Executive function and psychosocial adjustment in healthy children and adolescents: A latent variable modelling investigation.

Child Neuropsychology A Journal on Normal and Abnormal Development in Childhood and Adolescence

ISSN: 0929-7049 (Print) 1744-4136 (Online) Journal homepage: http://www.tandfonline.com/loi/ncny20

Executive function and psychosocial adjustment in healthy children and adolescents: A latent variable modelling investigation Adam R. Cassidy To cite this article: Adam R. Cassidy (2015): Executive function and psychosocial adjustment in healthy children and adolescents: A latent variable modelling investigation, Child Neuropsychology, DOI: 10.1080/09297049.2014.994484 To link to this article: http://dx.doi.org/10.1080/09297049.2014.994484

Published online: 08 Jan 2015.

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Child Neuropsychology, 2015 http://dx.doi.org/10.1080/09297049.2014.994484

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Executive function and psychosocial adjustment in healthy children and adolescents: A latent variable modelling investigation Adam R. Cassidy Center for Neuropsychology, Department of Psychiatry, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA The objective of this study was to establish latent executive function (EF) and psychosocial adjustment factor structure, to examine associations between EF and psychosocial adjustment, and to explore potential development differences in EF-psychosocial adjustment associations in healthy children and adolescents. Using data from the multisite National Institutes of Health (NIH) magnetic resonance imaging (MRI) Study of Normal Brain Development, the current investigation examined latent associations between theoretically and empirically derived EF factors and emotional and behavioral adjustment measures in a large, nationally representative sample of children and adolescents (7–18 years old; N = 352). Confirmatory factor analysis (CFA) was the primary method of data analysis. CFA results revealed that, in the whole sample, the proposed five-factor model (Working Memory, Shifting, Verbal Fluency, Externalizing, and Internalizing) provided a close fit to the data, χ2(66) = 114.48, p < .001; RMSEA = .046; NNFI = .973; CFI = .980. Significant negative associations were demonstrated between Externalizing and both Working Memory and Verbal Fluency (p < .01) factors. A series of increasingly restrictive tests led to the rejection of the hypothesis of invariance, thereby precluding formal statistical examination of age-related differences in latent EF-psychosocial adjustment associations. Findings indicate that childhood EF skills are best conceptualized as a constellation of interconnected yet distinguishable cognitive self-regulatory skills. Individual differences in certain domains of EF track meaningfully and in expected directions with emotional and behavioral adjustment indices. Externalizing behaviors, in particular, are associated with latent Working Memory and Verbal Fluency factors. Data used in the preparation of this article were obtained from the Pediatric MRI Data Repository created by the NIH MRI Study of Normal Brain Development. This is a multisite, longitudinal study of healthy children, from ages newborn through young adulthood, conducted by the Brain Development Cooperative Group and supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke (Contract #s N01-HD02-3343, N01-MH9-0002, and N01-NS-9-2314, −2315, −2316, −2317, −2319 and −2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://www. bic.mni.mcgill.ca/nihpd/info/participating_centers.html. This article reflects the views of the author and may not reflect the opinions or views of the NIH. The author has no conflicts of interest to declare. Portions of these data were presented at meetings of the American Academy of Clinical Neuropsychology in Chicago (2010) and Washington DC (2011), at the 2013 meeting of the International Neuropsychological Society in Hawaii, and in the author’s doctoral dissertation at the University of Minnesota in 2010. The author would like to thank Nicki R. Crick for her support of this project, Jane Holmes Bernstein, Megan Cassidy, Monica Luciana, Julie Markant, Katie Thomas, Debbie Waber, and Rich Weinberg for their thoughtful comments on earlier versions of this manuscript, and the children and families who participated in the NIH MRI Study of Normal Brain Development. Address correspondence to Adam R. Cassidy, Center for Neuropsychology, Department of Psychiatry, Boston Children’s Hospital, Boston, MA 02115, USA. E-mail: [email protected]

© 2015 Taylor & Francis

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Keywords: Executive function; Externalizing; Internalizing; Latent variable modelling; Working memory; Verbal fluency; Shifting.

Beginning in infancy and extending well into adolescence and young adulthood, the gradual emergence and refinement of executive function (EF) skills scaffolds the development of social competence (Diamond, 2013; Zelazo & Cunningham, 2007). Indeed, children and adolescents with stronger EF skills are, on average, more socially adept, demonstrate more advanced academic skills and perform better in school than their lower EF counterparts (e.g., Beauchamp & Anderson, 2010; Bull & Lee, 2014; Calkins & Marcovitch, 2010; Ciairano, Visu-Petra, & Settanni, 2007; Clark, Prior, & Kinsella, 2002; Morrison, Ponitz, & McClelland, 2010; Razza & Blair, 2009; Riggs, Jahromi, Razza, Dillworth-Bart, & Mueller, 2006). These children also seem to be at lower risk for emotional and behavioral problems (e.g., Emerson, Mollet, & Harrison, 2005; Giancola, Martin, Tarter, Pelham, & Moss, 1996; Giancola, Mezzich, & Tarter, 1998a, 1998b; Giancola, Moss, Martin, Kirisci, & Tarter, 1996; Kusché, Cook, & Greenberg, 1993; Kyte, Goodyer, & Sahakian, 2005; Martel et al., 2007; Nigg, Quamma, Greenberg, & Kusché, 1999; Pennington & Ozonoff, 1996; Raaijmakers et al., 2008; Rhoades, Greenberg, & Domitrovich, 2009; Riggs, Blair, & Greenberg., 2003; Séguin, Boulerice, Harden, Trembley, & Pihl, 1999; Yeates et al., 2007); however, to date, most studies looking to investigate associations between EF and psychosocial adjustment indices have relied on traditional data analytic approaches (e.g., correlation and/or exploratory factor analysis) that are seriously limited in their power to inform conclusions about complex, multifactorial cognitive skills such as EF (Miyake et al., 2000). In this article, I present the results of a latent variable modelling investigation linking EF and psychosocial functioning in a large, population-based sample of healthy children and adolescents who participated in the National Institutes of Health (NIH) magnetic resonance imaging (MRI) Study of Normal Brain Development. Executive Function The term “executive function” generally refers to an array of cognitive and selfregulatory skills employed in the guidance of future-oriented behavior (Diamond, 2013; Robbins, 1996; Stuss, 1992; Zelazo, Müller, Frye, & Marcovitch, 2003) and often includes constructs such as working memory, inhibitory control, shifting, and verbal fluency, among others. EF skills emerge over the course of childhood, adolescence, and young adulthood, in accordance with known patterns of increasing refinement and integration of frontal/prefrontal, parietal, cerebellar, and subcortical networks (Champod & Petrides, 2010; Conklin, Luciana, Hooper, & Yarger, 2007; Diamond, 2002; Little et al., 2010; Provost, Petrides, & Monchi, 2010; Robbins, 1996; Stoodley & Schmahmann, 2010). Structure and Organization of EF Whereas some have posited a unitary/domain-general understanding of EF based on the notion that seemingly varied EFs are actually manifestations of—or are facilitated by— the same fundamental mechanism (e.g., Duncan, Johnson, Swales, & Freer, 1997), others have suggested that EF is multifaceted, comprised of distinct yet related component processes (Brocki & Bohlin, 2004; Lee, Bull, & Ho, 2013; Lehto, Juujarvi, Kooistra, &

EXECUTIVE FUNCTION AND PSYCHOSOCIAL ADJUSTMENT

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Pulkkinen, 2003; Levin et al., 1991; Miyake et al., 2000; van der Ven, Kroesbergen, Boom, & Leseman, 2012; Welsh, Pennington, & Groisser, 1991). Developmental studies have contributed importantly to this ongoing discussion. Looking across the lifespan, evidence suggests that preschool/early school-age EF skills emerge as primarily unitary in nature (Garon, Bryson, & Smith, 2008; Hughes & Ensor, 2011; Hughes, Ensor, Wilson, & Graham, 2010; Wiebe, Espy, & Charak, 2008, 2011) with subsequent differentiation and integration across childhood/adolescence (Huizinga, Dolan, & van der Molen, 2006; Lee et al., 2013; Lehto et al., 2003; van der Sluis, De Jong, & van der Leij, 2007), and into adulthood (e.g., Miyake et al., 2000). The “Task Impurity Problem” and Advantage of a Latent Variable Modelling Approach Higher order cognitive skills such as EF are, by definition, multifactorial, assessed via performance on tasks that require the recruitment of numerous lower order executive and nonexecutive processes for successful completion (Miyake et al., 2000; van der Sluis et al., 2007). Consequently, there is no such thing as a “pure” measure of EF (the so-called “task impurity problem”; Denckla, 1994; Zelazo, Carter, Reznick, & Frye, 1997), and observed associations between performance on a given EF task and a relevant psychosocial measure may be artificially inflated or diminished by a multitude of extraneous factors that cannot be adequately managed by traditional statistical techniques such as correlation or exploratory factor analysis. Latent variable modelling (e.g., confirmatory factor analysis, structural equation modelling), in contrast, allows one to create theoretically meaningful latent constructs by combining shared variance components across multiple manifest indicators (i.e., behavioral tasks, rating scales) of a putative ability or trait, while at the same time minimizing error (Brown, 2006). For example, working memory is recognized as having verbal and visuospatial components that can be measured via performance on different behavioral tasks (Alloway, Gathercole, & Pickering, 2006). Although distinct in terms of modality, the common variance shared between one’s scores on these tasks may be considered to constitute a hypothetical “latent” (i.e., not directly measurable) Working Memory factor that can be modelled in latent space. Miyake and colleagues (2000) were the first to apply these methods to the study of EF, effectively launching a novel and informative approach to a domain of research long hampered by serious psychometric and statistical limitations. EF and Psychosocial Adjustment Considerable research suggests that individual differences in EF track meaningfully with aspects of psychosocial functioning at both clinical and subclinical levels. Séguin and colleagues (e.g., 1999, 2004), for example, reported both concurrent and predictive associations between working memory deficits and physical aggression in a large community sample of healthy children in Canada. Deficits in EF, particularly working memory and shifting, have also been documented in community samples rated by informants as high in internalizing symptoms (e.g., Kusché et al., 1993), as well as in children presenting with a clinically significant depressive disorder (e.g., Emerson et al., 2005; Forbes, Shaw, & Dahl, 2007; Halari et al., 2009; Kyte et al., 2005). Deficits in verbal fluency and inhibitory control have likewise been linked to both externalizing (Martel et al., 2007; Raaijmakers et al., 2008; Riggs et al., 2003; Stanford, Greve, &


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Gerstle, 1997) and internalizing behavior problems (Baune, Fuhr, Air, & Hering, 2014; Nigg et al., 1999; Rhoades et al., 2009). Despite mounting evidence of the usefulness of latent variable analysis in characterizing the latent factor structure and organization of EF in both adults (e.g., Miyake et al., 2000) and children (Huizinga et al., 2006; Lee et al., 2013; Lehto et al., 2003; van der Sluis et al., 2007), a scant few have harnessed these powerful techniques to examine associations between EF and emotional/behavioral functioning. Utilizing structural equation modelling, Romer et al. (2009), for example, showed that preadolescent risk taking and externalizing behaviors were related to impulsivity. This same group also highlighted prospective relations between preadolescent sensation-seeking and adolescent externalizing behavior problems (Romer et al., 2011). In a sample of healthy younger children, Hughes and Ensor (2011) found that latent growth in EF skills across the transition from preschool to first grade predicted externalizing, internalizing, and self-perceived academic competence. Current Study The current study extended previous investigations by examining latent associations between theoretically and empirically derived EF factors and parent-reported emotional and behavioral adjustment in a large sample of healthy children and adolescents. The four primary objectives and associated hypotheses were as follows: (1) Model the Latent Factor Structure of EF: Participants were school-age children and adolescents; therefore, a fully interrelated three-factor model (Working Memory, Shifting, and Verbal Fluency) was predicted to best represent EF structure and organization across this age range. (2) Model the Latent Factor Structure of Emotional/Behavioral Adjustment: Based on extant previous research (e.g., Achenbach, 2001), an interrelated two-factor model (Internalizing and Externalizing) was predicted. (3) Examine Latent Associations between EF Factors and Emotional/Behavioral Adjustment: Working Memory, Shifting, and Verbal Fluency factors were predicted to demonstrate significant, negative latent associations with Internalizing and Externalizing factors. (4) Investigate Developmental Differences in Associations between EF Factors and Emotional/Behavioral Adjustment: Societal expectations regarding goal-directed behavior and self-regulation increase over the course of childhood and adolescence (Thompson, 1994; Thompson & Meyer, 2007), raising the possibility that relative weaknesses in EF skills may become stronger predictors of psychosocial difficulties as individuals move from childhood into adolescence. The final study objective was to test this hypothesis by exploring age-related differences in relations between EF and psychosocial adjustment across developmental subgroups.

METHOD Procedures Recruitment procedures for the multisite NIH MRI Study of Normal Brain Development were extensive, involving population-based stratification of the sample by



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gender, race, and socioeconomic status according to the 2000 United States census and screening for medical, developmental, genetic, and psychiatric conditions with known or suspected neurological involvement (see Evans, 2006; Waber et al., 2007). Of particular significance to the current study was the exclusion of individuals with Child Behavior Checklist (CBCL) subscale scores ≥ 70, as this limited the sample to children/adolescents without clinically significant emotional or behavioral problems according to parent report. Individuals with estimated full-scale intelligence quotient scores < 70 were also excluded “to allow for inclusion of as broad a range of cognitive variability as possible” while excluding children with frank intellectual disability (Waber et al., 2007, p. 733). As such, the final sample is thought to be generally representative of healthy US children and adolescents. Following completion of screening measures, participants were invited to the study site for clinical, behavioral, and neuroimaging data collection. The clinical and behavioral assessment was completed in a single, 2- to 3-hour testing session (with breaks provided, as needed) during which a wide range of well-standardized neurobehavioral measures was administered in a fixed order. Measures of EF and parent-reported psychosocial adjustment were used in the current investigation. Data for this study were obtained directly from the purveyors of the Pediatric MRI Data Repository, in accordance with outlined procedures for data access to qualified researchers. No identifying information was accessible to the author.

Participants A total of 352 children and adolescents (184 female; 168 male) ranging in age from 7 to 18 years (M = 11.43 years, SD = 3.43) comprised the final sample for the current study. Children younger than 7 years of age were excluded due to differences in the test batteries administered. Sample demographic characteristics are depicted in Table 1.

Measures Executive Functions. The EF construct was operationalized as broadly as possible, given the constraints of secondary data analysis, and included two well-studied “core” EF components (i.e., working memory and shifting; see Diamond, 2013, for review), as well as verbal fluency, an aspect of EF that, to date, has not been looked at in relation to psychosocial adjustment within a latent modelling framework. As outlined above, all three of these factors are thought to be critically involved in psychosocial adjustment. Of note, deficits in inhibitory control—another core EF component (Diamond, 2013)—have also been found in children with externalizing (e.g., Hoaken, Shaughnessy, & Pihl, 2003; Martel et al., 2007; Nigg et al., 1999; Raajimakers et al., 2008; Riggs et al., 2003; Stanford et al., 1997) and internalizing behavior problems (e.g., Rhoades, Greenberg, & Domitrovitch, 2009; Riggs et al., 2003); however, behavioral measures of inhibitory control were not included in the NIH MRI Study protocol and thus could not be examined in the current study. Cambridge Neuropsychological Test Automated Battery (CANTAB; CeNeS, 1998). The CANTAB is an automated battery of mostly nonverbal

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A. R. CASSIDY Table 1 Sample Demographic Characteristics (Total N = 352). Characteristic


Age in years (n, % of sample)

Sex (% female) Handedness (% right-handed) Intellectual functioning (WASI): Full-4 IQ: mean (SD) Verbal IQ: mean (SD) Performance IQ: mean (SD)

Distribution (%) 7 8 9 10 11 12 13 14 15 16 17 18

55 36 38 35 27 31 26 23 20 22 24 15 184 317

(15.6%) (10.2%) (10.8%) (9.9%) (7.7%) (8.8%) (7.4%) (6.5%) (5.7%) (6.3%) (6.8%) (4.3%) (52.3%) (90.1%)

110.84 (12.14) 110.12 (13.26) 109.12 (12.37)

Family income: Low (less than $35,000/year) Medium ($35,000 to $75,000/year) High (more than $75,000/year)

85 (24.1%) 138 (39.2%) 129 (36.6%)

Racial/ethnic group: White African American Asian American Indian/Alaskan Native Hispanic Biracial/Multiracial

260 28 4 1 42 17

Geographic region: East Midwest West

104 (29.5%) 132 (37.5%) 116 (33.0%)

(73.9%) (8.0%) (1.1%) (0.3%) (11.9%) (4.8%)

Note. WASI = Wechsler Abbreviated Scale of Intelligence (1999).

neuropsychological tests presented visually, using a portable touchscreen computer. The following three subtests were included in the current study: Spatial Span (SSP): This task measures memory for a sequence of visually presented information. Participants viewed a lighted sequence after which they were asked to reproduce that sequence by touching boxes on the computer screen in the order in which the lights were presented. The longest sequence successfully recalled served as the dependent variable. Spatial Working Memory (SWM): This self-ordered search task is a spatial working memory task that required participants to carry out an organized search of various containers to find hidden tokens. Measures of between-trial and within-trial search errors were obtained. Error scores were reverse-coded for analysis.


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Intradimensional/Extradimensional Set Shift (IED): The IED task is a measure of discrimination and reversal learning that required participants to use feedback to learn response contingencies. Measures of total errors (reverse-coded for analysis) and total number of stages completed were obtained.

Digit Span. This verbal working memory task required participants to listen and repeat random digit strings of increasing length (forward and backward). Participants were administered either the Wechsler Intelligence Scale for Children, third edition (Wechsler, 1991; ages 7–17 years) or the Wechsler Adult Intelligence Scale, third edition (Wechsler, 1997; ages ≥ 17 years) version of the Digit Span task. Raw digit-span scores from forward and backward conditions served as the dependent variables. Verbal Fluency. The verbal fluency tasks used in this study were patterned after the NEPSY: A Developmental Neuropsychological Assessment word-generation test (Korkman, Kirk, & Kemp, 1998) and included both semantic (animals and foods/drinks) and phonetic (initial letter) conditions. The dependent variable was the total number of words generated for each condition. Behavior Rating Inventory of Executive Functions (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000). The BRIEF is a parent-report questionnaire designed to assess real-world EF abilities. Raw scores from the working memory and shift subscales served as the dependent variables in this study. BRIEF subscales were reverse coded for ease of interpretation. Psychosocial Functioning Child Behavior Checklist (CBCL; Achenbach, 2001). The CBCL is a parentreport questionnaire for assessing emotional and behavioral functioning. The CBCL yields scores for eight subscales (withdrawn-depressed, anxious-depressed, somatic problems, aggressive behavior, rule-breaking behavior, attention problems, social problems, thought problems). Raw subscale scores were included in analyses. Intelligence Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999). The WASI is a four-subtest measure of intellectual functioning that yields a full-scale intelligence quotient (IQ) score, as well as index scores for Verbal IQ and Performance IQ. Data Analysis Preliminary data analyses were conducted using SPSS 17.0 (2008) and PASW Statistics 18.0.0 (2009). Confirmatory factor analyses were conducted using LISREL 8.80 (Joreskog & Sorbom, 2007). Variance/covariance matrices were input to LISREL. Missing Data Missing data are problematic in latent variable modelling, especially when dealt with using traditional methods such as pairwise deletion, listwise deletion, or mean-


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replacement (Baraldi & Enders, 2010; Graham, Cumsille, & Elek-Fisk, 2003; McDonald & Ho, 2002). In the current study, missing data accounted for no greater than 2–3% of most variables of interest, with the exceptions of 9.1% (n = 32) missing for verbal fluency measures and 17.9% (n = 63) missing for CBCL rule-breaking and withdrawn-depressed subscales. Participants with missing CBCL, F(1, 350) = 8.25, p = .004, and verbal fluency, F(1, 350) = 61.19, p < .001, data were, on average, younger than those with complete data; there were no significant differences between groups in race, ethnicity, or WASI-4, full-scale, verbal, or performance IQ scores. Missing data were imputed using Full Information Maximum Likelihood procedures (LISREL-PRELIS 8.80). Given the higher frequency of missing data for CBCL rule-breaking and withdrawn/depressed subscales and verbal fluency measures, manifest correlations between these variables and age were examined using both imputed and nonimputed data with pairwise deletion (n = 289–351). Findings revealed an identical pattern of correlations, suggesting that subsequent analyses would not be unduly influenced by the imputation methods utilized. Primary analyses were performed using fully imputed data.

RESULTS Descriptive Results Descriptive statistics for EF and psychosocial functioning variables are depicted in Tables 2 and 3, respectively. Two EF variables (SWM-Within Trial Errors and IED-Stages Completed) were significantly skewed (skewness = 3.68 and −2.61, respectively) and, thus, were excluded from subsequent analyses. All other EF variables were relatively normally distributed. Psychosocial functioning variables were also within the generally acceptable range of skewness.

Intraconstruct Correlations Zero-order correlation coefficients were calculated separately for EF and psychosocial functioning variables (see Tables 4 and 5, respectively). With the exception of Table 2 Descriptive Statistics for Executive Function Measures (N = 352). Measure SSP SWM (BTE) SWM (WTE) Digit Span—Forward Digit Span—Backward BRIEF Working Memory IED (Errors) IED (Stages Completed) BRIEF Shift Verbal Fluency (Phonemic) Verbal Fluency (Semantic)

M

SD

Min

Max

Skewness

Kurtosis

5.93 32.66 1.74 8.59 5.57 13.83 23.48 7.92 10.35 22.94 33.64

1.71 21.33 2.89 2.12 2.02 3.57 14.05 1.55 2.30 11.09 11.24

0 0 0 4 2 10 0 0 8 0 0

9 97 24 15 12 28 69 9 17 61 87

.40), and in the expected directions on their respective latent factors (see Figure 3). Significant positive relations were observed among all three latent EF factors (p < .001), as well as between latent Externalizing and Internalizing factors (p < .001). Externalizing was significantly negatively associated with Working Memory (p < .01) and Verbal Fluency (p < .01) factors. Objective 4: Investigate Developmental Differences in Associations between EF Factors and Emotional/Behavioral Adjustment Creation of Subgroups. To facilitate analysis of developmental differences in associations between EF and emotional/behavioral adjustment factors, it was necessary to subdivide participants by age. Given prohibitively small cell sizes for some ages, groups were created by collapsing across ages thought to reflect four salient developmental periods: early childhood (7- to 8-year-olds; n = 91; 53.5% female), middle childhood (9- to 11-year-olds; n = 100; 57.0% female), early adolescence (12- to 14-year-olds; n = 80; 48.8% female), and late adolescence (15-to 18-year-olds; n = 81; 48.1% female). Descriptive statistics for EF and psychosocial functioning variables, separated by age group, are provided in Table 8. Measurement Invariance. Prior to examining group differences, a series of increasingly restrictive steps, outlined in Brown (2006), was performed to test the hypothesis of multigroup invariance. First, baseline CFA models were fit separately for each group. Except for three freed error parameters (attention problems-social problems in

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SSP .67 .73

WM

.62

SWM DS_F

.73

DS_B

.36 .85

–.18

Shift

1.00

.27


.86

VFl

–.09

–.12

IED_err

.80

–.16

VF_P VF_S Agg

–.03

.96

EXT

–.10

.62 .73

.63 .72

INT

.66

R1Brk AttnProb SocProb AnxDep

.62 .44

WithDep Som

Figure 3 Confirmatory factor analysis of latent EF and psychosocial functioning constructs. Values depicted are completely standardized factor loadings. Freed residual estimates and latent factor variances (fixed at 1.0) were omitted for ease of presentation. EXT = Externalizing; INT = Internalizing; WM = Working Memory; VFl = Verbal Fluency; SSP = Spatial Span; SWM = Spatial Working Memory (Between Trial Errors), DS_F = Digit Span Forward; DS_B = Digit Span Backward; IED = Intradimensional/Extradimensional Shift; VF_P = Verbal Fluency Phonetic; VF_S = Verbal Fluency Semantic; Agg = Aggression; RlBrk = Rule Breaking; AttnProb = Attention Problems; SocProb = Social Problems; AnxDep = Anxious-Depressed; WithDep = Withdrawn-Depressed; Som = Somatic Complaints.

both adolescent groups; aggression-attention problems in the early adolescent group), model structure was relatively consistent across groups. Model fit was adequate for each group with fit indices ranging from mediocre to close (see Table 9). Second, a simultaneous multigroup CFA was conducted with the pattern of fixed and freed parameters retained from previous separate models. Global fit of the model was acceptable, χ2(362) = 362.80, p < .001; RMSEA = .063; NNFI = .910; CFI = .933; however, differences in factor loadings were observed across groups; for example, among 12- to 14-year-olds only, spatial span failed to load significantly on Working Memory. Moreover, several factor loadings were found to be nonsalient contributors to their respective models, as indicated by completely standardized factor loading below .40. Nonsalient indicators included spatial span (all four groups), spatial working memory (late adolescent group), digit span-forward (early childhood group), digit span-backward (early childhood group), and somatic complaints (early childhood and early adolescent groups). The findings outlined above suggested that conditions for establishing measurement invariance were not met. A subsequent test, wherein equality constraints were imposed on


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Table 8 Descriptive Statistics for EF and Psychosocial Functioning Variables by Age Group. Age group


Early Childhood

SSP SWM (BTE) DS (Forward) DS (Backward) IED (Errors) VFl (Phonemic) VFl (Semantic) Aggression Rule Breaking Attn Problems Soc Problems Anx/Dep With/Dep Somatic Age (years)

Middle Childhood

Early Adolescence

Late Adolescence

M

SD

M

SD

M

SD

M

SD

4.58 48.37 7.13 4.27 26.88 12.73 23.57 3.09 1.41 1.86 1.09 1.85 0.77 0.71 7.40

1.00 19.78 1.50 1.41 16.11 6.74 6.73 2.94 1.37 2.25 1.29 1.90 1.05 1.10 0.49

5.38 39.70 8.23 4.89 26.84 20.72 32.84 2.49 0.98 1.70 1.19 1.51 0.63 0.84 9.89

1.16 17.88 1.85 1.63 11.90 8.46 8.51 2.74 1.29 2.08 1.49 1.62 0.98 1.30 0.80

6.56 23.69 9.71 6.26 20.35 27.05 36.95 2.56 0.73 1.73 0.89 1.78 0.91 0.83 12.90

1.58 16.30 2.02 1.89 13.76 8.14 9.15 2.78 0.94 2.03 1.30 1.84 1.24 1.28 0.82

7.52 15.16 9.56 7.16 18.62 33.11 42.68 1.86 0.84 1.80 0.75 1.49 1.20 0.83 16.42

1.46 12.81 2.04 1.82 12.28 9.39 11.03 2.23 1.59 2.42 1.41 1.91 1.60 1.25 1.06

Note. SSP = Spatial Span; SWM (BTE) = Spatial Working Memory (Between Trial Errors); DS = Digit Span; IED = Intradimensional/Extradimensional Shift; VFl = Verbal Fluency.

Table 9 Fit Indices for Full CFA Models by Age Group. Group 1 2 3 4

Early childhood Middle childhood Early adolescence Late adolescence

n

df

χ2

AICa

RMSEAa

NNFIb

CFIb

91 100 80 81

68 68 67 67

91.54 106.67** 89.30* 75.28

165.54 180.67 165.30 151.29

.062 .076 .065 .039

.895 .859 .869 .977

.921 .895 .904 .983

Note. AIC = Akaike Information Criterion; RMSEA = Root Mean Square Error of Approximation; NNFI = Non-Normed Fit Index; CFI = Comparative Fit Index. a Lower values represent better model fit; RMSEA ≤ .05 indicates a close fit of the model. b Values higher than .95 indicates a close fit of the model. *p < .05. **p < .01.

factor loadings across groups, further confirmed this conclusion. A nested chi-square difference test was used to compare the baseline (unconstrained) model to the model with equality constraints. Results were highly significant, Δχ2(27) = 74.47, p < .001; thus, the hypothesis of measurement invariance was determined to be untenable. An attempt was then made to determine if partial measurement invariance was achievable in the present sample, using the method proposed by Byrne, Shavelson, and Muthen (1989). A series of CFAs was conducted in which factor loadings were systematically freed and the resulting chi-square values tested against the unconstrained model to determine if the offending parameters could be identified and isolated. Unfortunately, all chi-square difference tests were significant, suggesting that the hypothesis of partial measurement invariance was also not supported.

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In sum, neither full nor partial measurement invariance hypotheses were supported by the data, thereby precluding formal statistical testing of age-related differences in latent associations between EF and psychosocial functioning.


Qualitative Descriptions of EF-Psychosocial Adjustment Models by Age Group. The following qualitative accounts of latent factor structure and interconstruct associations were derived from CFA models conducted for each age group separately and are provided for descriptive purposes. Early Childhood. Significant positive associations were observed between Working Memory and both Shifting (p < .05) and Verbal Fluency (p < .001) latent EF constructs; Shifting and Verbal Fluency were not significantly correlated. Latent Externalizing and Internalizing factors were also significantly positively correlated (p < .001). No statistically significant associations were detected between EF and psychosocial functioning constructs. Middle Childhood. Significant positive associations were observed between Working Memory and both Shifting (p < .05) and Verbal Fluency (p < .001) latent EF constructs; however, Shifting and Verbal Fluency were not significantly interrelated. Latent Externalizing and Internalizing factors were also significantly positively correlated (p < .001). Working Memory was significantly negatively associated with Externalizing problems (p < .05). No other statistically significant associations were detected. Early Adolescence. No statistically significant correlations were observed between latent EF constructs. Latent Externalizing and Internalizing factors were significantly positively correlated (p < .001). No statistically significant associations were found between latent EF and psychosocial functioning constructs. Late Adolescence. Significant positive associations were observed between all three latent EF factors: Working Memory was correlated significantly with Shifting (p < .05) and Verbal Fluency (p < .001); Shifting and Verbal Fluency were also significantly interrelated (p < .05). Latent Externalizing and Internalizing factors were significantly positively correlated (p < .001). No statistically significant associations were found between latent EF and psychosocial factors. DISCUSSION EF Factor Structure and Organization Contrary to a unitary/domain-general understanding of EF, the current study identified Working Memory, Shifting, and Verbal Fluency as separable yet related latent EF constructs. A one-factor model provided a very poor fit to the data, as did a model wherein latent associations between EF factors were fixed at zero to represent independence (i.e., lack of significant correlation) between factors. These findings confirm and extend prior investigations of EF structure and organization across the lifespan. Working Memory and Shifting have been identified consistently as robust EF factors among school-age children, adolescents, and adults (Huizinga et al., 2006; Lehto et al., 2003;


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Miyake et al., 2000; van der Sluis et al., 2007). In addition, to my knowledge, this study was the first of its kind to include Verbal Fluency as a distinct latent construct.


EF and Psychosocial Adjustment Hypotheses regarding expected latent associations between EF and psychosocial adjustment factors were partially confirmed. The proposed five-factor model (Working Memory, Shifting, Verbal Fluency, Internalizing, and Externalizing) provided a close fit to the data. All three EF factors were significantly intercorrelated, as were Internalizing and Externalizing factors. Overall, results indicate that individual differences in certain aspects of EF track meaningfully and in expected directions with parent ratings of concurrent psychosocial adjustment. Externalizing behaviors, in particular, were associated with latent Working Memory and Verbal Fluency factors, although Working Memory did account for marginally significant portions of variance in Internalizing problems, as well. Working memory has long been recognized for its role in reasoning and futureoriented problem solving (e.g., Diamond, 2013; Goldman-Rakic, 1987). More recently, the discovery that individual differences in working memory predict aspects of psychosocial adjustment has prompted researchers to consider even more complex theories, embedding working memory within a larger framework of emotion regulation and social-information processing. Séguin, Nagin, Assaad, and Tremblay (2004, Séguin & Zelazo, 2005), in their studies of executive function and aggressive behavior, suggested that a child who is better able to mentally represent and simultaneously consider multiple response options, as well as their respective consequences, may engage in more adaptive means of social problem solving when confronted with hostile or threatening circumstances. Working memory may also help facilitate a more accurate appraisal of the full array of positive and negative outcomes potentially accompanying more premeditated/ instrumental aspects of externalizing (e.g., theft, truancy), which may serve to downregulate antisocial tendencies. With respect to Working Memory and Internalizing, difficulties with attention and concentration frequently coexist with pediatric emotional problems (Emerson et al., 2005; Forbes et al., 2007; Halari et al., 2009; Kyte et al., 2005). Insofar as basic attention abilities are required to complete all tasks of working memory (e.g., Fernandez-Duque & Johnson, 2002), children experiencing even relatively subtle difficulties with attention might also be expected to perform more poorly on working memory tests than their peers. These working memory impairments may, in turn, feed into further internalizing problems if, for example, a child who is less able to consider multiple interpretations of a situation is hindered in his or her ability to divert initial negative thoughts toward alternative neutral or positive appraisals. In such a scenario, diminished working memory abilities may promote rumination and may lessen one’s capacity to cognitively reframe experiences that otherwise seem to conform to negative interpretation biases. Links between verbal abilities and behavioral problems have also been recognized for many years (Moffitt, 1993); yet, with a few notable exceptions (Nigg et al., 1999; Stanford et al., 1997), most previous studies have focused on crystallized verbal abilities while overlooking more executive aspects of language functioning such as verbal fluency. Thus, the present investigation contributes to the existing literature by documenting that children and adolescents with better developed verbal fluency skills are less likely to engage in parent-reported externalizing behaviors. The nature of this association remains open to interpretation. It is plausible that individuals who can more easily assert

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themselves verbally are better equipped to manage troublesome interpersonal situations with words rather than physical prowess (e.g., Moffitt, 1993). Conversely, the inability to express one’s thoughts effectively, whether real or perceived, may fuel feelings of frustration and increase emotional arousal, which may, in turn, increase risk for physical outbursts. Of course, as Nigg et al. (1999) point out, the explanatory power of such an argument is limited by its potential applicability to both internalizing and externalizing problems. Further investigation is, therefore, needed to clarify the nature of verbal fluency as a protective factor and to elucidate differential associations with emotional and behavioral functioning. Developmental Differences The final aim of this study was to explore developmental differences in associations between EF and emotional/behavioral adjustment. Participants were separated into four age-based subgroups to allow comparisons across salient developmental periods. A series of increasingly restrictive tests converged on the conclusion that latent factor structure was not invariant across groups; therefore, statistical comparison would not have been appropriate due to the presence of important disparities in the way latent factors were represented across groups (Horn & McArdle, 1992). The cause of the observed lack of measurement invariance is unclear. One possible explanation may be that the measures and/or parent-report ratings administered tapped into different underlying cognitive/psychosocial constructs depending on the age of the child. For example, it may be the case that not only are younger children able to maintain less information in working memory than adolescents but also that doing so requires the differential recruitment of complementary cognitive resources (e.g., inhibitory control; Diamond, 2013) that are perhaps less integral and/or more easily recruited as they get older. Unfortunately, the current study was not designed to test this hypothesis. Limitations This study provides a latent variable investigation of associations between EF and psychosocial functioning in the most-representative sample of healthy US children and adolescents to date. Nonetheless, there are some limitations that should be acknowledged. First, the lack of performance-based measures of inhibitory control precluded examination of a latent Inhibitory Control factor. Despite mixed evidence for the existence of a distinct inhibition latent factor in some investigations of children and adolescents (Huizinga et al., 2006; van der Sluis et al., 2007), other child/adolescent (e.g., Lee et al., 2013; Lehto et al., 2003) and most adult studies (e.g., Miyake et al., 2000) display evidence of inhibitory control as one of three fundamental executive functions, along with working memory and shifting. Therefore, the omission of behavioral measures of inhibitory control from the NIH MRI Study of Normal Brain Development protocol represents a significant limitation that constrains the generalizability of the findings obtained. Additional studies are therefore necessary to examine latent associations between inhibitory control and psychosocial outcomes, as well as to clarify the structure and organization of verbal fluency in relation to the full complement of core EF components. Second, due to their lack of substantive value to the hypothesized model, two theoretically meaningful manifest indicators were omitted from primary analyses. Unfortunately, this left only a single indicator with which to identify the Shifting factor. Third, it should be acknowledged that, while an interrelated



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three-factor model of EF fit the current sample of 7- to 18-year-olds well, preschool-age EF abilities tend to be less differentiated and thus may be better characterized by onefactor solution (e.g., Garon et al., 2008; Hughes & Ensor, 2011; Hughes et al., 2010; Wiebe et al., 2008, 2011). Finally, psychosocial functioning was gauged exclusively via parent-report questionnaires and examined as composite indices of broadly defined externalizing and internalizing problems. Future research should include additional measures of emotional and behavioral problems from other sources including participants, teachers, peer-reports, and naturalistic observations (for younger children). Moreover, the use of aggregate measures of emotional/behavioral problems risks masking important differences within a given category. The current study collapsed across aggressive and nonaggressive forms of externalizing behavior problems, for example, thus overlooking potentially meaningful differences in associations between EF and these distinct yet often related behavior problems (e.g., Barker et al., 2007; Broidy et al., 2003). This investigation also focused on children and adolescents whose emotional and behavioral problems fell below clinical cut-off points on the CBCL. Additional research is needed to validate the obtained findings among samples of children and adolescents experiencing clinically elevated psychosocial maladjustment. Implications and Future Directions Evaluation of EF is routine in pediatric neuropsychological assessment (Baron, 2004). Still, clinicians differ widely in their method of determining the integrity of various executive skills. Results from the present study confirm previous investigations suggesting that EF cannot and should not be considered a unitary construct, assessable via performance on any single test. Rather, executive functions exist as a constellation of interconnected yet distinguishable cognitive self-regulatory mechanisms, each potentially meaningful for understanding how a child navigates his or her environment. Clinicians should be explicit in interpreting the aspects of EF they assess, making certain to avoid overgeneralizing performance on one EF measure as evidence of the integrity or impairment of EF more broadly. Clinicians should also, within the context of the specific referral question or questions, administer a battery of EF tasks that samples several executive skills including working memory, shifting, inhibitory control, and verbal fluency. Moreover, the results obtained from these measures must be interpreted within the context of the child’s larger profile of neurobehavioral strengths and weaknesses, as well as his or her developmental history, contextual factors, motivation toward testing, and psychometric limitations of the instruments utilized. With respect to the observed associations between EF and psychosocial functioning, the concurrent nature of the present study precludes determination of the direction of these relations. That is, it is unclear whether EF difficulties increase risk for emotional/behavioral problems or if emotional/behavioral problems increase risk for EF difficulties. Perhaps the most likely scenario is one in which these factors exert complex, transactional influences that are not as easily captured in simple, cause-effect studies. These important considerations notwithstanding, the fact that aspects of EF were related to psychosocial adjustment, even at subclinical levels, suggests that prevention and intervention efforts may be improved by attempts to bolster cognitive self-regulatory abilities (Paschall & Fishbein, 2002; Riggs et al., 2006). This study also highlights several directions for future research. The first pertains to the use of latent variable modelling in elucidating brain-behavior relations. One of the


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most significant drawbacks of traditional exploratory factor analysis is the likelihood that factors extracted from theoretically dissimilar variables will ultimately prove limited in their capacity to identify mediating neural systems (e.g., Zelazo et al., 1997, 2003). Confirmatory factor analytic methods, which combine variance shared across hypothetically related manifest indicators while minimizing measurement error, may offer a useful alternative (e.g., Miyake et al., 2000). It is unlikely that the latent factors identified would map cleanly onto independent/dissociable neural systems. Still, latent variable analysis may prove informative in helping to delineate the structure and organization of EF from a multilevel perspective. Second, as discussed, the concurrent nature of this investigation limits analyses to correlation-based methods that cannot be used to infer causality. However, as a longitudinal project, the NIH MRI Study of Normal Brain Development holds promise for shedding some much-needed light on these important research questions. Rates of emotional and behavioral problems follow normative developmental trajectories and may change over the course of the study. It may be of interest to examine these changes within the context of previous development to determine potential neurobehavioral risk factors for onset of psychopathology. Finally, it is important to once again consider that the current study focused on healthy participants who were rated by their parents as below the designated clinical threshold on a well-validated measure of psychosocial adjustment. Even among members of this decidedly nonclinical population, relative weaknesses in EF skills were more common among children and adolescents experiencing higher rates of emotional/behavioral difficulties. Future research should attempt to replicate this study among individuals with more severe psychosocial maladjustment issues. Latent variable modelling offers a worthwhile alternative to traditional methods of analyzing executive function data and would be particularly useful in determining whether EF factor structure, organization, and associations with relevant clinical factors operate similarly across the full range of typical and atypical development.

Original manuscript received February 19, 2014 Revised manuscript accepted October 28, 2014 First published online January 9, 2015

REFERENCES Achenbach, T. M. (2001). Manual for the ASEBA school-age forms and profiles (child behavior checklist for ages 6 – 18). Burlington: University of Vermont. Alloway, T. P., Gathercole, S. E., & Pickering, S. J. (2006). Verbal and visuospatial short-term and working memory in children: Are they separable? Child Development, 77(6), 1698–1716. doi:10.1111/j.1467-8624.2006.00968.x Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48, 5–37. doi:10.1016/j.jsp.2009.10.001 Barker, E. D., Séguin, J. R., White, H. R., Bates, M. E., Lacourse, E., Carbonneau, R., & Trembley, R. E. (2007). Developmental trajectories of male physical violence and theft: Relations to neurocognitive performance. Archives of General Psychiatry, 64, 592–599. doi:10.1001/ archpsyc.64.5.592



21

Baron, I. S. (2004). Neuropsychological evaluation of the child. New York, NY: Oxford University Press. Baune, B. T., Fuhr, M., Air, T., & Hering, C. (2014). Neuropsychological functioning in adolescents and young adults with major depressive disorder–a review. Psychiatry Research, 218(3), 261– 271. doi:10.1016/j.psychres.2014.04.052 Beauchamp, M. H., & Anderson, V. (2010). SOCIAL: An integrative framework for the development of social skills. Psychological Bulletin, 136(1), 39–64. doi:10.1037/a0017768 Brocki, K. C., & Bohlin, G. (2004). Executive functions in children aged 6 to 13: A dimensional and developmental study. Developmental Neuropsychology, 26, 571–593. doi:10.1207/ s15326942dn2602_3 Broidy, L. M., Nagin, D. S., Trembley, R. E., Bates, J. E., Brame, B., Dodge, K. A., … Vitaro, F. et al. (2003). Developmental trajectories of childhood disruptive behaviors and adolescent delinquency: A six-site, cross-national study. Developmental Psychology, 39, 222–245. doi:10.1037/0012-1649.39.2.222 Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: The Guilford Press. Bull, R., & Lee, K. (2014). Executive functioning and mathematics achievement. Child Development Perspectives, 8(1), 36–41. doi:10.1111/cdep.12059 Byrne, B. M., Shavelson, R. J., & Muthen, B. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456–466. doi:10.1037/0033-2909.105.3.456 Calkins, S. D., & Marcovitch, S. (2010). Emotion regulation and executive functioning in early development: Integrated mechanisms of control supporting adaptive functioning. In S. D. Calkins & M. A. Bell (Eds.), Child development at the intersection of emotion and cognition. Washington, DC: American Psychological Association. CeNeS. (1998). Cambridge neuropsychological test automated battery (Version 2.3). Cambridge, UK: CeNeS Cognition. Champod, A. S., & Petrides, M. (2010). Dissociation within the frontoparietal network in verbal working memory: A parametric functional magnetic resonance imaging study. Journal of Neuroscience, 30(10), 3849–3856. doi:10.1523/JNEUROSCI.0097-10.2010 Ciairano, S., Visu-Petra, L., & Settanni, M. (2007). Executive inhibitory control and cooperative behavior during early school years: A follow-up study. Journal of Abnormal Child Psychology, 35, 335–345. doi:10.1007/s10802-006-9094-z Clark, C., Prior, M., & Kinsella, G. (2002). The relationship between executive function abilities, adaptive behaviour, and academic achievement in children with externalising behaviour problems. Journal of Child Psychology and Psychiatry, 43, 785–796. doi:10.1111/14697610.00084 Conklin, H. M., Luciana, M., Hooper, C. J., & Yarger, R. S. (2007). Working memory performance in typically developing children and adolescents: Behavioral evidence of protracted frontal lobe development. Developmental Neuropsychology, 31, 103–128. doi:10.1207/ s15326942dn3101_6 Denckla, M. B. (1994). Measurement of executive function. In G. R. Lyon (Ed.), Frames of reference for the assessment of learning disabilities: New views on measurement issues (pp. 263–277). Baltimore, MD: Brookes Publishing Company. Diamond, A. (2002). Normal development of prefrontal cortex from birth to young adulthood: Cognitive functions, anatomy, and biochemistry. In D. T. Stuss & R. T. Knight (Eds.), Principles of frontal lobe function (pp. 466–503). London, UK: Oxford University Press. Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135–168. doi:10.1146/ annurev-psych-113011-143750 Duncan, J., Johnson, R., Swales, M., & Freer, C. (1997). Frontal lobe deficits after head injury: Unity and diversity of function. Cognitive Neuropsychology, 14, 713–741. doi:10.1080/ 026432997381420


22

A. R. CASSIDY

Emerson, C. S., Mollet, G. A., & Harrison, D. W. (2005). Anxious-depression in boys: An evaluation of executive functioning. Archives of Clinical Neuropsychology, 20, 539–546. doi:10.1016/j.acn.2004.10.003 Evans, A. C. (2006). The NIH MRI study of normal brain development. NeuroImage, 30, 184–202. doi:10.1016/j.neuroimage.2005.09.068 Fernandez-Duque, D., & Johnson, M. L. (2002). Cause and effect theories of attention: The role of conceptual metaphors. Review of General Psychology, 6, 153–165. doi:10.1037/10892680.6.2.153 Forbes, E. E., Shaw, D. S., & Dahl, R. E. (2007). Alterations in reward-related decision making in boys with recent and future depression. Biological Psychiatry, 61, 633–639. doi:10.1016/j. biopsych.2006.05.026 Garon, N., Bryson, S. E., & Smith, I. M. (2008). Executive function in preschoolers: A review using an integrative framework. Psychological Bulletin, 134, 31–60. doi:10.1037/00332909.134.1.31 Giancola, P. R., Martin, C. S., Tarter, R. E., Pelham, W. E., & Moss, H. B. (1996). Executive cognitive functioning and aggressive behavior in preadolescent boys at high risk for substance abuse/dependence. Journal of Studies on Alcohol, 57, 352–359. Giancola, P. R., Mezzich, A. C., & Tarter, R. E. (1998a). Disruptive, delinquent and aggressive behavior in female adolescents with a psychoactive substance use disorder: Relation to executive cognitive functioning. Journal of Studies on Alcohol, 59, 560–567. Giancola, P. R., Mezzich, A. C., & Tarter, R. E. (1998b). Executive cognitive functioning, temperament, and antisocial behavior in conduct-disordered adolescent females. Journal of Abnormal Psychology, 107, 629–641. doi:10.1037/0021-843X.107.4.629 Giancola, P. R., Moss, H. B., Martin, C. S., Kirisci, L., & Tarter, R. E. (1996). Executive cognitive functioning predicts reactive aggression in boys at high risk for substance abuse: A prospective study. Alcoholism: Clinical and Experimental Research, 20, 740–744. doi:10.1111/j.15300277.1996.tb01680.x Gioia, G., Isquith, P., Guy, S., & Kenworthy, L. (2000). The behavior rating inventory of executive function. Lutz, FL: Psychological Assessment Resources. Goldman-Rakic, P. S. (1987). Development of cortical circuitry and cognitive function. Child Development, 58, 601–622. doi:10.2307/1130201 Graham, J. W., Cumsille, P. E., & Elek-Fisk, E. (2003). Methods for handling missing data. In J. A. Schinka & W. F. Velicer (Eds.), Handbook of psychology: Research methods in psychology, Vol. 2 (pp. 87–114). New York, NY: Wiley. Halari, R., Simic, M., Pariante, C. M., Papadopoulos, A., Cleare, A., Brammer, M., … Rubia, K. (2009). Reduced activation in lateral prefrontal cortex and anterior cingulate during attention and cognitive control functions in medication-naïve adolescents with depression compared to controls. Journal of Child Psychology and Psychiatry, 50, 307–316. doi:10.1111/j.1469-7610.2008.01972.x Hoaken, P. N., Shaughnessy, V. K., & Pihl, R. O. (2003). Executive cognitive functioning and aggression: Is it an issue of impulsivity? Aggressive Behavior, 29, 15–30. Horn, J. L., & McArdle, J. J. (1992). A practical and theoretical guide to measurement invariance in aging research. Experimental Aging Research, 18, 117–144. doi:10.1080/03610739208253916 Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. doi:10.1080/10705519909540118 Hughes, C., & Ensor, R. (2011). Individual differences in growth in executive function across the transition to school predict externalizing and internalizing behaviors and self-perceived academic success at 6 years of age. Journal of Experimental Child Psychology, 108, 663–676. doi:10.1016/j.jecp.2010.06.005 Hughes, C., Ensor, R., Wilson, A., & Graham, A. (2010). Tracking executive function across the transition to school: A latent variable approach. Developmental Neuropsychology, 35(1), 20– 36. doi:10.1080/87565640903325691



23

Huizinga, M., Dolan, C. V., & Van Der Molen, M. W. (2006). Age-related change in executive function: Developmental trends and a latent variable analysis. Neuropsychologia, 44, 2017– 2036. doi:10.1016/j.neuropsychologia.2006.01.010 Joreskog, K., & Sorbom, D. (2007). LISREL 8.80. Chicago, IL: Scientific Software International, Inc. Korkman, M., Kirk, U., & Kemp, S. (1998). NEPSY: A developmental neuropsychological assessment. San Antonio, TX: Psychological Corporation. Kusché, C. A., Cook, E. T., & Greenberg, M. T. (1993). Neuropsychological and cognitive functioning in children with anxiety, externalizing, and comorbid psychopathology. Journal of Clinical Child Psychology, 22, 172–195. doi:10.1207/s15374424jccp2202_5 Kyte, Z. A., Goodyer, I. M., & Sahakian, B. J. (2005). Selected executive skills in adolescents with recent first episode major depression. Journal of Child Psychology and Psychiatry, 46, 995– 1005. doi:10.1111/j.1469-7610.2004.00400.x Lee, K., Bull, R., & Ho, R. M. H. (2013). Developmental changes in executive functioning. Child Development, 84(6), 1933–1953. doi:10.1111/cdev.12096 Lehto, J. E., Juujarvi, P., Kooistra, L., & Pulkkinen, L. (2003). Dimensions of executive functioning: Evidence from children. British Journal of Developmental Psychology, 21, 59–80. doi:10.1348/ 026151003321164627 Levin, H. S., Culhane, K. A., Hartman, J., Evankovich, K., Mattson, A., Harward, H., … Fletcher, J. M. (1991). Developmental changes in performance on tests of purported frontal lobe functioning. Developmental Neuropsychology, 7, 377–395. Little, D. M., Kraus, M. F., Joseph, J., Geary, E. K., Susmaras, T., Zhou, X. J., & Gorelick, P. B. (2010). Thalamic integrity underlies executive dysfunction in traumatic brain injury. Neurology, 74(7), 558–564. Loehlin, J. C. (2004). Latent variable models: An introduction to factor, path, and structural equation analysis (4th ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Martel, M. M., Nigg, J. T., Wong, M. M., Fitzgerald, H. E., Jester, J. M., Puttler, L. I., … Zucker, R. A. (2007). Childhood and adolescent resiliency, regulation, and executive functioning in relation to adolescent problems and competence in a high-risk sample. Development and Psychopathology, 19, 541–563. McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64–82. Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100. Moffitt, T. E. (1993). The neuropsychology of conduct disorder. Development and Psychopathology, 5, 135–151. Morrison, F. J., Ponitz, C. C., & McClelland, M. M. (2010). Self-regulation and academic achievement in the transition to school. In S. D. Calkins & M. A. Bell (Eds.), Child development at the intersection of emotion and cognition. Washington, DC: American Psychological Association. Nigg, J. T., Quamma, J. P., Greenberg, M. T., & Kusché, C. A. (1999). A two-year longitudinal study of neuropsychological and cognitive performance in relation to behavioral problems and competencies in elementary school children. Journal of Abnormal Child Psychology, 27, 51–63. Paschall, M. J., & Fishbein, D. H. (2002). Executive cognitive functioning and aggression: A public health perspective. Aggression and Violent Behavior, 7, 215–235. Pennington, B. F., & Ozonoff, S. (1996). Executive functions and developmental psychopathology. Journal of Child Psychology and Psychiatry, 37, 51–87. Provost, J. S., Petrides, M., & Monchi, O. (2010). Dissociating the role of the caudate nucleus and dorsolateral prefrontal cortex in the monitoring of events within human working memory. European Journal of Neuroscience, 32(5), 873–880. Raaijmakers, M. A. J., Smidts, D. P., Sergeant, J. A., Maassen, G. H., Posthumus, J. A., Van Engeland, H., & Matthys, W. (2008). Executive functions in preschool children with aggressive


24

A. R. CASSIDY

behavior: Impairments in inhibitory control. Journal of Abnormal Child Psychology, 36(7), 1097–1107. Razza, R. A., & Blair, C. (2009). Associations among false-belief understanding, executive function, and social competence: A longitudinal analysis. Journal of Applied Developmental Psychology, 30, 332–343. Rhoades, B. L., Greenberg, M. T., & Domitrovich, C. E. (2009). The contribution of inhibitory control to preschoolers’ social-emotional competence. Journal of Applied Developmental Psychology, 30, 310–320. Riggs, N. R., Blair, C. B., & Greenberg, M. T. (2003). Concurrent and 2-year longitudinal relations between executive function and the behavior of 1st and 2nd grade children. Child Neuropsychology, 9, 267–276. Riggs, N. R., Jahromi, L. B., Razza, R. P., Dillworth-Bart, J. E., & Mueller, U. (2006). Executive function and the promotion of social-emotional competence. Journal of Applied Developmental Psychology, 27, 300–309. Robbins, T. W. (1996). Dissociating executive functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London, 351, 1463–1471. Romer, D., Betancourt, L., Brodsky, N. L., Gianetta, J. M., Yang, W., & Hurt, H. (2011). Does adolescents risk taking imply weak executive function? A prospective study of relations between working memory performance, impulsivity, and risk taking in early adolescence. Developmental Science, 14(5), 1119–1133. Romer, D., Betancourt, L., Giannetta, J. M., Brodsky, N. L., Farrah, M., & Hurt, H. (2009). Executive cognitive functions and impulsivity as correlates of risk taking and problem behavior in preadolescents. Neuropsychologia, 47, 2916–2926. Séguin, J. R. (2004). Neurocognitive elements of antisocial behavior: Relevance of an orbitofrontal cortex account. Brain and Cognition, 55, 185–197. Séguin, J. R., Boulerice, B., Harden, P. W., Tremblay, R. E., & Pihl, R. O. (1999). Executive functions and physical aggression after controlling for attention deficit hyperactivity disorder, general memory, and IQ. Journal of Child Psychology and Psychiatry, 40, 1197–1208. Séguin, J. R., Nagin, D., Assaad, J.-M., & Tremblay, R. E. (2004). Cognitive-neuropsychological function in chronic physical aggression and hyperactivity. Journal of Abnormal Psychology, 113, 603–613. Séguin, J. R., & Zelazo, P. D. (2005). Executive function in early physical aggression. In R. E. Trembley, W. W. Hartup, & J. Archer (Eds.), Developmental origins of aggression. New York, NY: Guilford Press. Stanford, M. S., Greve, K. W., & Gerstle, J. E. (1997). Neuropsychological correlates of selfreported impulsive aggression in a college sample. Personality and Individual Differences, 23, 961–965. Stoodley, C. J., & Schmahmann, J. D. (2010). Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex, 46(7), 831–844. Stuss, D. T. (1992). Biological and psychological development of executive functions. Brain and Cognition, 20, 8–23. Thompson, R. A. (1994). Emotion regulation: A theme in search of definition. In N. A. Fox (Ed.), The development of emotion regulation: Biological and behavioral considerations. Monographs of the Society for Research in Child Development 59(2-3, Serial No. 240), (pp. 25–52). Chicago, IL: University of Chicago Press. Thompson, R. A., & Meyer, A. (2007). Socialization of emotion regulation in the family. In J. J. Gross (Ed.), Handbook of emotion regulation. New York, NY: Guilford Press. Van der Sluis, S., De Jong, P. F., & Van Der Leij, A. (2007). Executive functioning in children, and its relations with reasoning, reading, and arithmetic. Intelligence, 35, 427–449. Van der Ven, S. H., Kroesbergen, E. H., Boom, J., & Leseman, P. P. (2012). The development of executive functions and early mathematics: A dynamic relationship. British Journal of Educational Psychology, 82(Pt 1), 100–119.



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Waber, D. P., De Moor, C., Forbes, P. W., Almli, C. R., Botteron, K. N., Leonard, G., … The Brain Development Cooperative Group. (2007). The NIH MRI study of normal brain development: Performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery. Journal of the International Neuropsychological Society, 13, 729–746. Wechsler, D. (1991). Wechsler intelligence scale for children (3rd ed.). New York, NY: Psychological Corporation. Wechsler, D. (1997). Wechsler adult intelligence scale (3rd ed.). New York, NY: Psychological Corporation. Wechsler, D. (1999). Wechsler abbreviated scale of intelligence. New York, NY: Psychological Corporation. Welsh, M. C., Pennington, B. F., & Groisser, D. B. (1991). A normative-developmental study of executive function: A window on prefrontal function in children. Developmental Neuropsychology, 7, 131–149. Wiebe, S. A., Espy, K., & Charak, D. (2008). Using confirmatory factor analysis to understand executive control in preschool children I. Latent structure. Developmental Psychology, 44, 575– 587. Wiebe, S. A., Sheffield, T., Nelson, J. M., Clark, C. A., Chevalier, N., & Espy, K. A. (2011). The structure of executive function in 3-year-olds. Journal of Experimental Child Psychology, 108, 436–452. Yeates, K. O., Bigler, E. D., Dennis, M., Gerhardt, C. A., Rubin, K. H., Stancin, T., … Vannatta, K. (2007). Social outcomes in childhood brain disorder: A heuristic integration of social neuroscience and developmental psychology. Psychological Bulletin, 133, 535–556. Zelazo, P. D., Carter, A., Reznick, J. S., & Frye, D. (1997). Early development of executive function: A problem-solving framework. Review of General Psychology, 1, 198–226. Zelazo, P. D., & Cunningham, W. A. (2007). Executive function: Mechanisms underlying emotion regulation. In J. J. Gross (Ed.), Handbook of emotion regulation. New York, NY: The Guilford Press. Zelazo, P. D., Müller, U., Frye, D., & Marcovitch, S. (2003). The development of executive function in early childhood. Monographs of the Society for Research in Child Development, 68, 3.

Appendix: Description of goodness-of-fit indices and model comparison statistic Goodness-of-Fit Indices Chi-Square (χ2). The chi-square statistic provides an index of model fit based on the null hypothesis that the parameter estimates generated according to a given model’s specifications are identical to the available data. Thus, failure to reject the null hypothesis, as determined by a nonsignificant chi-square statistic relative to the chi-square distribution, is indicative of good model fit. Conversely, a significant chi-square statistic suggests that the tested model provides a poor fit because the implied parameter estimates differ significantly from the data. Despite being among the most frequently reported statistics in latent variable modelling, it is now widely recognized that the chi-square fit statistic is unduly sensitive to sample size (Brown, 2006). Root Mean Square Error of Approximation (RMSEA). The RMSEA is an absolute fit index that yields an estimate of model misfit per degree of freedom, without reference to a hypothetical null model. Thus, as an estimate of misfit, smaller values reflect better model fit. In general, RMSEA values are interpreted according to the following guidelines: 0.0 = exact/perfect fit; 0.01– 0.05 = close fit; 0.05–0.08 = acceptable fit; 0.08–0.10 = mediocre fit; and, > 0.10 = poor fit (Brown, 2006; Hu & Bentler, 1999).

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A. R. CASSIDY

Comparative Fit Index (CFI). The CFI is a relative fit index in which a given model is compared to a null model with manifest interrelations fixed to zero. CFI values are generally interpreted according to the following guidelines: 1.0 = exact/perfect fit; 0.95–0.99 = close fit; 0.90– 0.95 = acceptable fit; 0.85–0.90 = mediocre fit; and, < 0.85 = poor fit (Brown, 2006). Non-Normed Fit Index (NNFI). Like the CFI, the NNFI (also known as the Tucker-Lewis Index [TLI]) is a relative fit index in which a tested model is compared to a null model wherein relations among manifest indicators are fixed to zero. NNFI values are generally interpreted in the same manner as CFI values (Brown, 2006). Model Comparison


Akaike Information Criterion (AIC). The AIC is used to compare non-nested models (i.e., models with different numbers of latent variables).

Adjustment disorder in children and adolescents.

Psychosocial adjustment following acquired brain injury in childhood and adolescence: executive, behavioural and emotional contributions.

Recurrent Dreams and Psychosocial Adjustment in Preteenaged Children.

Latent mean differences in executive function in at-risk preterm children: the delay-deficit dilemma.

Executive Function in Children and Adolescents with Critical Cyanotic Congenital Heart Disease.

Education and Successful Aging Trajectories: A Longitudinal Population-Based Latent Variable Modelling Analysis.

Psychosocial adjustment of children with a terminally ill parent.

Psychosocial measures and cardiovascular risk factors in children and adolescents.

Executive Functioning in Children and Adolescents With Perinatal HIV Infection.

The relationship of dental caries and dental fear in Malaysian adolescents: a latent variable approach.

Executive function as a mediator in the link between single or complex trauma and posttraumatic stress in children and adolescents.

AIDS in Ghana.

Relationships between Dietary Intake and Cognitive Function in Healthy Korean Children and Adolescents.

Psychosocial adjustment and quality of life of adolescents and adults with congenital heart disease.

Executive Function in Previously Institutionalized Children.

Obesity-associated biomarkers and executive function in children.

Executive function and magnitude skills in preschool children.

Psychosocial adaptation of children and adolescents with chronic renal failure.

Longitudinal analysis of lung function growth in healthy children and adolescents.

Psychosocial adjustment of children with migraine and tension-type headache - a nationwide study.

Rejection Reactivity, Executive Function Skills, and Social Adjustment Problems of Inattentive and Hyperactive Kindergarteners.

[Serum fructosamine in healthy and diabetic children and adolescents].

Impulsivity, risk taking, and cortisol reactivity as a function of psychosocial stress and personality in adolescents.

Association between sleep bruxism and psychosocial factors in children and adolescents: a systematic review.