Developmental Psychology 2014, Vol. 50, No. 6, 1698 –1709

© 2014 American Psychological Association 0012-1649/14/$12.00 http://dx.doi.org/10.1037/a0036633

Longitudinal Associations Between Executive Functioning and Academic Skills Across Content Areas Mary Wagner Fuhs

Kimberly Turner Nesbitt and Dale Clark Farran

University of Dayton

Vanderbilt University

Nianbo Dong This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

University of Missouri This study assessed 562 four-year-old children at the beginning and end of their prekindergarten (pre-k) year and followed them to the end of kindergarten. At each time point children were assessed on 6 measures of executive function (EF) and 5 subtests of the Woodcock–Johnson III academic achievement battery. Exploratory factor analyses yielded EF and achievement factor scores. We examined the longitudinal bidirectional associations between these domains as well as the bidirectional associations among the separate content areas and the EF factor. In the pre-k year, strong bidirectional associations were found for EF skills and mathematics and oral comprehension skills but not for literacy skills. After controlling for pre-k gains in both EF and achievement, EF skills continued to be strong predictors of gains in mathematics in kindergarten and a more moderate predictor of kindergarten language gains. These results provide important information on the interrelationship of the developmental domains of EF and achievement as well as support for efforts to determine effective pre-k activities and/or curricula that can improve children’s EF skills. They also suggest that mathematics activities may be a possible avenue for improving EF skills in young children. Keywords: executive functioning, academic skills, early childhood development

period when many children in the United States also begin participating in more formal learning environments and have been associated with greater academic achievement (e.g., Blair & Razza, 2007; Duncan et al., 2007; McClelland et al., 2007; Welsh, Nix, Blair, Bierman, & Nelson, 2010). The implication, however, that these skills should be globally targeted by teachers in both prekindergarten (pre-k) and kindergarten classrooms (Blair & Diamond, 2008; Blair & Razza, 2007; McClelland et al., 2007) is limited by several unanswered questions about the strength and direction of effects through children’s transition from pre-k to kindergarten as well as the specificity of effects by academic content area. In the current study, we assessed children’s EF and academic skills development across three time points spanning from the fall of pre-k to the spring of kindergarten to address three novel questions:

Successful creation of early childhood activities and programs to enhance young children’s school readiness requires an in-depth understanding of children’s developing cognitive skills. Recent accounts suggest that executive functioning (EF) skills, an umbrella term for higher order cognitive processes such as working memory, inhibitory control, and attention flexibility (e.g., Anderson, 2002; Carlson, 2005; Garon, Bryson, & Smith, 2008), may be particularly important for early academic skills development because they aid children in the adaptation to a classroom environment. Indeed, EF skills show significant development during the

This article was published Online First April 21, 2014. Mary Wagner Fuhs, Department of Psychology, University of Dayton; Kimberly Turner Nesbitt, Peabody Research Institute, Vanderbilt University; Dale Clark Farran, Department of Teaching and Learning and Peabody Research Institute, Vanderbilt University; Nianbo Dong, Department of Educational, School, and Counseling Psychology, University of Missouri. The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A080079, awarded to Mark W. Lipsey and Dale Clark Farran. Mary Wagner Fuhs and Kimberly Turner Nesbitt were supported by an Institute of Education Postdoctoral Fellowship (R305B100016) awarded to Dale Clark Farran and Mark W. Lipsey. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education. Special thanks to Deanna Meador and the Peabody Research Institute research team for their very capable management of the data collection for this project. Correspondence concerning this article should be addressed to Mary Wagner Fuhs, Department of Psychology, University of Dayton, 300 College Park, Dayton, OH 45469. E-mail: [email protected] 1698

1.

Do EF skills predict academic skills across the transition from pre-k to kindergarten after controlling for the gains children made in both EF and academic achievement across the pre-k year?

2.

Is the inverse path also significant such that children’s academic skills at the end of pre-k predict their later EF skills after controlling for the gains children made in pre-k in both domains?

3.

Do the associations between EF and achievement vary by academic content area (i.e., mathematics, language, and literacy)?

EXECUTIVE FUNCTIONING AND ACADEMIC SKILLS

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Executive Functioning as a Predictor of Academic Skill Gains The development of EF skills in childhood appears to be gradual, with marked improvements occurring during early childhood (ages 3–5; Garon et al., 2008). This particularly rapid development is facilitated in part by the maturation of the prefrontal cortex, although children’s EF skills continue to mature through young adulthood (Johnson, 2001). Certainty about the developmental trajectory has been limited by measurement issues when assessing young children. First, any single assessment of EF measures not only EF but also other skills such as motor and verbal capabilities. Second, any single assessment of EF typically involves more than one EF skill. For example, it is difficult to assess inhibitory control in young children while completely eliminating any demands on working memory. Efforts to determine the factor structure of EF in young children have uniformly found a one-factor solution to be best (Bull, Espy, Wiebe, Sheffield, & Nelson, 2011; Bull & Scerif, 2001; Hughes, Ensor, Wilson, & Graham, 2009; Wiebe, Espy, & Charak, 2008; Willoughby, Blair, Wirth, Greenberg, & The Family Life Project Investigators, 2010), suggesting that in early childhood EF is best represented by a single latent factor. However, the fact that EF measures are generally impure in young children would likely make it difficult to tease apart distinct factors even if a multidimensional construct exists. Pre-k and kindergarten are a critical period to address associations between EF and academic skills, not only because EF research suggests a rapid improvement as well as considerable individual variability in young children’s EF skills (Garon et al., 2008), but also because this is a time when many children are learning fundamental academic skills. This coincidence of learning domains becomes even more interesting for the many children who are participating in formal learning environments such as pre-k classrooms for the first time. For example, research with pre-k students suggests that EF is associated with skills across several academic domains including literacy skills (Allan & Lonigan, 2011); language, particularly vocabulary skills (Weiland, Barata, & Yoshikawa, 2014); and mathematics skills (Clark, Pritchard, & Woodward, 2010). Longitudinal research using pre-k EF assessments has also supported an association between EF and these same three academic content areas over time (Duncan et al., 2007; McClelland et al., 2007; Welsh et al., 2010). It is plausible that the explanation for these relationships is that EF skills indirectly facilitate learning in classrooms by promoting relevant and facilitative behaviors (Blair & Diamond, 2008). In other words, children whose EF skills are more developed may be more likely to understand teacher directions and transition easily between activities in the classroom, while also attending to academically related activities to enhance emergent skills. But do these indirect associations hold beyond pre-k and into kindergarten when children who attended pre-k have had a year of classroom experience, including participating in academically related activities? Or does the importance of EF as a general mechanism for learning in a classroom lessen? If so, we might expect an association between EF and academic skills to hold only for those skills that have not become more automatized as crystallized knowledge (Welsh et al., 2010). Unfortunately, we know much less about the association between EF and academic skills through the kindergarten year compared to pre-k and even less about these

1699

associations after taking into account the pre-k gains that children make. Assessing children only in kindergarten, Brock, RimmKaufman, Nathanson, and Grimm (2009) found that while EF skills at pretest were associated with children’s kindergarten gains in mathematics skills, they were not associated with their gains in literacy skills. The indirect effect of EF on achievement in kindergarten was not confirmed; teacher ratings of children’s learningrelated classroom behavior during the year did not mediate the association between EF and mathematics gains, suggesting, in fact, a direct relationship between EF and mathematics skills. Blair and colleagues (Blair, Knipe, & Gamson, 2008; Blair & Razza, 2007) hypothesized that as children transition from pre-k to kindergarten, EF becomes important for facilitating learning the types of skills that require less automatic processing and more involved problem solving. Many literacy and language skills, such as knowing letter names and vocabulary, become more rote and automatic, and therefore may rely less on EF processes. Mathematics, however, continues to build in complexity and demands for problem-solving skills, meaning that EF skills may be more directly salient for learning mathematics. More targeted analyses even suggest that EF is associated with mathematics skills that require attention to discrete quantity and number sets instead of irrelevant problem information (Fuhs, Byrd, & McNeil, 2013). It is possible that there may be a direct association between EF and mathematics skills in kindergarten that does not hold for other academic content areas in this grade level.

Bidirectional Associations Between Executive Functioning and Academic Skill Gains Young children make great advances in both EF and academic skills simultaneously, suggesting there may be overlapping developmental processes. Children who are learning academically related content more quickly may be more likely to participate in demanding academic activities that help develop higher level cognitive skills (Fuhs & Day, 2011; Stipek, Newton, & Chudgar, 2010). Most studies, however, have not directly assessed the bidirectionality of the association between EF and achievement, with some notable exceptions. Welsh et al. (2010) assessed associations between EF and both mathematics and literacy skills in pre-k students, finding that EF skills at the beginning of pre-k predicted gains in both mathematics and literacy. They also found support for a bidirectional association in which children’s mathematics skills at school entry predicted gains in EF skills. They did not find the same pattern of effects to hold for literacy skills. Stipek et al. (2010) tested for a bidirectional association between kindergarten or first-grade teachers’ perceptions of children’s learning-related behaviors (a measure that could be associated with EF) and a reading composite across time. They found that ratings of elementary school children’s learning-related behaviors predicted later reading skills, but they did not find that early reading skills predicted children’s subsequent learning-related skills ratings. In this study, however, the reading composite score included both letter–word recognition and passage comprehension, a combination of literacy and language elements. It is not known whether assessing the association between learning-related behavior and the letter–word recognition subtest alone would have produced a significant effect.

1700

FUHS, NESBITT, FARRAN, AND DONG

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

The question of bidirectional effects in the language/literacy area is further complicated by conflicting findings regarding vocabulary skills across the year in pre-k. Weiland et al. (2014) reported an effect of pretest EF on receptive vocabulary outcomes but no prediction from receptive vocabulary to EF gains. Fuhs and Day (2011), on the other hand, reported an effect of pretest global language skills on EF gains, but no prediction of EF for gains on global language. These two studies used different sampling, different measures, and different language constructs, and thus, further research is needed to disentangle these findings. Neither study addressed the associations through kindergarten.

Addressing Causality in Executive Functioning and Achievement Gains An important theoretical concern about the nature of the associations between EF skills and academic achievement is whether the relationship is causal. The coincidence of development may mean that two domains interact but not necessarily that either is influencing the developmental trajectory of the other. Recently, there has been much said about the need for early childhood curricula to focus on developing EF skills as a way to increase later academic achievement (for examples, see Bodrova & Leong, 2007; Diamond & Lee, 2011). Willoughby, Kupersmidt, and Voegler-Lee (2012) suggested caution in making these assertions without testing the associations between EF and academic skill using more conservative statistical techniques. We do not yet know the full range of potential covariates that could account for these associations, including both time-varying (e.g., growth in skill levels) and time-invariant (e.g., parental education) confounds. Using prior test scores on the constructs of interest may reduce many concerns about potential confounds. Willoughby et al. examined the association between simple difference scores on EF and achievement in reading, writing, and mathematics in pre-k. This technique statistically controlled for all measured and unmeasured time-invariant confounds and resulted in nonsignificant associations between EF and achievement. The fixed-effects approach, however, has both benefits (e.g., conservative controls) and limits (e.g., does not control for timevarying confounds and reliability of simple difference scores are dependent on differences between how much children’s scores change across time). Moreover, Willoughby et al. (2012) were limited by having only pre- and posttest scores over a single year. An alternative is to use path analyses to test the longitudinal associations between EF and academic skills from pre-k to kindergarten while controlling for the prior gains children made in each domain across pre-k. This approach can account for the development that occurs during pre-k to test whether these associations hold through kindergarten when children continue to gain in both EF and academic types of skills. While it is true that nonexperimental longitudinal studies cannot directly address causality, this approach can yield a better understanding of patterns of development in early childhood to highlight potentially fruitful directions for future causal research using experimental designs. In the current study, we assessed the longitudinal associations between EF and academic skills over three time points as children transitioned from the beginning of pre-k through the end of kindergarten. We expected that early EF skills would predict academic gains across mathematics, literacy, and language in the

pre-k year, but we predicted that EF would significantly predict only mathematics skills through kindergarten after controlling for pre-k gains because mathematics content continues to grow in complexity and demands. Consistent with prior research (Welsh et al., 2010), we predicted that there would be a bidirectional association between EF and mathematics across the pre-k year that would not hold for literacy and language, and we expected that bidirectional association to hold through kindergarten.

Method Participants Participants originally included 572 children recruited from 58 pre-k classrooms in four rural and semiurban school systems and a group of metropolitan community child care centers serving primarily low-income families. This sample was recruited to participate in a measurement study to evaluate self-regulation (including EF) assessments; no curriculum interventions were involved. Children participated in the pre-k or preschool activities their program dictated. There were 10 children who did not assent to one or more assessments in the fall of pre-k and were removed from the sample. Thus, 562 children completed the assessments in the fall of pre-k. Between fall and spring of pre-k, 26 children moved and one child did not provide assent, resulting in a sample of 535 at the spring of pre-k. Between the end of pre-k and the end of kindergarten, 22 of the children’s parents withdrew consent at the kindergarten assessment or the children were withdrawn by the research team because of participation in another study. Finally, 25 children moved or could not be located in their kindergarten year, yielding a final sample of 488 at the kindergarten assessments. We conducted t tests to compare those who completed all assessments to those with missing data; we did not find significant differences in either demographics or direct child assessments at the end of pre-k, ts(560) ⱕ 1.93, ps ⱖ .06, or at the end of kindergarten, ts(560) ⱕ 1.67, ps ⱖ .10, with 73% of the contrasts translating into small effect sizes (less than 0.1 standard deviation difference). Therefore, all children who had at least one time point of data (N ⫽ 562) were included in path analyses using full-information maximum likelihood estimation in Mplus. The mean age of the 562 children at the beginning of the study was 54 months (SD ⫽ 4; range: 46 – 65). There were slightly more males (52.1%; N ⫽ 293) than females. Child-level information regarding ethnicity and socioeconomic status was not collected due to district policies and Family Educational Rights and Privacy Act regulations; however, children came from ethnically and economically diverse school systems in semiurban and rural settings. Racial diversity in the study schools ranged from approximately 3% to 87% African American, 2% to 30% Hispanic, and 13% to 95% European American. Economic diversity in the study schools ranged from 27% to 100% of the school population qualifying for free or reduced-price lunch.

Procedure Children were assessed twice during the pre-k year—in the fall (early September through October) and in the spring (mid-March to early May), hereafter referred to as Time 1 and Time 2, respectively—and in the spring of kindergarten (mid-March to early

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

EXECUTIVE FUNCTIONING AND ACADEMIC SKILLS

May), referred to as Time 3. On average, the lapse in time between the Time 1 and Time 2 assessment periods was 6.29 months (SD ⫽ 0.37) with 18.24 months (SD ⫽ 0.66) between Time 1 and Time 3. Children were assessed individually in two or three sessions for 20 –30 minutes per session in a quiet area away from the classroom. The pre-k assessments were administered in three sessions, and the kindergarten assessments were administered in two sessions. On average, all pre-k sessions for a child occurred within a period of fewer than 15 days, and all kindergarten sessions occurred within a period of fewer than 5 days. Within a given session, measures were presented in a fixed order; however, the order of the sessions varied. In pre-k, the sessions included additional measures not reported in this study.

Measures Executive function. Six tasks were chosen based on results from a larger measurement study. These six measures of EF were closely associated with each other and captured individual variability in growth (see Fuhs & Turner, 2012). These measures also captured various methods of assessment (e.g., verbal and motor) and assessed the three specific skills typically comprising definitions of EF (inhibitory control, working memory, and attention flexibility; Garon et al., 2008). Detailed descriptions of each measure are available at the technical website (https://my.vanderbilt .edu/cogselfregulation/) for the study from which these data were drawn. We chose to use a battery of assessments and to analyze them as a composite in line with previous research (Bull et al., 2011; Wiebe et al., 2008), capitalizing on the shared variance among the measures. The Backward Digit Span (BDS; Davis & Pratt, 1995) involved asking children to listen to a set of orally presented numbers and then repeat the series back in reverse order, which assessed working memory. Children were provided two practice trials that included feedback for an incorrect response followed by six test trials with an increasing number of digits to recall. The task was terminated with the first incorrect response during the test trials. One point was scored for each number recalled correctly in sequence (e.g., the sequence 587 recalled backward as 875 received a score of 1). The final score was the sum of digits correctly recalled across the practice1 and test trials (range: 0 –23). Based on data from a different sample of pre-k children (see Lipsey et al., 2014), test–retest reliability following a 2-week delay for BDS was r ⫽ .73. The Copy Design task (Davie, Butler, & Goldstein, 1972; Osborn, Butler, & Morris, 1984) required children to replicate eight simple geometric designs from a printed model. Designs became more complex with each item, which required attentional flexibility via sustained attention during an increasingly difficult task. It should be noted that although this assessment likely taps motor and/or drawing skills, children must perform under a specific condition requiring sustained attention to detail. Two attempts were allowed for each design, and the quality of the best attempt was scored based on a defined set of criteria (range: 0 to 8). All coders of the task established interrater agreement (Cohen’s kappa) for eight designs in pre-k and kindergarten samples (␬s ⱖ .60 and .74, respectively). Copy Design test–retest reliability (Lipsey et al., 2014) was r ⫽ .72.

1701

The Dimensional Change Card Sort (DCCS; Frye, Zelazo, & Palfai, 1995; Zelazo, 2006), also an assessment of attention flexibility, required children to sort a set of six cards that had a picture of either a red or blue star or a red or blue truck, first according to color and then according to shape. The task therefore required children to flexibly shift their attention from one salient aspect of the card to another in order to comply with a new rule. Children were required to “pass” one sort (successfully sorted at least five of six cards) before moving on to the next rule. Children who passed the shape sort were also given a set of similar 12 cards that either did or did not include a black border around the edge of the card. Children were then asked to sort border cards by color and nonborder cards by shape; to pass this final shifting task, children had to achieve a score of at least 9. The examiner first demonstrated all sorting rules, and children were provided with a reminder of the sorting rule (e.g., “If there’s a border play the color game, if there’s no border play the shape game”) before each card they sorted. The prompt also indicated the salient aspect of the sorting rule (e.g., “Here’s one with a border”). On the basis of Zelazo’s (2006) scoring criteria, children received a score of 0 if they did not pass the color sort, 1 if they passed the color sort but not the shape sort, 2 if they passed the shape sort, and a 3 if they also passed the border sort (successfully sorted at least nine of the 12 cards). DCCS test–retest reliability (Lipsey et al., 2014) was moderate (r ⫽ .48). Although lower than the test–retest reliability of other EF tasks, these results are consistent with those of other studies assessing test–retest reliability on the DCCS in pre-k children (r ⫽ .44; Müller, Kerns, & Konkin, 2012), suggesting that this is not an artifact of the current study. We chose to keep this assessment in the battery because it correlates well with the other EF assessments and is widely used in studies predicting achievement in young children and is now a standardized measure in the NIH Toolbox (Weintraub et al., 2013). The Head–Toes–Knees–Shoulders task (HTKS; Ponitz, McClelland, Matthews, & Morrison, 2009) is an assessment that likely taps all three primary EF skills: cognitive flexibility, working memory, and inhibitory control (McClelland & Cameron, 2012). First, children were presented with prompts to “touch your head” and “touch your toes,” and children were asked to touch their heads when the assessor said, “Touch your toes,” and vice versa. Children completed six practice trials with feedback before completing 10 test trials. For children who responded correctly to at least half the test trials, two new prompts were added: “touch your shoulders” and “touch your knees.” Again, children were instructed to respond counter to the prompt, touching their shoulders when the assessor said, “Touch your knees,” and vice versa. Children completed four practice trials with feedback, followed by 10 test trials that included all four possible prompts. For each trial, children were scored 0 for an incorrect response, 1 for a selfcorrected response, and 2 for a correct response. The total score was the sum score across all practice and test trials (range: 0 –52). HTKS test–retest reliability (Lipsey et al., 2014) was r ⫽ .80. 1 We explored various scoring options systematically in a larger measurement study and found that including practice items in the scoring for BDS and HTKS produced scores that were more normally distributed and were more highly related to the other EF measures. Correlations between the scoring options were also large (rs ⫽ .96 –.99), thus warranting their inclusion in the final scores (Lipsey et al., 2014).

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

1702

FUHS, NESBITT, FARRAN, AND DONG

In the Kansas Reflection-Impulsivity Scale for Preschoolers (KRISP; Wright, 1971), an assessment of inhibitory control, children were presented with a series of drawings. Each set of drawings contained a target drawing and four to six other drawings that, except for one, varied from the target in minor ways (e.g., shading, additional or missing details). Children were asked to identify the drawing that was the duplicate of the target drawing; the KRISP assesses children’s ability to attend to details of presented items before making a response. Children were given three practice trials with feedback before continuing to the test trials that became progressively more difficult. In pre-k, children were given 12 test trials. In kindergarten, additional items of a higher difficulty were also included, resulting in 15 test trials. Children were given up to three attempts to identify the correct drawing. Each trial was scored by subtracting the total number of errors a child made from the total number of errors possible (range: 0 –36 for Time 1 and 2 and 0 – 45 for Time 3). KRISP test–retest reliability (Lipsey et al., 2014) was r ⫽ .64. The Peg Tapping task (Diamond & Taylor, 1996) required children to tap once with a wooden dowel when an examiner tapped twice and to tap twice when an examiner tapped once. The task assesses children’s inhibitory control and working memory. Children first received two practice trials with feedback for incorrect responses followed by up to eight attempts to demonstrate understanding of the two rules (i.e., respond correctly in two successive attempts) with no feedback. If they were able to demonstrate understanding, children completed 16 test trials with no feedback; if not, the task was terminated. Children received a score of 0 for incorrect responses and 1 for correct responses. If the task was terminated because of failure on the practice trials, children received a score of ⫺1. Scores could range from ⫺1 to 16. Peg Tapping test–retest reliability (Lipsey et al., 2014) was r ⫽ .80. Academic achievement. Five subtests from the Woodcock– Johnson III achievement battery (WJ-III; Woodcock, McGrew, & Mather, 2001) were used to assess children’s academic achievement. Four of the subscales of the WJ-III were outcomes for mathematics (Applied Problems and Quantitative Concepts), language (Oral Comprehension), and literacy (Letter– Word Identification). Applied Problems measures young children’s ability to solve small numerical and spatial problems, while Quantitative Concepts measures their understanding of number identification, sequencing, shapes, and symbols. Oral Comprehension requires children to use syntactic or semantic cues to complete a short passage. Lastly, Letter–Word Identification measures recognition of alphabet letters and sight words. The WJ-III Picture Vocabulary subscale scores (children’s ability to name objects presented in pictures) from the fall of pre-k were used as a covariate in all analyses as a proxy for general cognitive ability. The present study analyzed Rasch scaled W scores and included age at Time 1 as a covariate. Examination of standard scores at Time 1 indicated that, on average, the children in the sample were comparable to the average normed WJ-III standard score (M ⫽ 100, SD ⫽ 15). The 562 children had a standard score of 100.47 (SD ⫽ 12.09) on Applied Problems, 91.13 (SD ⫽ 12.41) on Quantitative Concepts, 96.42 (SD ⫽ 12.34) on Oral Comprehension, 96.61 (SD ⫽ 12.95) on Letter–Word Identification, and 101.06 (SD ⫽ 11.30) on Picture Vocabulary.

Analytic Strategy Analyses proceeded in two phases. First, exploratory factor analysis (EFA) was used to evaluate the dimensionality of the EF skills and academic achievement measures and create factor scores for further analyses. Second, a series of path analyses in Mplus 7.0 (Muthén & Muthén, 2012) using full-information maximum likelihood estimation was conducted to examine the longitudinal associations between EF skills and academic achievement after controlling for child gender, age at first wave of assessment, general cognitive ability (Picture Vocabulary), and the intervals of time elapsed between assessments. These control variables were covaried out of EF composites and academic achievement at all time points. We allowed the residuals of EF and academic achievement to be correlated at each of the three time points. Prior EF and academic achievement measures predicted later EF and academic achievement simultaneously. To account for nonindependence of observations (children nested within classrooms), chi-square tests of model fit and standard errors were adjusted with the Complex type command in Mplus.

Results Descriptive Statistics and Exploratory Factor Analysis Descriptive statistics for individual study measures are presented in Table 1. Correlations among all variables used in analyses are presented in Table 2. The concurrent correlations among the six measures of EF skills were all significant (p ⬍ .001) at each of the three assessment periods. Factor loadings from an EFA conducted separately at Times 1, 2, and 3 suggested a one-factor solution, and all loadings for each time point were greater than .40. The one-factor solution accounted for 33.93% of variance at Time 1, 35.20% at Time 2, and 29.86% at Time 3. The Cronbach’s alpha estimates, which assess the internal consistency of the items, were .74, .76, and .71 at Times 1, 2, and 3, respectively. We chose to utilize factor scores for each assessment period in the analyses for all three of our questions. This method capitalizes on the shared variance among the measures, which is particularly important for EF measures that also tap non-EF skills (e.g., motor and verbal skills). Although recent evidence suggests that in pre-k and kindergarten, child measures of EF skills may reflect a unitary general ability (Hughes et al., 2009; Wiebe et al., 2008, 2011; Willoughby et al., 2010), we also acknowledge the possibility that EF skills could be distinct skills that cannot be parsed apart because of the impure nature of EF assessments currently used in the literature. The correlations among the study’s five subtests of the WJ-III were also significant (p ⬍ .01) at all three time points (Table 2). An EFA of the four subtests (without Picture Vocabulary) produced a onefactor solution at all three assessment periods, and all loadings at each time point were greater than .60. The one-factor solution accounted for 55.41% at Time 1, 54.53% at Time 2, and 51.23% at Time 3. The Cronbach’s alpha estimates were .82, .82, and .83 at Times 1, 2, and 3, respectively. Even though empirically the four measures of academic achievement seemed to be assessing a unitary construct, conceptually, the subtests were designed to assess various aspects of

EXECUTIVE FUNCTIONING AND ACADEMIC SKILLS

1703

Table 1 Descriptive Statistics for Measures of Executive Functioning and Academic Achievement at Three Assessment Periods Time 1

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Measure Academic achievement Applied Problems Quantitative Concepts Letter–Word Identification Oral Comprehension Executive function Backward Digit Span Copy Design DCCS HTKS KRISP Peg Tapping

Time 2

Time 3

M

SD

M

SD

M

SD

397.59 409.73 325.62 447.59

22.92 13.43 26.22 14.65

412.66 423.56 348.30 454.30

21.38 14.91 23.84 14.16

438.81 448.60 397.00 468.20

16.08 13.32 27.02 13.04

1.31 1.39 1.47 14.58 28.85 6.98

1.19 1.43 0.57 15.55 4.13 5.96

2.05 2.27 1.75 23.65 31.44 10.21

2.13 1.70 0.52 17.57 3.13 5.48

4.39 4.85 2.10 40.20 41.09 14.08

3.58 1.66 0.52 12.52 3.16 2.84

Note. Academic achievement means are based on W scores. Executive function means are based on raw scores. Time 1 ⫽ beginning of prekindergarten; Time 2 ⫽ end of prekindergarten; Time 3 ⫽ end of kindergarten; DCCS ⫽ Dimensional Change Card Sort; HTKS ⫽ Head–Toes–Knees–Shoulders; KRISP ⫽ Kansas ReflectionImpulsivity Scale for Preschoolers.

achievement, including mathematics, language, and literacy. Because of the interest in the field in both general and content-specific relationships between EF and achievement, analyses were conducted on the academic achievement composite to address Research Questions 1 and 2 and on the four achievement subtests individually to address Question 3.

Path Analysis of Longitudinal Associations Between Executive Functioning Skills and Academic Achievement Our next set of analyses examined cross-lagged paths between EF and academic achievement across the three time points. Child’s

Table 2 Correlations Among All Variables Modeled in Path Analysis at Three Assessment Periods Measure Time 1 1. Applied Problems 2. Quantitative Concepts 3. Oral Comprehension 4. Letter–Word 5. EF factor 6. WJ factor Time 2 7. Applied Problems 8. Quantitative Concepts 9. Oral Comprehension 10. Letter–Word 11. EF factor 12. WJ factor Time 3 13. Applied Problems 14. Quantitative Concepts 15. Oral Comprehension 16. Letter–Word 17. EF factor 18. WJ factor Covariates Picture Vocabulary Age at Time 1 Gender (1 ⫽ male) Interval

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

— .67ⴱⴱ .61ⴱⴱ .45ⴱⴱ .67ⴱⴱ .86ⴱⴱ

— .48ⴱⴱ — .66ⴱⴱ .33ⴱⴱ — .65ⴱⴱ .57ⴱⴱ .46ⴱⴱ — .94ⴱⴱ .65ⴱⴱ .68ⴱⴱ .73ⴱⴱ

.70ⴱⴱ .61ⴱⴱ .60ⴱⴱ .44ⴱⴱ .65ⴱⴱ .73ⴱⴱ

.58ⴱⴱ .68ⴱⴱ .47ⴱⴱ .59ⴱⴱ .60ⴱⴱ .73ⴱⴱ

.52ⴱⴱ .43ⴱⴱ .71ⴱⴱ .30ⴱⴱ .54ⴱⴱ .57ⴱⴱ

.40ⴱⴱ .58ⴱⴱ .33ⴱⴱ .71ⴱⴱ .43ⴱⴱ .60ⴱⴱ

.65ⴱⴱ .64ⴱⴱ .56ⴱⴱ .48ⴱⴱ .81ⴱⴱ .73ⴱⴱ

.69ⴱⴱ .72ⴱⴱ .61ⴱⴱ .60ⴱⴱ .69ⴱⴱ .81ⴱⴱ

— .64ⴱⴱ .60ⴱⴱ .47ⴱⴱ .69ⴱⴱ .85ⴱⴱ

— .49ⴱⴱ — .65ⴱⴱ .39ⴱⴱ — .68ⴱⴱ .57ⴱⴱ .51ⴱⴱ — .92ⴱⴱ .69ⴱⴱ .72ⴱⴱ .77ⴱⴱ

.64ⴱⴱ .51ⴱⴱ .58ⴱⴱ .43ⴱⴱ .53ⴱⴱ .66ⴱⴱ

.59ⴱⴱ .56ⴱⴱ .52ⴱⴱ .57ⴱⴱ .50ⴱⴱ .68ⴱⴱ

.44ⴱⴱ .34ⴱⴱ .64ⴱⴱ .30ⴱⴱ .43ⴱⴱ .49ⴱⴱ

.44ⴱⴱ .54ⴱⴱ .36ⴱⴱ .60ⴱⴱ .38ⴱⴱ .58ⴱⴱ

.64ⴱⴱ .54ⴱⴱ .54ⴱⴱ .49ⴱⴱ .68ⴱⴱ .67ⴱⴱ

.66ⴱⴱ .60ⴱⴱ .62ⴱⴱ .58ⴱⴱ .57ⴱⴱ .74ⴱⴱ

.63ⴱⴱ .51ⴱⴱ .55ⴱⴱ .41ⴱⴱ .55ⴱⴱ .64ⴱⴱ

.63ⴱⴱ .63ⴱⴱ .49ⴱⴱ .60ⴱⴱ .56ⴱⴱ .72ⴱⴱ

.48ⴱⴱ .39ⴱⴱ .67ⴱⴱ .33ⴱⴱ .46ⴱⴱ .53ⴱⴱ

.49ⴱⴱ .58ⴱⴱ .36ⴱⴱ .70ⴱⴱ .43ⴱⴱ .64ⴱⴱ

.68ⴱⴱ .59ⴱⴱ .54ⴱⴱ .48ⴱⴱ .73ⴱⴱ .71ⴱⴱ

.70ⴱⴱ .65ⴱⴱ .61ⴱⴱ .61ⴱⴱ .62ⴱⴱ .80ⴱⴱ

— .68ⴱⴱ .56ⴱⴱ .55ⴱⴱ .62ⴱⴱ .91ⴱⴱ

— .46ⴱⴱ .61ⴱⴱ .62ⴱⴱ .88ⴱⴱ

.53ⴱⴱ .49ⴱⴱ .62ⴱⴱ .38ⴱⴱ .44ⴱⴱ .59ⴱⴱ .43ⴱⴱ .25ⴱⴱ .25ⴱⴱ .20ⴱⴱ .20ⴱⴱ .33ⴱⴱ .28ⴱⴱ .20ⴱⴱ .06 .06 .10ⴱ .15ⴱⴱ .15ⴱⴱ .08 .06 .02

.39ⴱⴱ .24ⴱⴱ .08 .04

.61ⴱⴱ .19ⴱⴱ .10ⴱ .05

.35ⴱⴱ .13ⴱⴱ .19ⴱⴱ .04

.38ⴱⴱ .28ⴱⴱ .21ⴱⴱ .02

.51ⴱⴱ .25ⴱⴱ .11ⴱ .05

.38ⴱⴱ .17ⴱⴱ .03 .00

.36ⴱⴱ .61ⴱⴱ .14ⴱⴱ .16ⴱⴱ .10ⴱ .03 .00 ⫺.06

16

17

18

— .72ⴱⴱ

—

—

—

— .40ⴱⴱ — .52ⴱⴱ .49ⴱⴱ .65ⴱⴱ .75ⴱⴱ

.33ⴱⴱ .31ⴱⴱ .46ⴱⴱ .13ⴱⴱ .25ⴱⴱ .18ⴱⴱ .17ⴱⴱ .20ⴱⴱ .09 .01 ⫺.01 ⫺.01

Note. Zero-order correlations between Time 2 (end of prekindergarten) measures and interval are for the duration of time that elapsed between Time 1 (beginning of prekindergarten) and Time 2. Zero-order correlations between Time 3 (end of kindergarten) measures and interval are for the duration of time that elapsed between Time 1 and Time 3. EF ⫽ executive functioning; WJ ⫽ Woodcock–Johnson III achievement battery. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

1704

FUHS, NESBITT, FARRAN, AND DONG

gender, age at the first wave of assessment, the interval of time elapsed from the first wave of assessment to the second and third waves, and general cognitive ability (Picture Vocabulary) were controlled. Figures 1–3 display the results for the path analyses broken down by subtest. For simplification, we do not report the estimates for the covariates in the figures. The standardized coefficients are presented in the figures, where solid lines and bold text indicate estimates with a p value less than .05. Dashed lines and bold text indicate marginal paths. EF and composite academic achievement. To test our Research Questions 1 and 2 for general achievement, we first entered the EF and the academic achievement factor scores into the path models. The model testing the longitudinal associations between EF skills and general academic achievement (i.e., achievement factor score) fit the data very well, ␹2(8) ⫽ 8.74, p ⫽ .37, Bentler comparative fit index (CFI) ⫽ 1.00, root-mean-square error of approximation (RMSEA) ⫽ .01, standardized root-mean-square residual (RMSR) ⫽ .01. In line with prior research, we found that across the pre-k year (Time 1 to Time 2), there was a bidirectional association between EF and the academic achievement composite score. The path from Time 1 EF to Time 2 academic achievement was significant (␤ ⫽ .29, SE ⫽ .03, p ⬍ .001), as was the path from Time 1 academic achievement to Time 2 EF (␤ ⫽ .22, SE ⫽ .05, p ⬍ .001). With respect to our first research question concerning whether EF skills predict later academic achievement through kindergarten controlling for pre-k gains in both EF and academic skills, we found that EF skills at the end of pre-k (Time 2) had a positive direct influence on global academic achievement at the end of kindergarten (Time 3; ␤ ⫽ .23, SE ⫽ .05, p ⬍ .001). This suggests that when prior gains in both EF and academic achievement are controlled, EF skills continue to predict later academic achievement in young children. With respect to our second research question about the potential for bidirectional pathways between EF and academic skills after controlling for pre-k gains in both domains, the path from Time 2 achievement to Time 3 EF was only marginal (␤ ⫽ .12, SE ⫽ .07, p ⫽ .080). We then proceeded to test our research questions for each academic achievement subtest separately to investigate differential associations by academic content area as proposed in Question 3. Content areas were addressed through several separate models, presented by area below. EF and domain-specific mathematics achievement. Mathematics achievement was measured with the WJ-III Applied Problems and Quantitative Concepts subtests. As shown in Figures 1A and 1B, separate models were conducted to examine the associations between EF skills and each of the subtests. The model testing the longitudinal associations between EF skills and Applied Problems fit the data very well, ␹2(8) ⫽ 6.83, p ⫽ .56, Bentler CFI ⫽ 1.00, RMSEA ⫽ .00, standardized RMSR ⫽ .01 (Figure 1A), as did the model with Quantitative Concepts, ␹2(8) ⫽ 7.43, p ⫽ .49, Bentler CFI ⫽ 1.00, RMSEA ⫽ .00, standardized RMSR ⫽ .01 (Figure 1B). In line with the overall academic achievement model, we found bidirectional associations between EF and mathematics skills in the pre-k year (Time 1 to Time 2). Extending previous research, our results indicated that EF skills at the end of pre-k (Time 2) had a positive direct effect on mathematics performance at the end of kindergarten (Time 3) for both Applied Problems and Quantitative Concepts after accounting for pre-k gains in both EF skills and mathematics. The path from

Time 2 Applied Problems to Time 3 EF skills was nonsignificant, and the path from Time 2 Quantitative Concepts to Time 3 EF was marginal. EF and domain-specific language achievement. Language achievement was measured with the WJ-III Oral Comprehension subtest. The model fit the data well, ␹2(8) ⫽ 10.16, p ⫽ .25, Bentler CFI ⫽ 1.00, RMSEA ⫽ .02, standardized RMSR ⫽ .01 (see Figure 2). In line with the mathematics results for pre-k, we found bidirectional associations between EF and Oral Comprehension across the pre-k year. Results indicated that EF skills at the end of pre-k (Time 2) were significantly associated with Oral Comprehension performance at the end of kindergarten (Time 3) after accounting for pre-k gains in both EF skills and Oral Comprehension. However, the path from Time 2 Oral Comprehension to Time 3 EF skills was nonsignificant, which did not support a bidirectional association between EF skills and language skills through kindergarten. In summary, EF skills continued to predict Oral Comprehension through kindergarten, but the inverse path from Oral Comprehension pre-k gains to EF skills through kindergarten did not hold. EF and domain-specific literacy achievement. Literacy achievement was measured with the WJ-III Letter–Word Identification subtest. The model testing the longitudinal association between EF skills and Letter–Word fit the data adequately, ␹2(8) ⫽ 21.32, p ⫽ .01, Bentler CFI ⫽ .99, RMSEA ⫽ .05, standardized RMSR ⫽ .02 (see Figure 3). Children’s EF skills at the beginning of pre-k predicted end of pre-k Letter–Word performance, but Letter–Word performance at the beginning of pre-k did not predict end of pre-k EF skills. Results indicated that EF skills at the end of pre-k (Time 2) did not have a direct effect on Letter–Word performance at the end of kindergarten (Time 3) after accounting for pre-k gains in both EF skills and Letter–Word. Likewise, the path from Time 2 Letter–Word to Time 3 EF skills was nonsignificant, which did not support a bidirectional association between EF skills and Letter–Word Identification through kindergarten. Results from the literacy analyses are consistent with hypotheses that literacy skills may be less dependent on EF skills through kindergarten compared to mathematics.

Discussion With respect to the global achievement factor, growth in EF and general achievement skills were interrelated in the pre-k year, the first year children were likely exposed to a formal learning environment. EF pre-k gains were significantly predictive of continued academic gains in kindergarten, whereas achievement gains at the end of pre-k only marginally related to continued EF gains in kindergarten. These results are consistent with those of Stipek et al. (2010) in the sense that both sets of results indicated that EF (or an EF-related construct) continued to predict academic gains beyond early childhood. We also found some support for a trend for a bidirectional association, whereas Stipek et al. did not. However, the academic achievement composite results alone do not give a complete picture of the nature of associations between EF and academic skills because academic skills were collapsed into a composite in a similar way to the reading composite used by Stipek et al. Thus, we proceeded to test associations by separating academic content areas to understand better the specificity of these effects.

EXECUTIVE FUNCTIONING AND ACADEMIC SKILLS

1705

A. Applied Problems Time 1

Time 2

Time 3

.24(.06)

.73 T1 Executive Function

.44

.32 T2 Executive Function

.66(.03)

T3 Executive Function

.49(.07)

.21(.04) .04(.05)

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

.55(.03)

.13(.06) .34(.04) T1 Applied Problems .68

T2 Applied Problems

.45(.05) .30(.04)

.04(.07) .36(.05) .35(.07)

.14(.05)

T3 Applied Problems .45

.45 .25(.04)

B. Quantitative Concepts Time 1

Time 2

Time 3

.24(.06)

.73 T1 Executive Function

.34 .70(.03)

.44

T2 Executive Function

.12(.04) -.01(.04)

.53 (.03)

.01(.05) .32(.04) T1 Quant Concepts .73

.45(.04) .34(.04)

T2 Quant Concepts .47

.47(.07)

T3 Executive Function

.09(.05) .33(.04) .26(.06)

.33(.05)

T3 Quant Concepts .54

.17(.05)

Figure 1. Path analysis of executive function and measures of mathematics at the beginning of prekindergarten (Time 1 [T1]), end of prekindergarten (Time 2 [T2]), and end of kindergarten (Time 3 [T3]). Estimates provided are standardized coefficients, and standard errors are presented in parentheses. Bolded estimates with solid lines were significant at p ⬍ .05. Bolded estimates with dashed lines were significant at p ⬍ .10. Single-headed arrows indicate the predicted direction of effects, and the double-headed arrows indicate associations between two residual variances. For the purpose of simplification, model covariates are not reported in the figure. Quant ⫽ Quantitative.

Examining the content areas separately, we found that EF at the beginning of pre-k predicted gains in all academic content areas across pre-k. Bidirectional associations in pre-k were found for mathematics and oral comprehension. We found support for the hypothesis that gains in pre-k EF continue to predict gains through kindergarten in mathematics, but contrary to our hypothesis, these associations held for language as well. We hypothesized that the associations between EF and mathematics skills would remain bidirectional through the kindergarten year when controlling for pre-k gains. However, there was only marginal evidence for a bidirectional association between EF and one of the math measures through the kindergarten year. Our primary questions of interest concerned the associations between children’s EF and academic gains through kindergarten

after controlling for pre-k gains, although it should be noted that our pre-k findings were generally consistent with prior theory and empirical work suggesting that EF skills may enable children to adapt to classroom demands and learn new skills across academic content areas during pre-k. However, we expected for only mathematics skills to continue to be predicted by EF through kindergarten because mathematics skills continue to build on one another, with new activities such as addition, subtraction, and using simple word problems that rely heavily on EF skills even when basic number sense skills may start to become more automatized. Blair et al. (2008) have also speculated about the reasons EF continues to be salient for mathematics learning, arguing that attention and persistence are more important for the kinds of new problems children encounter in kindergarten. Consistent with our

FUHS, NESBITT, FARRAN, AND DONG

1706 Time 1

Time 2

Time 3

.25 (.06)

T1 Executive Function

.44

.33

.73

T2 Executive Function

.72(.03)

.49(.06)

T3 Executive Function

.13(.04) .01(.04)

.40(.03)

.06(.05)

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

.20(.04) T1 Oral Comprehension .60

.44(.04)

T2 Oral Comprehension

.18(.04)

.05(.05) .22(.04) .16(.06) .28(.05)

T3 Oral Comprehension .42

.42 .19(.05)

Figure 2. Path analysis of executive function and Oral Comprehension at the beginning of prekindergarten (Time 1 [T1]), end of prekindergarten (Time 2 [T2]), and end of kindergarten (Time 3 [T3]). Estimates provided are standardized coefficients, and standard errors are presented in parentheses. Bolded estimates with solid lines were significant at p ⬍ .05. Single-headed arrows indicate the predicted direction of effects, and the doubleheaded arrows indicate associations between two residual variances. For the purpose of simplification, model covariates are not reported in the figure.

passage. Children must first attend to the passage, then remember the words and use them to generate a likely candidate word to complete the idea. The basis for comprehension is attention and memory, perhaps accounting for the connection between the two domains. In fact, Stipek et al. (2010) included a print version of this same type of assessment (Passage Comprehension), which could have driven their significant findings for the association

predictions, EF skills continued to relate to both of our mathematics assessments through kindergarten. Unexpectedly, however, we also found an effect for language skill development through kindergarten of a smaller, but still significant, magnitude than that found for the mathematics subtests. The Oral Comprehension subtest requires children to complete short orally presented passages based on cues provided in the

Time 1

Time 2

Time 3

.25(.05)

T1 Executive Function

.44

.34

.73 .75(.03)

T2 Executive Function

.50(.06)

T3 Executive Function

.04(.03) -.02(.05)

.32(.04)

.12(.06) .18(.04) T1 Letter Word .82

.61(.05) .21(.04)

T2 Letter Word .46

.07(.05) .18(.04) .07(.06) .49(.05)

T3 Letter Word .47

.15(.05)

Figure 3. Path analysis of executive function and literacy skills—Letter–Word Identification—at the beginning of prekindergarten (Time 1 [T1]), end of prekindergarten (Time 2 [T2]), and end of kindergarten (Time 3 [T3]). Estimates provided are standardized coefficients, and standard errors are presented in parentheses. Bolded estimates with solid lines were significant at p ⬍ .05. Bolded estimates with dashed lines were significant at p ⬍ .10. Single-headed arrows indicate the predicted direction of effects, and the double-headed arrows indicate associations between two residual variances. For the purpose of simplification, model covariates are not reported in the figure.

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

EXECUTIVE FUNCTIONING AND ACADEMIC SKILLS

between learning-related skills and a reading composite. While vocabulary by itself may become more automatized, EF skills may continue to be implicated in tasks that involve comprehension. This distinction requires further research to understand better the specificity of the association between EF and language as children develop. The nonsignificant kindergarten literacy effect is consistent with prior research (Welsh et al., 2010) and supports our hypothesis that these types of skills that involve basic letter identification become more rote or automatized and may require less higher order cognitive processing. In summary, our pre-k findings were consistent with extant literature suggesting global effects of EF on pre-k academic gains. Our extended examination of these associations through kindergarten suggests a more domain-specific account where EF is more closely associated with academic content areas in which activities are more likely to tax EF specifically. Our findings are not consistent with findings from the fixedeffects approach used by Willoughby et al. (2012). There are several potential explanations for the differences. First, the fixedeffects approach is dependent on the reliability of differences scores; this reliability increases based on how much scores change across time points (Rogosa & Willett, 1983). Compared to the sample of Willoughby et al., our sample had a longer pre-k pretest and posttest interval (6.3 vs. 4.4 months), and our sample also exhibited greater change on measures we have in common (e.g., 3.2 points compared to 2.0-point gain on Peg Tapping). Second, Willoughby et al. analyzed the EF measures separately instead of as a composite. It is possible that our use of an EF factor reduced measurement error often associated with measures of EF and allowed us to detect significant associations. Finally, we examined change across time, into the kindergarten year, instead of across a relatively short interval during the pre-k year. One important extension of prior work is this examination of bidirectional associations between EF and academic skills through kindergarten. Only a few prior studies assessed bidirectional associations in pre-k (Fuhs & Day, 2011; Weiland et al., 2014; Welsh et al., 2010), and the current study is unique in extending these analyses across multiple academic content areas through kindergarten, controlling for pre-k gains in both EF and academic skills. Across pre-k, we found bidirectional associations between EF and both mathematics subtests and Oral Comprehension, but not Letter–Word Identification. The finding that EF was reciprocally associated with mathematics but not literacy skills is consistent with the findings of Welsh et al. (2010), and the Oral Comprehension findings provide insight into previous conflicting findings regarding language skills and EF (Fuhs & Day, 2011; Weiland et al., 2014). Fuhs and Day (2011) found a significant correlation between language at the beginning of pre-k and EF at the end of pre-k, but not the reverse. However, the standardized coefficient for the inverse path was .16, which was similar to the coefficient in the current study (.20), suggesting that the small sample size may have prevented them from finding a significant bidirectional association. In comparison, Weiland et al. (2014) used a single measure of receptive vocabulary and found that EF predicted language gains in pre-k, but not the reverse. This study did not include a more complex assessment of language beyond vocabulary, suggesting that the cognitively demanding assessment of language likely accounts for the bidirectional effects.

1707

We expected to find bidirectional associations between EF and mathematics through kindergarten, but we only found a trend for EF and one of the mathematics subtests, Quantitative Concepts, which assesses patterning and sequencing skills. It may be the case that there are certain mathematics skills that might work to facilitate EF skills beyond pre-k, but not all mathematics skills. This is an important area for future work to assess how different subskills within the mathematics content area are associated with EF skills in young children. Overall, finding that the associations between EF skills and mathematics achievement were stronger than those for language and literacy adds to a growing body of theoretical and empirical work suggesting a particularly strong association between EF skills and mathematics. Although it may be true that mathematics content demands more EF skills to master it, an alternative explanation could be that the association is a product of early childhood classroom demands. Observational research of early childhood classrooms indicates that teachers spend substantially more time in, and exhibit greater instructional quality for, literacy instruction compared to mathematics instruction (Hamre & Pianta, 2007; Hofer, Farran, & Cummings, 2013; Milesi & Gamoran, 2006). It is possible, therefore, that mastery of mathematics skills in early childhood may depend to a greater extent on children independently initiating engagement with mathematics content (e.g., choosing mathematics manipulatives during free choice centers), and hence may be influenced to a greater extent by children’s EF skills. The bidirectional findings in pre-k suggest that participating in these mathematics activities could facilitate further development of EF skills. The notion that pre-k classrooms should focus on improving EF skills with the goal of improving academic learning has been gaining traction. These goals depend on the idea that EF skills are malleable and can be improved with different instructional practices; however, the effectiveness of these efforts when evaluated experimentally has been mixed. The Tools of the Mind curriculum (Bodrova & Leong, 2007) directly targets EF skills across the school day, but in several recent cluster-randomized trials of the curriculum in pre-k, researchers did not find significantly greater gains in either EF skills or academic achievement for children who were part of experimental classrooms (e.g., Barnett, Jung, Yarosz, Thomas, & Hornbeck, 2008; Clements, Sarama, Unlu, & Layzer, 2012; Lonigan & Phillips, 2012; Wilson & Farran, 2012). An indirect approach to modifying children’s EF skills as a way of improving academic skills was taken in the Chicago School Readiness Project (Raver et al., 2011). Raver et al. (2001) found that children in Head Start pre-k classrooms where teachers were trained in behavioral management and received assistance from a mental health counselor for reducing stress in the classroom, made significantly more gains on some EF and academic skills compared to a control group. Furthermore, they found that observer ratings of EF skills actually mediated the relationship between condition and academic outcomes, suggesting that the mechanism through which better managed classrooms had an effect on academic outcomes was through the increase in children’s EF skills. Our results support the idea that targeting EF skills broadly may be appropriate for pre-k. Continued efforts to validate practices that actually increase children’s EF skills are warranted. However, bidirectional associations between EF and mathematics and language skills in pre-k and a trend for Quantitative Concepts in

FUHS, NESBITT, FARRAN, AND DONG

1708

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

kindergarten suggest that specific academic activities may actually enhance children’s EF, which has not previously been tested experimentally in applied settings. This is an important area for future study, as it has implications for the development of mathematics and language curricula that include the types of activities assessed in these measures. Although experimental work thus far suggests EF skills may be malleable under certain circumstances, much more work is needed in the way of experimental research to address the contexts in which improvements in EF skills are likely to occur, especially with respect to the potential for specific mathematics activities in particular to enhance young children’s EF skills.

Limitations Several limitations to this study must be acknowledged. First, although we controlled for demographics such as age, gender, and testing interval, we could not rule out potential effects of other demographic covariates such as ethnicity (unavailable to us). Second, we did not have access to scores on a full-scale IQ measure; we were able to use the Picture Vocabulary subtest of the WJ-III as a proxy for general cognitive ability, which increased our ability to control for potential confounds. Another possible limitation is that the current study findings are limited to children who are from low-income homes and who are attending a pre-k program. These results may not generalize to children who are from higher socioeconomic-status backgrounds or who do not attend pre-k prior to kindergarten. Arguments for the applicability of this sample to others rest with the WJ-III standard scores that the children received at pretest that were at least similar to the normative sample on which WJ-III was standardized. Lastly, we cannot of course infer causality from correlational data. The strength of this particular study was the ability to account for the gains in children’s EF and academic skills in pre-k, which yielded a conservative approach to address the sustained effects of these two domains through kindergarten and provides a sound basis on which to build future experimental work.

Conclusion The findings of this study suggest that EF skills may promote the development of early mathematics and oral comprehension skills at a time when children are transitioning to a more formal schooling environment in which academic skills development is an important part of their day. Efforts to validate approaches for enhancing EF skills as well as including EF outcomes of mathematics and oral comprehension programs in pre-k and of mathematics programs in kindergarten are warranted toward the goal of fostering children’s readiness for schooling and facilitating their continued acquisition of academic skills.

References Allan, N. P., & Lonigan, C. J. (2011). Examining the dimensionality of effortful control in preschool children and its relation to academic and socioemotional indicators. Developmental Psychology, 47, 905–915. doi:10.1037/a0023748 Anderson, P. (2002). Assessment and development of executive function (EF) during childhood. Child Neuropsychology, 8, 71– 82. doi:10.1076/ chin.8.2.71.8724

Barnett, W. S., Jung, K., Yarosz, D. J., Thomas, J., & Hornbeck, A. (2008). Educational effects of the Tools of the Mind curriculum: A randomized trial. Early Childhood Research Quarterly, 23, 299 –313. doi:10.1016/j .ecresq.2008.03.001 Blair, C., & Diamond, A. (2008). Biological processes in prevention and intervention: The promotion of self-regulation as a means of preventing school failure. Development and Psychopathology, 20, 899 –911. doi: 10.1017/S0954579408000436 Blair, C., Knipe, H., & Gamson, D. (2008). Is there a role for executive functions in the development of mathematics ability? Mind, Brain, and Education, 2, 80 – 89. doi:10.1111/j.1751-228X.2008.00036.x Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78, 647– 663. doi:10.1111/ j.1467-8624.2007.01019.x Bodrova, E., & Leong, D. J. (2007). Tools of the Mind: The Vygotskian approach to early childhood education (2nd ed.). Upper Saddle River, NJ: Pearson. Brock, L. L., Rimm-Kaufman, S. E., Nathanson, L., & Grimm, K. J. (2009). The contributions of “hot” and “cool” executive function to children’s academic achievement, learning-related behaviors, and engagement in kindergarten. Early Childhood Research Quarterly, 24, 337–349. doi:10.1016/j.ecresq.2009.06.001 Bull, R., Espy, K. A., Wiebe, S. A., Sheffield, T. D., & Nelson, J. M. (2011). Using confirmatory factor analysis to understand executive control in preschool children: Sources of variation in emergent mathematic achievement. Developmental Science, 14, 679 – 692. doi:10.1111/ j.1467-7687.2010.01012.x Bull, R., & Scerif, G. (2001). Executive functioning as a predictor of children’s mathematics ability: Inhibition, switching, and working memory. Developmental Neuropsychology, 19, 273–293. doi:10.1207/ S15326942DN1903_3 Carlson, S. M. (2005). Developmentally sensitive measures of executive function in preschool children. Developmental Neuropsychology, 28, 595– 616. doi:10.1207/s15326942dn2802_3 Clark, C. A. C., Pritchard, V. E., & Woodward, L. J. (2010). Preschool executive functioning abilities predict early mathematics achievement. Developmental Psychology, 46, 1176 –1191. doi:10.1037/a0019672 Clements, D. H., Sarama, J., Unlu, F., & Layzer, C. (2012, March). The efficacy of an intervention synthesizing scaffolding designed to promote self-regulation with an early mathematics curriculum: Effects of executive function. Paper presented at the annual spring meeting of the Society for Research on Educational Effectiveness, Washington, DC. Davie, R., Butler, N. R., & Goldstein, H. (1972). From birth to seven: The second report of the National Child Development Study. London, England: Longman. Davis, H. L., & Pratt, C. (1995). The development of children’s theory of mind: The working memory explanation. Australian Journal of Psychology, 47, 25–31. doi:10.1080/00049539508258765 Diamond, A., & Lee, K. (2011). Interventions shown to aid executive function development in children 4 to 12 years old. Science, 333, 959 –964. doi:10.1126/science.1204529 Diamond, A., & Taylor, C. (1996). Development of an aspect of executive control: Development of the abilities to remember what I said and to “Do as I say, not as I do.” Developmental Psychobiology, 29, 315–334. doi:10.1002/(SICI)1098-2302(199605)29:4⬍315::AID-DEV2⬎3.0.CO; 2-T Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., . . . Duckworth, K. (2007). School readiness and later achievement. Developmental Psychology, 43, 1428 –1446. doi:10.1037/ 0012-1649.43.6.1428 Frye, D., Zelazo, P. D., & Palfai, T. (1995). Theory of mind and rule-based reasoning. Cognitive Development, 10, 483–527. doi:10.1016/08852014(95)90024-1

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

EXECUTIVE FUNCTIONING AND ACADEMIC SKILLS Fuhs, M. W., Byrd, C. E., & McNeil, N. M. (2013, October). Specific number sense skills mediate the association between inhibitory control and mathematics achievement. Poster presented at the Eighth Biennial Meeting of the Cognitive Development Society, Memphis, TN. Fuhs, M. W., & Day, J. D. (2011). Verbal ability and executive functioning development in preschoolers at Head Start. Developmental Psychology, 47, 404 – 416. doi:10.1037/a0021065 Fuhs, M. W., & Turner, K. A. (2012, February). Evaluating group and longitudinal measurement equivalence in a battery of cognitive selfregulation measures for preschoolers. Poster presented at the Society for Research in Child Development 2012 Themed Meeting: Developmental Methodology. Tampa, FL. 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:10.1037/0033-2909.134.1.31 Hamre, B. K., & Pianta, R. C. (2007). Learning opportunities in preschool and early elementary classrooms. In R. C. Pianta, M. J. Cox, & K. L. Snow (Eds.), School readiness and the transition to kindergarten in the era of accountability (pp. 49 – 83). Baltimore, MD: Brookes. Hofer, K., Farran, D. C., & Cummings, T. (2013). Preschool children’s math-related behaviors mediate curriculum effects on math achievement gains. Early Childhood Research Quarterly, 28, 487– 495. doi:10.1016/ j.ecresq.2013.02.002 Hughes, C., Ensor, R., Wilson, A., & Graham, A. (2009). Tracking executive function across the transition to school: A latent variable approach. Developmental Neuropsychology, 35, 20 –36. doi:10.1080/ 87565640903325691 Johnson, M. H. (2001). Functional brain development in humans. Nature Reviews Neuroscience, 2, 475– 483. doi:10.1038/35081509 Lipsey, M. W., Turner, K. A., Farran, D. C., Dong, N., Fuhs, M. W., & Wilson, S. J. (2014). Learning-related cognitive self-regulation school readiness measures for preschool children: Optimizing predictive validity for achievement. Manuscript in preparation. Lonigan, C. J., & Phillips, B. M. (2012, March). Comparing skills-focused and self-regulation focused preschool curricula: Impacts on academic and self-regulatory skills. Paper presented at the annual spring meeting of the Society for Research on Educational Effectiveness, Washington, DC. McClelland, M. M., & Cameron, C. E. (2012). Self-regulation in early childhood: Improving conceptual clarity and developing ecologically valid measures. Child Development Perspectives, 6, 136 –142. doi: 10.1111/j.1750-8606.2011.00191.x McClelland, M. M., Cameron, C. E., Connor, C. M., Farris, C. L., Jewkes, A. M., & Morrison, F. J. (2007). Links between behavioral regulation and preschoolers’ literacy, vocabulary, and math skills. Developmental Psychology, 43, 947–959. doi:10.1037/0012-1649.43.4.947 Milesi, C., & Gamoran, A. (2006). Effects of class size and instruction on kindergarten achievement. Educational Evaluation and Policy Analysis, 28, 287–313. doi:10.3102/01623737028004287 Müller, U., Kerns, K. A., & Konkin, K. (2012). Test–retest reliability and practice effects of executive function tasks in preschool children. Clinical Neuropsychologist, 26, 271–287. doi:10.1080/13854046.2011 .645558 Muthén, L. K., & Muthén, B. O. (2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén. Osborn, A. F., Butler, N. R., & Morris, A. C. (1984). The social life of Britain’s five-year-olds. London, England: Routledge and Paul.

1709

Ponitz, C. C., McClelland, M. M., Matthews, J. S., & Morrison, F. J. (2009). A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes. Developmental Psychology, 45, 605– 619. doi:10.1037/a0015365 Raver, C. C., Jones, S. M., Li-Grining, C., Zhai, F., Bub, K., & Pressler, E. (2011). CSRP’s impact on low-income preschoolers’ preacademic skills: Self-regulation as a mediating mechanism. Child Development, 82, 362–378. doi:10.1111/j.1467-8624.2010.01561.x Rogosa, D. R., & Willett, J. B. (1983). Demonstrating the reliability of the difference score in the measurement of change. Journal of Educational Measurement, 20, 335–343. doi:10.1111/j.1745-3984.1983.tb00211.x Stipek, D., Newton, S., & Chudgar, A. (2010). Learning-related behaviors and literacy achievement in elementary school-aged children. Early Childhood Research Quarterly, 25, 385–395. doi:10.1016/j.ecresq.2009 .12.001 Weiland, C., Barata, M. C., & Yoshikawa, H. (2014). The co-occurring development of executive function skills and receptive vocabulary in preschoolaged children: A look at the direction of the developmental pathways. Infant and Child Development, 23, 4–21. doi:10.1002/icd.1829 Weintraub, S., Dikmen, S. S., Heaton, R. K., Tulsky, D. S., Zelazo, P. D., . . . Gershon, R. C. (2013). Cognition assessment using the NIH Toolbox. Neurology, 80, S54 –S64. doi:10.1212/WNL.0b013e3182872ded Welsh, J. A., Nix, R. L., Blair, C., Bierman, K. L., & Nelson, K. E. (2010). The development of cognitive skills and gains in academic school readiness for children from low-income families. Journal of Educational Psychology, 102, 43–53. doi:10.1037/a0016738 Wiebe, S. A., Espy, K. A., & Charak, D. (2008). Using confirmatory factor analysis to understand executive control in preschool children: I. Latent structure. Developmental Psychology, 44, 575–587. doi:10.1037/00121649.44.2.575 Wiebe, S. A., Sheffield, T., Nelson, J. M., Clark, C. A. C., Chevalier, N., & Espy, K. A. (2011). The structure of executive function in 3-year-olds. Journal of Experimental Child Psychology, 108, 436 – 452. doi:10.1016/ j.jecp.2010.08.008 Willoughby, M. T., Blair, C. B., Wirth, R. J., Greenberg, M., & The Family Life Project Investigators. (2010). The measurement of executive function at age 3 years: Psychometric properties and criterion validity of a new battery of tasks. Psychological Assessment, 22, 306 –317. doi: 10.1037/a0018708 Willoughby, M. T., Kupersmidt, J. B., & Voegler-Lee, M. E. (2012). Is preschool executive function casually related to academic achievement? Developmental Neuropsychology, 36, 79 –91. doi:10.1080/09297049 .2011.578572 Wilson, S. J., & Farran, D. C. (2012, March). Experimental evaluation of the Tools of the Mind preschool curriculum. Paper presented at the annual spring meeting of the Society for Research on Educational Effectiveness, Washington, DC. Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock– Johnson III Tests of Achievement. Itasca, IL: Riverside. Wright, J. C. (1971). KRISP (Kansas Reflection-Impulsivity Scale for Preschoolers). Unpublished manuscript, University of Kansas, Lawrence. Zelazo, P. D. (2006). The Dimensional Change Card Sort (DCCS): A method of assessing executive function in children. Nature Protocols, 1, 297–301. doi:10.1038/nprot.2006.46

Received June 27, 2013 Revision received January 28, 2014 Accepted March 15, 2014 䡲