Journal of Emotional and Behavioral Disorders

Journal of Emotional and Behavioral Disorders http://ebx.sagepub.com/

Socioeconomic Status and Child Development: A Meta-Analysis Nicole Lyn Letourneau, Linda Duffet-Leger, Leah Levac, Barry Watson and Catherine Young-Morris Journal of Emotional and Behavioral Disorders published online 15 December 2011 DOI: 10.1177/1063426611421007 The online version of this article can be found at: http://ebx.sagepub.com/content/early/2011/12/07/1063426611421007

Published by: Hammill Institute on Disabilities

and http://www.sagepublications.com

Additional services and information for Journal of Emotional and Behavioral Disorders can be found at: Email Alerts: http://ebx.sagepub.com/cgi/alerts Subscriptions: http://ebx.sagepub.com/subscriptions Reprints: http://www.sagepub.com/journalsReprints.nav Permissions: http://www.sagepub.com/journalsPermissions.nav

>> OnlineFirst Version of Record - Dec 15, 2011 What is This?

Downloaded from ebx.sagepub.com at UNIV CALGARY LIBRARY on April 16, 2013

421007 neau et al.Journal of Emotional and Behavioral Disorders © Hammill Institute on Disabilities 2010

EBXXXX10.1177/1063426611421007Letour

Reprints and permission: http://www. sagepub.com/journalsPermissions.nav

Socioeconomic Status and Child Development: A Meta-Analysis

Journal of Emotional and Behavioral Disorders XX(X) 1–14 © Hammill Institute on Disabilities 2011 Reprints and permission: http://www. sagepub.com/journalsPermissions.nav DOI: 10.1177/1063426611421007 http://jebd.sagepub.com

Nicole Lyn Letourneau1, Linda Duffett-Leger1, Leah Levac1, Barry Watson1, and Catherine Young-Morris1

Abstract Lower socioeconomic status (SES) is widely accepted to have deleterious effects on the well-being and development of children and adolescents. However, rigorous meta-analytic methods have not been applied to determine the degree to which SES supports or limits children’s and adolescents behavioural, cognitive and language development. While SES is largely determined by combinations of variables such as parental education level, marital status, and income, SES may also interact with other variables mediating or moderating the influence of SES on children’s behavior and cognitive development. Thus, the objective of this study was to conduct a meta-analysis of research on the relationship between composite measures of SES and developmental outcomes for children and adolescents between the ages of birth to 19 years of age.The results revealed very small to small, but significant effects of SES on aspects of the three outcome variables of literacy and language, aggression, and internalizing behaviours including depression. Many other factors come in to play that may better explain the small, but significant relationship between SES and development. Given the small observed associations, policy makers and programmers may focus interventions on family and community factors that contribute to child and adolescent developmental outcomes across the socioeconomic spectrum. Keywords externalizing behavior(s), internalizing behavior(s), families/parent(s), socioeconomics, language disorders/disabilities

Lower socioeconomic status (SES) is widely accepted to have a deleterious effect on the well-being and development of children and adolescents, including internalizing behaviors, externalizing behaviors, and cognitive and language development (Keating & Hertzman, 1999; Mendelson, Kubzansky, Datta, & Buka, 2008; Willms, 2002b). The degree to which a child’s environment supports or limits their developmental potential is largely determined by combinations of variables that comprise SES measures (BrooksGunn, Duncan, & Britto, 1999; National Research Council, 2000). Measures of SES are typically comprised of combinations of at least two of the following: parental education level, parental marital status, parental employment status, parental occupation prestige, and household income and eligibility for subsidy (Ensminger & Fotherill, 2003), hereafter referred to as “composite” measures. SES may also interact with other variables mediating or moderating the influence of other key variables predicting children’s development in behavioral and cognitive domains. SES may exert different influences on child and adolescent development at different stages in life and through different avenues, including parental resources, social support, and parental mental health (Kalil & DeLeire, 2004; McLoyd & Shanahan, 1993). Although some argue that lower SES

affects child development through parents being unable to provide the tangible, material resources necessary for healthy child development (Becker, 1991), others argue that parental stress and behavior predict developmental outcomes in children (McLoyd & Wilson, 1990; McMahon & Peters, 2002). Still others offer a combination of these arguments, suggesting that parents in stressful economic situations are unable to provide tangible or intangible resources necessary to support children’s and adolescents’ successful development (Conger et al., 1992; Elder, Conger, Foster, & Ardelt, 1992; Elder, Nguyen, & Caspi, 1985; Lempers, Clark-Lempers, & Simons, 1989; McLoyd & Shanahan, 1993; McLoyd & Wilson, 1990). Low SES is also associated with poor parental mental health (Petterson & Albers, 2001), which is linked to unsupportive, inconsistent, and uninvolved parenting styles and poor caregiver–child attachment (Crittenden, 2008; Meadows, McLanahan, & BrooksGunn, 2007). Social isolation and exclusion, which contribute 1

University of Calgary, Calgary, Alberta, Canada

Corresponding Author: Nicole Lyn Letourneau, University of Calgary, Faculties of Nursing & Medicine (Pediatrics), 2500 University Drive NW, Calgary,T2N 1N4, Canada Email: [email protected]


2

Journal of Emotional and Behavioral Disorders XX(X)

to the incidence of internalizing and externalizing behaviors, are also associated with low SES (Federal/Provincial/ Territorial Advisory Committee on Population Health and Health Security, 2003). Children reared in environments with “relational poverty,” comprised of few social support network members, experience deprivation that negatively influences their cognitive neurodevelopment and behavior (Perry, 2007; Perry & Szalavitz, 2006). Early child development programs targeting low-SES children have been effective in promoting children’s (Olds, Kitzman, et al., 2007; Olds, Sadler, & Kitzman, 2007) and adolescents’ development (Dougherty, 2007). Children from countries with more shallow socioeconomic gradients (i.e., with more economic equality) experience improved literacy (Irwin, Siddiqi, & Hertzman, 2007; Willms, 2003) and mathematics skills (Case, Griffin, & Kelly, 1999). The gradient has been successfully flattened in populations provided with compensatory educational programming (Case et al., 1999) and well-baby care (Irwin et al., 2007). With few exceptions, studies examining programs to promote low-SES children’s development do not directly address the child’s SES by, for example, cash transfers (Gertler, 2004).

Guiding Framework The Total Environmental Assessment Model of Early Childhood Development (TEAM-ECD) framework (Irwin et al., 2007), developed for the World Health Organization’s Commission on the Social Determinants of Health, guides this review. TEAM-ECD draws from a wide range of theories about influences on children’s and adolescents’ development, including Bronfenbrenner’s Bioecological Model (1986), theories about biological embedding and developmental psychology (Brooks-Gunn et al., 1999; Gottesman & Hanson, 2005; Lupien, King, Meaney, & McEwen, 2000), social relationships (Putnam, 2000), and the determinants of health (Federal/Provincial/Territorial Advisory Committee on Population Health and Health Security, 2003; Raphael, 2004). The framework consists of interacting and interdependent spheres and suggests that SES influences child and adolescent developmental outcomes through individual, family, residential, and relational communities; early child development programs and services; regional, national, and global environments; and civil society. The TEAM-ECD framework was chosen due to its integration of multiple perspectives. Moreover, the framework places children’s development at the center and adds an important temporal component to understanding how children’s development is influenced over time and under changing conditions. The TEAM-ECD describes the interplay between the developing brain and external environment that sets the course for children’s growth and development across their life span. The primary source of environmental influence

on children’s development is the family, as parents and other family members are largely responsible for providing stimulation to children and controlling contact with the outside world. Chronic family conditions, such as domestic violence and health issues (e.g., physical and mental problems), can have a deleterious impact on children’s development, particularly when the primary caregiver is affected. The influence of family resources, social (e.g., parenting skills and education) and economic (e.g., wealth, occupational status, and dwelling conditions), on children’s development is mediated by access to societal resources (e.g., education, primary health care, and quality day care). The residential community relates to the neighborhood in which children reside and determines children’s access to growth fostering resources (e.g., parks) or potentially hazardous elements (e.g., pollution), whereas the relational community provides cues about parenting norms and provides children with a sense of self-worth and self-esteem. The TEAM-ECD also recognizes that investment in targeted ECD programs and services are important economic strategies that promote the quality of human capital (e.g., skills and abilities necessary for individuals to be productive members of society), reflected in a stronger and more vibrant workforce. Children’s early development is influenced by environmental factors at the regional (e.g., physical, social, political, and economic environments), national (e.g., policy and economics), and global (e.g., economic and social conditions of nations) levels. Civil society, consisting of nongovernmental bodies and civil society groups, is instrumental in promoting ECD by holding countries accountable for the development of well-child policies and stimulating governments and community action on the social determinants of ECD.

Rationale for Meta-Analysis Although many primary reviews exist (Bornstein & Bradley, 2003; Keating & Hertzman, 1999; McLoyd & Shanahan, 1993; Steinhauer, 1998; Willms, 2002a), with the exception of two meta-analyses of academic achievement (Sirin, 2005; White, 1982) and one of depression (Twenge & Nolen-Hoeksema, 2002), researchers have not attempted to apply rigorous meta-analytic methods to examine the magnitude of the relationship between SES and child and adolescent developmental outcomes. Although systematic reviews attempt to collate all empirical evidence that fits prespecified eligibility criteria to answer a specific research question, they may or may not include a meta-analysis (Higgins & Green, 2006). Previous reviews have often used more traditional methods, seeking to summarize and describe studies examining the influence of SES on a variety of child developmental outcomes. In contrast, metaanalytic methods may permit closer, more precise examination of these relationships (Higgins & Green, 2006;


3

Letourneau et al. Stroup et al., 2000). Moreover, meta-analysis of observational studies may provide a tool for helping understand and quantify sources of variability across studies (Stroup et al., 2000). Knowledge, not only of the associations but also sources of variability across studies and the statistical strength of associations between SES and child developmental outcomes, may provide greater direction to policy decision making and relevant programs. Without this knowledge, policy makers may be prone to over- or underestimation of the actual magnitude of relationships. Thus, the objective of this research was to conduct a systematic, rigorous, and exhaustive meta-analysis of research on the relationship between composite measures of SES and the development outcomes for children and adolescents between the ages of birth to 19 years. The specific research question is as follows: Research Question: What does meta-analysis reveal about the relationship between composite measures of SES and internalizing and externalizing behavior, and cognitive and language development in children and youth between the ages of birth to 19 years?

Method In the summer of 2009, an extensive literature search was conducted with no limitations on date of publication using the following databases: MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Cochrane Developmental Psychosocial and Learning Problems Group, The Cochrane Library, including the Cochrane Central Register of Controlled Trials, HealthSource: Nursing/ Academic Edition, ClinicalTrials.gov (U.S. National Institutes of Health), Dissertation Abstracts International, EMBASE, Educational Resources Information Centre, Latin American and Caribbean Health Sciences Literature, National Research Register (United Kingdom), PsycINFO, WorldCat, and Web of Science. To identify SES-relevant research articles, various terms and their variants were used, including socioeconomic, social status, social inequity, social gradient, poverty, low income, lone parents, single parents, occupational prestige, and education. Although previous meta-analyses on similar topics were not included in the final meta-analysis for this study, these existing reviews were reviewed for the inclusion of eligible studies. To capture child and adolescent developmental articles, search terms and their variants included the following: neonatal/infant/child/adolescent development and language, cognitive, social, behavioral, literacy, numeracy, mathematics, mental, and emotional development. The collection and analyses of research articles were carried out using EPPIReviewer 3.0, a web-based software program (EPPI-Centre, 2009). This software package assists with the organization of

data and, more specifically, the implementation of metaanalytic methods. Stroup and colleagues’ (2000) guidelines were followed for reporting on all aspects of the meta-analyses. The outcomes of interest to the review included: (a) externalizing behaviors, (b) internalizing behaviors, and (c) cognitive and language development. Externalizing behaviors are directed outwardly toward the social environment and are characterized by undercontrolled and outerdirected patterns of development such as hyperactivity or aggression, behaviors more commonly expressed by boys than girls (Achenbach & McConaughy, 1997; Furlong, Morrison, & Jimerson, 2004). Internalizing behaviors, such as anxiety and depression, are expressed as an overcontrolled and inner-directed pattern of development and are more commonly observed in girls compared with boys (Achenbach & McConaughy, 1997). Cognitive and language development, such as numeracy and literacy, are characterized by changes in mental skills that occur with increasing maturity and experience (Sternberg, 1999).

Inclusion Criteria Articles were eligible for inclusion if they included a composite measure of SES and at least one standardized measure of the developmental outcomes of interest: (a) externalizing behaviors (e.g., aggression, hyperactivity), (b) internalizing behaviors (e.g., anxiety, depression), and (c) cognitive and language development (e.g., literacy or expressive vocabulary). Composite measures of SES include any combination of income, education, marital status, occupational prestige, financial assets and liabilities, and eligibility for subsidy. For example, Hollingshead’s Four Factor Index (Bornstein et al., 2003) includes maternal and paternal education, marital status, and occupational prestige, whereas the Household Economic and Social Status Index (Barbarin & Richter, 2001) includes indicators of material resources such as financial assets (i.e., home ownership, access to housing) and monthly expenses and material possessions, reflecting the status of the household rather than an individual parent. Composite measures have also been used to characterize neighborhood SES based on, for example, rate of adult employment and median neighborhood income (Fanti, 2007). Researchers also developed their own composite measures of SES, which were typically a combination of the following: parental education level, parental marital status, parental employment status, parental occupation prestige, and household income. In some cases, eligibility for subsidized programs (i.e., lunch program, day care, welfare) was also included in composite indices. Using the variety of search terms, a total of 268,742 citations were extracted from the literature and uploaded to EPPI-Reviewer 3.0. Once duplicates, nonresearch articles, reviews, and qualitative studies were removed, more than


4


Total Reports Identified: 268,742

Composite SES: 41,080

Removed Papers Published Before 2000:7,429

Abstracts Screened: 33,651

Reports Excluded Based on Abstract: 31,457

Potential: 2,194

Unable to Retrieve: 181

Retrieved: 2,013 Quality Assessment and Data Extraction: 2,013 Included on Full Report: 33

Externalizing Meta-Analysis: N=7

Internalizing Meta-Analysis: N=5

Excluded: 1,980 Excluded: 14

Cognitive/Language MetaAnalysis: N=7

Figure 1. An illustration of the selection process of studies for meta-analysis Note: SES = socioeconomic status.

40,000 articles remained for examination. A feasibility assessment resulted in the research team’s decision to eliminate literature published prior to 2000 from further review. This decision was justified by the observation that 10 years is a standard range for a meta-analysis (e.g., Sirin, 2005) and that inclusion of articles published prior to 2000 would have subjected the review to the potential for historical bias. In other words, recent and older studies were conducted in widely variable policy contexts, making them potentially unsuitable for grouping or comparison. Significant policy changes have occurred in many countries, which have had made the experience of low SES qualitatively different depending on the era under study. For example, the 1996 overhaul of Canadian unemployment insurance had dramatic financial consequences for families experiencing heightened probabilities of unemployment. In the United States, the Earned Income Tax Credit experienced notable changes throughout the 1980s and 1990s having significant impacts on family SES. Finally, the Working Tax Credit in the United Kingdom was enacted in 2003, which caused material changes to SES among British residents. After the date limitation was applied, abstracts were sought on the remaining articles. Abstracts for 181 articles were not able to be retrieved and the remaining 2,013 articles were retrieved and screened according to the following

exclusion criteria: (a) did not focus on human children from birth to 19 years of age; (b) were not written in English or French; (c) were focused on children with physical or mental disabilities of demonstrated organic or genetic origin; (d) were focused on foster children; (e) were focused exclusively on neighborhood level data; (f) presented no variability in SES; (g) did not include composite measures of SES drawn from the main predictors—SES, social status, social inequity, social gradient, gradient effect, poverty, low income, lone parents, single parents, occupational prestige, and parental education status; (h) did not include internalizing or externalizing behavioral, or cognitive or language outcomes; (i) contained only general behavior as an outcome, (j) did not contain data suitable for conversion to effect size estimates, or (k) did not meet quality assessment criteria (described below). A total of 1,980 articles were excluded as they did not meet at least one, and frequently a combination of, the criteria above. By far, the most frequent reason for exclusion was due to the use of a single indicator (e.g., parental education) as opposed to a composite measure of SES. See Figure 1 for an illustration of the inclusion and extraction process.

Data Extraction and Quality Assessment A data extraction tool was developed to record article content (e.g., sample size, age of sample, statistical findings, specific outcome) within EPPI-Reviewer (EPPI-Centre, 2009) based on the format suggested by Stock (1994) and a general EPPI-Reviewer data extraction guideline. The tool was piloted by senior team members and reviewed by an external expert for accuracy and thoroughness. All team members and three research assistants conducted the data extraction and quality assessment. To ensure accuracy, senior team members extracted outcome and quality assessment data using accepted weight of evidence criteria (Gough, 2007). The dominant questions used for evaluation included: “Does the study’s design/method fit with the purpose of the review?” and “Does the study provide answers relevant to SES and child and adolescent development?” The “overall weight of evidence” score was used as a final determinant of exclusion when the articles failed to receive a rating of medium or high. In every case in which an article was identified as failing to achieve an acceptable rating, the article also failed to meet another inclusion criterion. Senior team members validated extracted data prior to finalizing each article for inclusion in the analysis. A mean of 86% agreement was achieved on an item-per-item basis between screeners on a random set of 29 abstracts. Coding procedures also required description of the actual sample (e.g., age, ethnicity, gender, number of participants, study design), consideration of the study’s links to the TEAMECD framework, and description of predictors, mediators, moderators, and outcomes of interest. Studies were coded


5

Letourneau et al. as either “cohort” in which participants were followed longitudinally or “cross-sectional” in which participants were measured once. In addition, whether a study used a “correlational” or “group” design was also captured and coded. In studies where the number of SES groups was greater than two, the highest and lowest SES groups were retained in the meta-analysis. A mediator was defined as a variable that accounted, at least in part, for the relationship between SES and the outcome, whereas a moderator qualified the relationship (Baron & Kenny, 1986).

Meta-Analyses As indicated by Rosenthal (Rosenthal, 1994) and Borenstein and colleagues (Borenstein, Hedges, Higgins, & Rothstein, 2009), data were converted to Hedges’s g from d (effect size for mean differences), r (correlations), or t and F (group designs). Beta coefficients from regression-type analyses were not included as accepted conversions do not currently exist (Borenstein et al., 2009). Hedges’s g is the standardized mean difference that transforms all effect sizes to a common metric that can be interpreted as the difference between two groups measured in reference to one standard deviation (Cooper & Hedges, 1994). Thus, the measure of effect for the meta-analysis was Hedges’s g, whereas the meta-analytic method was DerSimonian and Laird (random effects model, which reduces the weight given to articles with large sample sizes and increases the weights given to studies with smaller sample sizes) or inverse variance (fixed effects model, which gives greater weight to studies with less variance, usually studies with larger samples) as appropriate (Cohen, 1988; Lipsey & Wilson, 2001). In other words, meta-analysis of studies with high variance in sample sizes (e.g., 20 in one study vs. 20,000 in another study) was analyzed with DerSimonian and Laird. The effect size can take on positive and negative values and, based on the analysis, determines the direction of the effect (i.e., in favor of control or intervention group). Based on Cohen’s “rule of thumb,” a standardized mean effect below 0.2 is considered small, a value between 0.2 and 0.8 is considered medium, and a value more than 0.8 is considered large (Cohen, 1988). Although this is only considered an approximation and may ignore, for example, the significance of even a small effect, Cohen’s rule corresponds well to the distribution of effects across meta-analyses (Lipsey & Wilson, 2001). In general, multiplying the standard deviation of an outcome by Hedges’s g returns a value in accordance with the original scale (Cooper & Hedges, 1994). Our meta-analysis combines numerous articles, whereby SES is not always measured using the same methods or scale. Consequently, we cannot make such conversions with the final Hedges’s g

statistic. However, with respect to each individual article, the calculated Hedges’s g for each study may be multiplied by the corresponding standard deviation to obtain a statistic which is on the original scale. As studies examining the relationship between SES and child and adolescent development use a variety of methods and do not generally use randomized controlled trials, a variety of observational methods and statistical tests were captured in the review. Moreover, a variety of outcomes were to be combined in meta-analyses. As such, statistical assessments of heterogeneity were expected to reveal large and significant heterogeneity, even within the separate groups of externalizing and internalizing behavior and cognitive and language development. It is generally accepted that meta-analyses should assess heterogeneity, which may be defined as the variation in true effect sizes underlying different studies (Higgins, 2008; Stroup et al., 2000). Debate exists about the proper approach to statistical heterogeneity in meta-analysis, ranging from mere quantification to attempts to reduce heterogeneity (Patsopoulos, Evangelou, & Ioannidis, 2008a). Methods proposed for coping with heterogeneity include: (a) carrying out a narrative review and foregoing meta-analysis, (b) allowing or ignoring heterogeneity by using either fixed or random effects models, respectively, to model the data, and (c) exploring heterogeneity to explain and remove it (Higgins & Thompson, 2002; Higgins, Thompson, Deeks, & Altman, 2003). As the purpose of this study was to conduct a metaanalysis, the first method was not considered. The second method was not undertaken as it has been criticized for resulting in too narrow or too broad confidence intervals and pooled estimates (Higgins & Green, 2006). Thus, we used the third method to explore and remove heterogeneity. As suggested by Patsopoulos, Evangelou, and Ioannidis (2008b), a threshold for the heterogeneity statistic (I2) was sought. In this case, we sought to reduce the heterogeneity, to the degree possible, to a nonsignificant value ranging between approximately 50% and 75% (still moderate to large heterogeneity). Moreover, to add additional rigor, a variant of the combinatorial approach (Patsopoulos et al., 2008b), in which groups of studies may be removed to achieve the threshold I2, was used. This process may also be described as a form of sensitivity analysis, whereby some studies are excluded based on certain study characteristics thought to affect the homogeneity of the set of studies (e.g., widely disparate age ranges in samples; Song, Sheldon, Sutton, Abrams, & Jones, 2001). The potential for needing to reduce heterogeneity was anticipated a priori; thus, studies were categorized as data were extracted (e.g., according to study design, sample age and ethnicity, and outcome measure). These subsets were prespecified to avoid the potential for bias at the outset. These characteristic subsets


6


were used to eliminate combinations of studies until an acceptable I2 was attained and a more homogeneous metaanalytic set of studies was identified.

Results A total of 33 articles ultimately met all the inclusion criteria and contained complete data or data that could be converted to an unbiased Hedges’s g statistic from d, r, t, or F. Heterogeneity analysis and removal of studies, as appropriate, using the combinatorial approach (Patsopoulos et al., 2008b) left 19 studies for the three meta-analyses: 7 articles focused on externalizing behaviors, 5 focused on internalizing behaviors, and 7 focused on cognitive and language development, accounting for 22,745, 1,922, and 665 participants, respectively.

Sample Heterogeneity The final heterogeneity of the articles included in the externalizing behavior (Q = 12.2, df = 6, I2 = 51%, p = .058) and internalizing behavior (Q = 9.09, df = 4, I2 = 56%, p = .059) categories were not significant, whereas the heterogeneity of the cognitive/language development meta-analyses (Q = 26.8, df = 6, I2 = 78%, p = .0002) was. The assessment of heterogeneity revealed that the greatest drivers were the disparate sample ages and wide variety of measures included in the outcomes. In the assessment of cognitive and language development studies, the elimination of studies focused on cognitive development (as opposed to language and literacy) brought the I2 near the acceptable range; no combination of categories of articles removed from the meta-analytic set (i.e., according to outcome measure, design, or sample age) resulted in a nonsignificant I2. In the externalizing behavior set of studies, removal of the youngest cohorts (less than 6 years of age) and elimination of measures other than aggression resulted in a satisfactory reduction in the I2. In the internalizing behavior meta-analytic set, removal of studies that focused on anxiety disorders exclusively resulted in a satisfactory reduction in the I2.

Externalizing Behavior Seven articles contained data appropriate for the externalizing behavior meta-analysis (see Table 1) and focused primarily on adolescents aged 12 to less than 19 years. Studies were mostly cross-sectional, and all included studies focused on aggression as an outcome of interest. Samples included in this meta-analysis were largely drawn from the continental United States; however, one focused on Filipino adolescents in Hawaii. Dersimonian and Laird test statistic revealed a very small but significant relationship between SES and aggression (Hedges’s g = 0.06,

confidence interval [CI] = [0.02, 0.11], p = .007); thus, increases in SES are related to decreases in aggression. Six of the seven reviewed studies showed that aggression increased as SES decreased among adolescents (Guerrero, Hishinuma, Andrade, Nishimura, & Cunanan, 2006; Hay, Forston, Hollist, Altheiner, & Schaible, 2007; McElroy, 2005; Nyaronga, 2006; Veenstra, Lindenberg, Oldehinkel, De Winter, & Ormel, 2006; Wadsworth & Compas, 2002) and young children (McElroy, 2005). Only one study found no significant relationship between SES and aggression (Dekovic, Janssens, & Van As, 2003). Rather, parental monitoring, consistency, competence, and family cohesion predicted aggression in adolescents (Dekovic et al., 2003). Veenstra and colleagues (2006) also found moderation of the effect of SES by gender and temperament. In other words, higher SES may be protective for males and children with a difficult temperament. In Guerrero and colleagues’ (2006) study, family support emerged as a strong mediating factor for the relationship between SES and aggression. Hay and colleagues (2007) found high school children living in families relatively better off socioeconomically than their neighbors to be at reduced risk for aggression, leading them to conclude that community poverty may be a better predictor of aggressive delinquent behaviors than family SES.

Internalizing Behavior The five articles (see Table 2) included in the internalizing behavior meta-analysis had a less homogeneous age focus; participants ranged across all considered ages, with the majority of the studies focusing on children between the ages of 6 and 12 years or adolescents between the ages of 12 and 19 years. These studies used internalizing behavior or depression as an outcome of interest and were largely cross-sectional, and half were based on ethnically diverse samples drawn from the continental United States; however, one focused on South African children, another on Moroccan immigrant youth in the Netherlands, and one on Filipino adolescents in Hawaii. Using the inverse variance procedure, the meta-analysis of these studies revealed a very small but significant relationship between SES and internalizing behaviors (Hedges’s g = 0.08, CI = [0.01, 0.16], p = .023); that is, lower levels of SES were associated with higher levels of internalizing behaviors, including depression. Both Daillaire et al. (2008) and Guerrero et al. (2006) found that lower SES predicted increased depressive symptoms; however, mediation was also present in both cases. The relationship between SES and depression was mediated by negative parenting behaviors (Dallaire et al., 2008) or by increased family support (Guerrero et al., 2006). One study demonstrated moderation by gender in higher rates of


7

Letourneau et al. Table 1. Summary of the Seven Studies on Externalizing Behavior Included in the Meta-Analysis

Author(s)

N

Age range

Findings and mediator or moderator variables

Outcome

Study design

Variables included in the composite measure of SES

Dekovic, Janssens, and Van As (2003)

508 12 to