggression (L) C 1 Positive Interaction C l Parental Consistency (SR) C l Hostile Punitive C l Biological Parent C4 PMK Depression (L) C4 Friends (RL) C4 General Self (RL) C4 Emotional Disorder (SR) C4 Hyperactivity (SR) C4 Aggression/CD (L) B P Sr2 E c h a n g e (4 /) 114.25*** (2,1712) 0.51 -0.21 0.22*** -0.26*** 0.22 -0.26 0.48 . -0.23 0.14 -0.04 0.29 0.06 -0.01 -0.01 -0.01 0.25 0.21*** -0.28*** 0.05 -0.03 0.08* 0.02 -0.03 -0.01 -0.01 0.09** 0.20 -0.25 0.04 -0.02 0.07 0.02 -0.02 -0.01 -0.01 0.08 27.39*** (10,1704) 23.73*** (20,1694) 0.44 -0.25 0.10 -0.06 0.22 0.02 -0.01 -0.01 -0.01 0.24 0.17 0.08 0.31 0.26 -0.05 0.10 0.19*** -0.30*** 0.03 -0.05 0.06* 0.01 -0.02 -0.01 -0.03 0.08 0.06 0.02 0.08** 0.19*** -0.04 0.03 0.17 -0.26 0.03 -0.04 0.05 0.01 -0.02 -0.01 -0.03 0.06 0.06 0.02 0.07 0.16 -0.03 0.02 96 p2 xv n2 xv change C4 Parent Nurturance (SR) C4 Parent Monitoring (RSR) C4 Parental Rejection C4 Biological Parent B P sr2 -0.05 0.12 0.02 -0.02 -0.04 0.07* 0.08** -0.01 -0.03 0.06 0.07 F Change(^/) - 0 .0 1 Notes: SR = square root transformation. RSR = reflect and square root transformation. L = logarithmic transformed. RL = reflect and logarithmic transformed. * p < .05 ** p < .01 *** p < .001 97 Appendix B Results fo r hierarchical multiple regression fo r predicting youth depression in biological mother-child dyads. Predictor Variable Step 1 Gender Age n 2 0.14 n2 fv change B P sr1 U 7 is * * * (2,1480) 0.14 Step 2 0.16 0.03 Gender Age Cl PMK Depression (L) Cl Hyperactivity (SR) Cl Emotional Disorder (L) Cl Aggression (L) Cl Positive Interaction Cl Parental Consistency (RSR) Cl Hostile Punitive Cl Biological Parent Step 3 0.26 0.09 Gender Age Cl PMK Depression (L) Cl Hyperactivity (SR) Cl Emotional Disorder (L) Cl Aggression (L) Cl Positive Interaction Cl Parental Consistency (RSR) Cl Hostile Punitive Cl Biological Parent C4 PMK Depression (L) C4 Friends (RL) C4 General Self (RL) C4 Emotional Disorder (SR) C4 Hyperactivity (SR) C4 Aggression/CD (L) F C h z n g e id f) 0.55 -0.23 0.24*** -0.28*** 0.24 -0.28 2 8 .93*** (10,1472) 0.51 -0.25 0.12 -0.06 0.42 0.09 -0.01 0.003 -0.006 0.27 0.22*** -0.30*** 0.04 -0.05 on** 0.45 -0.26 0.07 -0.09 0.37 0.04 -0.01 0.01 -0.01 0.36 0.21 0.15 0.32 0.29 -0.09 0.03 Q J9*** 0.03 -0.03 0.002 -0.03 0.09** 0.21 -0.27 0.04 -0.04 0.10 0.02 -0.02 0.001 -0.02 0.09 2 5 .15*** (20,1462) -0.32*** 0.02 -0.07 0.10** 0.01 -0.03 0.006 -0.05 0.12* 0.08* 0.04 0.09* 0.21*** -0.07 0.01 0.18 -0.27 0.02 -0.06 0.09 0.01 -0.03 0.006 -0.04 0.08 0.07 0.04 0.07 0.17 -0.06 0.006 98 p2 ** C4 Parent Nurturance (SR) C4 Parent Monitoring (SR) C4 Parental Rejection C4 Biological Parent d2 change B P sr2 -0.06 0.14 0.03 -0.14 -0.05 0.08* -0.04 0.07 0.09 -0.03 Q JJ*** -0.05 ^C hang e{df) Notes: SR = square root transformation. RSR = reflect and square root transformation. L logarithmic transformed. RL = reflect and logarithmic transformed. * p < . 05 ** p < .01 *** p < .001 Appendix C Table Cl Proportions o f Youth in each Depression Category Category Frequency Percent Minimal Somewhat Elevated Very Elevated 1484 191 40 86.5% 11.2% 2.3% Note: Minimal = 0 to 11, Somewhat Elevated = 12 to 20 Very Elevated = 21 to 36 Table C2 Proportions o f Boys in each Depression Category Category Frequency Percent Minimal Somewhat Elevated Very Elevated 737 51 8 92.6% 6.4% 1.0% Note: Minimal = 0 to 11, Somewhat Elevated = 12 to 20 Very Elevated = 21 to 36 Table C3 Proportions o f Girls in each Depression Category Category Frequency Percent Minimal Somewhat Elevated Very Elevated 745 142 32 81.1% 15.4% 3.5% Note: Minimal = 0 to 11, Somewhat Elevated = 12 to 20 Very Elevated = 21 to 36 Appendix D Descriptive Statistics fo r the M ixed Sample fo r Boys Variable Mean SD Cycle 1 Variables PMK Depression Hyperactivity Anxiety/Depression Aggression Positive Interaction Parental Consistency Hostile/Punitive 4.50 5.60 2.54 4.01 13.26 15.05 18.88 5.41 3.95 2.44 4.26 3.00 3.44 5.34 Cycle 4 Variables PMK Depression Friends General Self Anxiety/Depression Hyperactivity Aggression Parent Nurturance Parent Monitoring Parent Rejection 4.23 12.74 13.27 3.02 4.13 3.75 21.09 14.86 9.48 5.20 2.74 2.44 2.59 2.85 3.77 5.35 3.18 5.05 Note: Means and standard deviations for continuous variables for Scale variables for (n = 796) boys Appendix E D escriptive Statistics fo r the Mixed Sample fo r Girls Mean SD Cycle 1 Variables PMK Depression Hyperactivity Anxiety/Depression Aggression Positive Interaction Parental Consistency Hostile/Punitive 5.40 4.11 2.42 3.18 12.97 15.01 17.70 5.99 3.21 2.50 3.53 3.03 3.37 4.87 Cycle 4 Variables PMK Depression Friends General Self Anxiety/Depression Hyperactivity Aggression Parent Nurturance Parent Monitoring Parent Rejection 4.64 13.58 13.43 3.62 3.26 2.67 21.60 15.45 9.07 5.85 2.37 2.57 2.69 2.72 3.23 5.09 3.10 4.75 Variable Note: Means and standard deviations for continuous variables for Scale variables for (n = 919) girls. 102 Appendix F Descriptive Statistics fo r the Biological M other-Child Sample f o r Boys Variable Mean SD Cycle 1 Variables PMK Depression Hyperactivity Anxiety/Depression Aggression Positive Interaction Parental Consistency Hostile/Punitive 4.57 5.66 2.58 4.11 13.22 14.96 19.12 5.40 3.97 2.44 4.24 2.95 3.44 5.28 Cycle 4 Variables PMK Depression Friends General Self Anxiety/Depression Hyperactivity Aggression Parent Nurturance Parent Monitoring Parent Rejection 4.23 12.73 13.29 3.01 4.18 3.76 21.07 14.96 9.41 5.13 2.79 2.46 2.59 2.88 3.83 5.30 3.19 4.97 Note: Means and standard deviations for continuous variables for Scale variables for (n = 686) boys. 103 Appendix G Descriptive Statistics fo r the Biological M other-Child Sample f o r Girls Variable Mean SD Cycle 1 Variables PMK Depression Hyperactivity Anxiety/Depression Aggression Positive Interaction Parental Consistency Hostile/Punitive 4.97 3.86 2.30 3.11 13.19 15.14 17.60 5.34 3.09 2.40 3.44 2.86 3.10 4.71 Cycle 4 Variables PMK Depression Friends General Self Anxiety/Depression Hyperactivity Aggression Parent Nurturance Parent Monitoring Parent Rejection 4.39 13.62 13.48 3.47 3.12 2.59 21.75 15.52 8.95 5.66 2.27 2.62 2.52 2.55 3.21 4.98 3.01 4.70 Note: Means and standard deviations for continuous variables for Scale variables for (n = 797) girls. 104 Appendix H Correlations between Cycle 1 Variables fo r the Mixed Sample ** © 3 1 -0.02 -0.07** 0.08** 0.01 -0.38* -0.09** 0.01 -0.01 4 1 0.23** 0.32** 0.28** 5 1 0.42** 0.45** -0.07** o n * * 0.24** 0.27** 0.44** 0.24** 0.11** ** 2 1 -0.03 0.09** -0.19** -0.03 -0.10** -0.05* 0.01 -0.12** 0.03 o1 1 1 C l Depression (SR) 1 2 Cl Gender 0.23** 3 Cl Age of Child -0.26** 4 Cl PMK Depression (L) 0.12** 5 Cl Hyperactivity (SR) 0.01 6 Cl Emotional Disorder (L) 0.07** 7 Cl Aggression Score (L) 0.03 8 Cl Positive Interaction 0.06* 9 Cl Parent Consistency (RSR) 0.03 10 Cl Hostile/Punitive 0.01 11 Cl Biological Parent Note: Correlations between Cycle 1 variables for the mixed sample (n = 1715) *p<. 05 **p < .01 ***/?<.001 6 1 0.39** -0.14** 0.15** 0.34** 0.10** 7 8 9 1 1 -0.11** 1 0.15** -0.11** 0.47** -0.30** 0.27** -0.01 0.09** -0.01 10 0.03 11 1 105 Appendix I Correlations between Cycle 4 Variables fo r the Complete Sample 1 1 C7 Depression (SR) 1 2 C4 PMK Depression (L) 0.12** 3 C4 Friends (R.L) 0.11** 4 C4 General Self (R.L) 0.12** 5 C4 Emotional Disorder (SR) 0.28** 6 C4 Hyperactivity (SR) 0.07** 7 C4 Aggression Score (L) 0.07** 8 C4 Parent Nurturance (RSR) 0.04 9 C4 Parent Monitoring (RSR) 0.01 10 C4 Parent Rejection 0.09** 11 C4 Biological Parent 0.08** 2 1 0.02 0.10** 0.06* 0.09** 0.06* 0.13** 0.06** 0.13** 0.23** 3 1 0.34** 0.27** 0.23** 0.19** 0.22** 0.10** 0.09** 0.04 4 1 0.36** 0.37** 0.40** 0.43** 0.24** 0.29** 0.09** 5 1 0.43** 0.33** 0.19** -0.02 0.27** 0.02 Note: Correlations between Cycle 4 variables for the mixed sample (n = 1715) * p <.05 * * p < .01 * * * p <.001 6 1 0.49** 0.27** 0.16** 0.30** 0.06* 7 1 0.34** 0.15** 0.37** 0.08** 8 0.45** 0.40** 0.10** 9 0.16** 0.13** 10 - 11 - 0.05* - 106 Appendix J Correlations between Cycle 1 and Cycle 4 Variables fo r the Complete Sample. Cl-1 1 C4 PMK Depression (L) 0.34** -0.02 2 C4 Friends 3 C4 General Self (R.L.) 0.02 4 C4 Emotional Disorder (SR) 0.06* 5 C4 Hyperactivity (SR) 0.04 6 C4 Aggression (L) 0.03 7 C4 Parent Nurturance (RSR) 0.10** 8 C4 Parent Monitoring (RSR) 0.03 9 C4 Parent Rejection 0.05* 10 Biological Parent 0.25** Cl-2 0.04 -0.16** -0.06** 0.13** -0.16** -0.17** -0.05* -0.10** -0.04 -0.01 Cl-3 0.01 -0.12** 0.14** -0.03 0.10** 0.12** 0.25** 0.23** 0.22** 0.05* Cl-4 0.19** 0.17** 0.08** 0.12** 0.16** 0.49** 0.09** -0.02 0.06* 0.14** Cl-5 0.22** 0.06** 0.06* 0.14** 0.43** 0.06* 0.08** 0.01 0.08** 0.13** Cl-6 0.15** 0.11** 0.06** 0.09** 0.12** 0.20** 0.15** 0.03 0.14** 0.12** Note: Correlations between Cycle 4 variables for the mixed sample (n = 1715) * p <.05 * * p < .01 *** p <,001 1 = Cl PMK Depression 2 = Cl Child Gender 3 = Cl Child Age 4 = Cl Hyperactivity 5 = Cl Emotional Disorder 6 = Cl Aggression 7 = Cl Positive Interaction 8 = Cl Parental Consistency 9 = Cl Hostile/Punitive Parenting 10 = Cl Biological Parent Status Cl-7 Cl-8 -0.15** 0.15** 0.07** -0.01 -0.10** 0.02 -0.02 0.02 -0.13** -0.01 -0.10** -0.01 -0.22** 0.05* -0.13** 0,03 -0.04** 0.01 -0.09** 0.04 Cl-9 0.18** 0.10** 0.08** 0.07** 0.16** 0.14** 0.14** 0.06* 0.18** 0.6* Cl-10 0.15** 0.04 0.04 0.01 0.02 0.03 0.08** 0.03 0.05* 0.67**