Child Development, September/October 2014, Volume 85, Number 5, Pages 1932–1947

Longitudinal Relations Among Parents’ Reactions to Children’s Negative Emotions, Effortful Control, and Math Achievement in Early Elementary School Jodi Swanson, Carlos Valiente, Kathryn Lemery-Chalfant, Robert H. Bradley, and Natalie D. Eggum-Wilkens Arizona State University

Panel mediation models and fixed-effects models were used to explore longitudinal relations among parents’ reactions to children’s displays of negative emotions, children’s effortful control (EC), and children’s math achievement (N = 291; M age in fall of kindergarten = 5.66 years, SD = .39 year) across kindergarten through second grade. Parents reported their reactions and children’s EC. Math achievement was assessed with a standardized achievement test. First-grade EC mediated the relation between parents’ reactions at kindergarten and second-grade math achievement, beyond stability in constructs across study years. Panel mediation model results suggested that socialization of EC may be one method of promoting math achievement in early school; however, when all omitted time-invariant covariates of EC and math achievement were controlled, first-grade EC no longer predicted second-grade math achievement.

When children show high levels of academic achievement during childhood, it typically augurs well for healthy and productive functioning later in life (Duncan et al., 2007; Fiscella & Kitzman, 2009). In contrast, underachievers are at increased risk for delinquency, dropping out of high school, criminal activity, and chronic joblessness (Alexander, Entwisle, & Horsey, 1997; Bureau of Labor Statistics, 2010). Despite some evidence of a general decline in the rate of U.S. students who drop out of school and continued high levels of expenditures per student (Aud et al., 2010), U.S. students continue to lag behind peers in other industrialized nations in math and science performance (Mullis et al., 2008). An understanding of methods to promote math achievement during the first few years of elementary school, in particular, is essential given its relation to later academics.

This study was funded, in part, by a National Science Foundation CAREER Award BCS-0546096 to Carlos Valiente. This article is based on a dissertation submitted by Jodi Swanson to Arizona State University under the direction of Carlos Valiente and Robert H. Bradley. We thank committee member Becky Kochenderfer-Ladd for her helpful comments on previous versions of the manuscript. We are grateful to the principals, teachers, parents, and students of the Gilbert and Kyrene School Districts and the data collection staff members for their invaluable support of this research. Correspondence concerning this article should be addressed to Jodi Swanson, T. Denny Sanford School of Social and Family Dynamics, Arizona State University, Tempe, AZ 85287-3701. Electronic mail may be sent to [email protected].

Students’ attentional ability and capacity to exhibit self-control are critical for school success (National Institute of Child Health and Human Development Early Child Care Research Network [NICHD ECCRN], 2003). Effortful control (EC), an attentional, behavioral, and emotional-regulation component of temperament, is an aspect of self-regulation that is associated with facets of academic functioning (Blair, Calkins, & Kopp, 2010), including achievement, close relationships with teachers and schoolmates, and engagement in school (e.g., Blair & Razza, 2007; Deater-Deckard, Mullineaux, Petrill, & Thompson, 2009; Valiente, Lemery-Chalfant, Swanson, & Reiser, 2008). Consequently, more fully delineating how caregivers can promote the development of EC is important, with implications for educators and legislators (Blair et al., 2010). Moreover, fostering these skills during the early years of formal schooling when achievement motivation and affective connectedness to school are being established may jump-start positive academic trajectories. Although growing evidence suggests that parents can influence the development of EC and that EC is important for academic success, linkages between parenting processes and EC to academics remain incompletely explicated. The © 2014 The Authors Child Development © 2014 Society for Research in Child Development, Inc. All rights reserved. 0009-3920/2014/8505-0015 DOI: 10.1111/cdev.12260

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purpose of this investigation was to test whether an index of parental socialization in kindergarten predicted first graders’ EC, which was expected to be related to second-grade math achievement, using a three-wave longitudinal design in which all constructs were assessed at each measurement occasion. The use of a panel design extends the literature base that relies on mainly cross-sectional data by affording a stronger test of mediation (Cole & Maxwell, 2003) and by allowing for the simultaneous consideration of bidirectional effects during a critical 3-year developmental window. Parenting and Academic Achievement Empirical relations between the home environment and children’s academic achievement are well established. Distal features, such as family socioeconomic status (SES), parents’ level of education, and family configuration, have demonstrated fairly consistent relations with academic functioning (DavisKean, 2005), perhaps because they affect proximal processes in the home, such as parenting styles and practices (Martini, 1995). In fact, discrete parenting behaviors are associated with children’s achievement, often across age and beyond SES. In fact, positive parenting practices in later childhood predict the likelihood of high school graduation (Robertson & Reynolds, 2010). Children whose parents provide supportive, emotionally positive homes and refrain from using overly harsh, controlling practices appear to perform better academically (NICHD ECCRN, 2008). These relations likely exist because parents who respond to their children’s needs in developmentally and contextually appropriate ways enable children to act on their own proclivities and follow their curiosities using a style of behavior that is comfortable. Consequently, children develop a widening interest in learning and are inclined to engage in learning tasks more readily. Conversely, showing little regard for children’s perspectives or developmental needs tends to be linked with poor school performance. Much of the work to date demonstrates relations among adolescent students (Doyle & Markiewicz, 2005). The limited evidence among early elementary students indicates that achievement is lower for children whose parents do not set up opportunities for them to learn, explore, and act on their environments in pursuit of natural interests (Chen, Dong, & Zhou, 1997; Gadeyne, Ghesquiere, & Onghena, 2004). Parents who are predominantly controlling in parent–child interactions emphasize unquestioning

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compliance and compel children to enact parentpreferred behaviors. These practices likely weaken children’s personal sense of responsibility and discovery of knowledge, which can undermine learning and academic motivation (Gurland & Grolnick, 2005). Clearly, evidence suggests that responsive parenting is important for children’s academic success; however, observed relations between parent behaviors and achievement are generally modest, indicating that child- or school-level mediating mechanisms may explain why characteristics of the home environment influence academic outcomes (Swanson, Valiente, & Lemery-Chalfant, 2012). In fact, child-level constructs related to well-being, such as adaptive coping strategies and ego resilience, have mediated these relations (Kim, Brody, & Murry, 2003; Swanson, Valiente, Lemery-Chalfant, & O’Brien, 2011). In particular, EC is hypothesized to account for some of the association between parenting and achievement because consistently supportive parental behavior can foster children’s understanding of regulated responses important for academic success (Eisenberg, Cumberland, & Spinrad, 1998). Despite this promise, investigators have infrequently examined EC as a mediator linking parenting to academic functioning. The Mediating Role of EC EC integrates skills important for academic performance, including abilities to sustain attention and to plan, to shift attention, to manage emotions when engaged in a task, to inhibit inappropriate behavior in favor of achieving a goal, and to initiate and complete tasks proactively (see Eisenberg, Smith, & Spinrad, 2011), and a parent’s behavior toward a child has been related to the development of EC (Stormshak, Fosco, & Dishion, 2010). Eisenberg et al. (1998) theorized that from infancy forward, caregivers help shape the development of EC via socialization practices related to emotions and emotion regulation—practices termed emotionrelated socializing behaviors (ERSBs). Among the most critical of these ERSBs are parents’ reactionary behaviors when children display negative emotions. Some reactions validate a child’s experience or expression of negative emotions, such as telling a tearful child that crying is okay when he reports a cherished possession is broken or encouraging a child to talk about fearful feelings just before a recital (Grusec, 2011). By contrast, harsh reactions focus on the hierarchical relationship between caregiver and child: Here, the caregiver is an authority

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figure seeking to correct or alter the child’s behavior, a type of responding within the control domain of socialization (Grusec & Davidov, 2010). ERSBs that exhibit optimal arousal levels offer children opportunities to learn and experience appropriate engagement of EC skills because ERSBs imply the socializer’s message of what is and is not appropriate emotional expression and management for a particular context (Eisenberg et al., 1998). By reacting in a way that exhibits mastery over their own negative emotion, socializers model the ability to focus attention on longer term goals. For example, a mother whose son is crying loudly in a public park because his friends did not invite him to play can model EC skills by inhibiting the tendency to show frustration herself, focusing attention on comforting the child, and assisting with a solution. Associations are evident between parental behavior and EC as children transition into early school years: Maternal sensitivity prior to first grade predicted first graders’ EC (NICHD ECCRN, 2005). Likewise, authoritative parenting has predicted first and second graders’ concurrent EC (Zhou, Eisenberg, Wang, & Reiser, 2004). As it happens, the start of formal schooling coincides with rapid development of the executive attention neural network responsible for EC (Rothbart, Ellis, Rueda, & Posner, 2003). Thus, this period of life is likely particularly important for the socialization of skills pertaining to sustained attention and inhibition of inappropriate emotions and behaviors across settings. Nonetheless, little is known about how parenting is implicated in the continued development of EC across early schooling. This study extends work on the socialization of EC by including cross-lag paths between parents’ reactions and children’s EC to explore the direction of effects from kindergarten through second grade. Although most investigators posit that parenting practices influence EC, a well-regulated child may elicit sensitivity and nurturing from a caregiver (or a poorly regulated child may elicit hostility and control); thus, child-driven effects should also be tested. The majority of findings support parentdriven models (e.g., Hofer, Eisenberg, & Reiser, 2010; Valiente et al., 2006); however, toddlers’ EC has predicted mothers’ cognitive assistance and directive teaching strategies (Eisenberg et al., 2010). In addition, indices of regulation have sometimes predicted parents’ reactions across middle elementary school grades, accounting for paths in both directions across multiple data waves (Eisenberg et al., 1999).

Predicting Academic Achievement From EC Children’s EC in preschool and elementary school is important for academic success during elementary school. EC has been related to achievement and social skills in pre-K and across the transition to kindergarten (Blair & Razza, 2007; Fabes, Martin, Hanish, Anders, & Madden-Dierdich, 2003). In addition, EC has predicted reading and math achievement in elementary school, controlling for previous performance, in numerous studies conducted in the United States and China (e.g., DeaterDeckard et al., 2009; Duncan et al., 2007; Rudasill, Gallagher, & White, 2010; Valiente et al., 2008; Zhou, Main, & Wang, 2010). A few studies also show that gains in EC-related skills predict achievement at school entry (e.g., Bierman, Nix, Greenberg, Blair, & Domitrovich, 2008). In addition, EC has mediated relations between parenting and social functioning outcomes in diverse samples of elementary school children and adolescents (e.g., Eisenberg et al., 2005; Hofer et al., 2010), but only rarely has the mediating role of EC between parenting (or the broader home environment) and achievement been considered (for exceptions, see Kim et al., 2003; NICHD ECCRN, 2003; Swanson et al., 2012). Self-regulation skills and academic achievement are expected to be mutually reinforcing, perhaps because self-regulated learning increases children’s perceptions of the control they can exert over their academic success (Blair et al., 2010). Executive functioning ability, which involves abilities that overlap with EC (Zhou, Chen, & Main, 2012), appears instrumentally important for emerging mathematics skills such as problem solving and calculations (Blair, Knipe, & Gamson, 2008; Espy et al., 2004), perhaps because working through math problems is associated with activation of the prefrontal cortex, where EC is neurologically housed (Fulbright et al., 2000; Rothbart et al., 2003). High achievement may improve EC because students who consistently perform well academically are motivated to focus on learning or to manage negative emotions in the classroom to maximize benefits from school experiences. Given that EC and mathematics competence are expected to develop rapidly across the early school years (Rothbart et al., 2003), an understanding of the interplay between these capacities during these years is needed. Delineating reciprocal relations between environmental conditions, EC, and academic processes is particularly worthwhile, as there have been no studies of such relations among early elementary school children to our knowledge. Math-related skills and EC skills are likely to

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influence each other markedly during this time. Cognitive capacities associated with the promotion of achievement are expected to foster the development of EC, particularly in early childhood (Blair et al., 2010), perhaps because focusing attention and inhibiting distraction are central components of both academic performance and EC. Modeling longitudinal bidirectional paths may elucidate directionality regarding EC and achievement in young children. The Present Study We investigated prospective relations between parents’ reactions to their children’s displays of negative emotions, EC, and math achievement using a three-wave path analysis mediation model, which was estimated according to procedures outlined by Cole and Maxwell (2003) and MacKinnon (2008). The primary aim was to test the hypothesis that first-grade (Grade 1 [G1]) EC mediates the relation between parents’ kindergarten (K) reactions and second-grade (Grade 2 [G2]) math achievement. A secondary aim was to examine directionality during an important developmental window. The start of formal schooling represents a time frame during which children’s emergent EC abilities and mathematics understanding are undergoing significant developmental changes; in addition, the early years of school represent a qualitative environmental shift (Sameroff, 1996). The use of a panel design extends previous work by simultaneously testing bidirectional effects while controlling for construct stabilities, offering an optimal means of testing mediation and stepping closer to ruling out alternative arguments of directionality. Finally, to test whether relations were robust when controlling for the influences of all time-invariant variables unmeasured in the model and for between-person variability, we reestimated the panel model as a fixed-effects model. These models overcome the limitation that parameter estimates may be biased by omitted variables and offer a more stringent test of our research questions (Allison, 2005; Bollen & Brand, 2010). Sex or SES may influence EC or academic performance. Sex differences in EC are well documented across early childhood (Else-Quest, Hyde, Goldsmith, & Van Hulle, 2006). Girls tend to outperform boys on observable assessments of EC and are rated higher than boys in EC on self and other reports, but findings are not consistent (Li-Grining, 2007). Girls also typically score higher than boys across academic subjects in early grades (Halpern & LaMay, 2000), but not always (Spelke & Grace, 2007). Despite mean differences, processes linking parenting

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to EC or EC to achievement have been shown to operate similarly for girls and boys (Swanson et al., 2012; Valiente et al., 2008; Zhou et al., 2010). Likewise, although family income and parents’ education are related to achievement (Davis-Kean, 2005; NICHD ECCRN, 2005), whether the pattern of relations between variables is typically similar across SES levels remains unclear. Thus, although we did not expect the pattern of findings to differ across groups, we computed Box’s Ms (Winer, 1971) to test for potential moderation of relations by sex or SES.

Method Participants We recruited 291 elementary school children (46% girls) from regular education kindergarten classrooms in public schools in greater Phoenix, Arizona, along with their parents. Parents provided consent and children provided assent. Children were 5.66 years old (SD = .39 year), on average, at recruitment. Parents’ reports of sex and race or ethnicity showed that the sample represented the classroom populations from which participants were recruited. Forty percent of all eligible kindergartners were girls. At K, 75% of participants were White (kindergarten classroom population percentages are in parentheses; 70%), 14% (17%) were Latino/a, 8% (8%) were Asian American, 3% (4%) were Black, and < 1% (2%) were American Indian. Most children resided in two-parent homes (89% of homes at K, 90% at G1, 89% at G2) of middle- to upper-middle income (M annual family income ranges = $70,000–$80,000 at K and $80,000–$90,000 at G1 and G2; range = below $10,000 to above $100,000). Parents reported that the primary caregiver was the child’s mother (95%, 91%, and 90%) who completed all questionnaires. Almost all caregivers had attained a 2-year college degree or higher (94%, 97%, and 96% of primary caregivers and 90%, 89%, and 92% of secondary caregivers). Parental educational attainment ranged from less than a high school diploma (< 2% of the sample) to some graduate school attendance (14% of the sample). Census figures for the county from which participants were sampled show that the participant sample was more affluent and better educated than the resident population within the metropolitan county at large, but membership in major racial/ethnic groups was similar: Median household income in county = $53,284, percentage of high school graduates aged 25 + in county = 84%, and percentage of White persons in county = 73%.

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Procedure All families of incoming kindergarten children received an introductory letter describing the study. During kindergarten orientation, research assistants presented the study to parents and enrolled them in the study. Of the 291 reporting parents who provided consent for themselves and their children, 85% (n = 248) submitted questionnaires via postal mail at K, 79% (n = 229) at G1, and 73% (n = 213) at G2. We obtained data on achievement assessments for 99% (n = 289) of children in the study at K, 96% (n = 278) at G1, and 92% (n = 267) at G2. Five families dropped out of the study between recruitment and the start of G1. An additional eight dropped between G1 and G2 (total dropped n = 13). According to results from nonparametric Kolmogorov–Smirnov tests, which are robust against unequal group sizes and test whether two groups have significantly different measures of central tendency and distributions (Magel & Wibowo, 1997), children who dropped after the initial wave did not significantly differ from those retained on SES, sex, ethnicity, or the K variables in the model. Parents completed questionnaires in the fall of each academic year. We requested that the same parent answer surveys at each assessment, if possible. Parents’ questionnaires assessed family demographics, their reactions to children’s negative emotions, and children’s EC. In the spring of each year, research assistants administered standardized math achievement tests to children during the school day. Parents received a modest monetary compensation per questionnaire completed. Each child received a small toy at each assessment.

Measures Family Demographics In fall of each year, parents reported primary and secondary caregivers’ highest educational attainment and annual household income. These were correlated, rs(within-time dfs 187–240) > .29, p < .01 so we created a mean composite of the z scores as an index of SES at K, G1, and G2. Parents also reported child’s sex and birth date. Parents’ Reactions to Children’s Negative Emotions In fall of each year, the reporting parent completed the Coping with the Children’s Negative

Emotions Scale (CCNES). The CCNES is composed of 11 scenarios, each of which depicts a typical situation that young children experience that evokes distress and negative affect (e.g., crying after losing a prized possession; Fabes, Poulin, Eisenberg, & Madden-Derdich, 2002). For each item, respondents rate the nature of their reactions during circumstances in which children displayed a particular negative emotion. Parents reported the likelihood of their emotion-focused (eight items; “I would encourage my child to talk about his/her feelings”; as = .71, .75, and .75 for K, G1, and G2, respectively), expressive-encouragement (seven items; “I would tell him/her it’s okay to cry when you feel unhappy”; as = .79, .79, and .80), minimization (eight items; “I would tell my child not to make a big deal out of it”; as = .75, .83, and .78), problemfocused (eight items; “I would talk to my child about ways to make it hurt less”; as = .72, .75, and .76), and punitive (eight items; “I would tell him/ her to shape up or he/she won’t be allowed to do something he/she likes to do [e.g., watch TV]”; as = .80, .84, and .86) reactions on a 7-point Likert scale (1 = very unlikely to 7 = very likely). Across samples, the scale is internally reliable and demonstrates satisfactory test–retest reliability, construct validity, and predictive validity to facets of children’s emotional competence (Fabes et al., 2002). We created a mean composite of positive reactions from the correlated scale scores comprising the emotion-focused, expressive-encouragement, and problem-focused reactions. We created a mean composite of negative reactions from the correlated scale scores comprising minimization and punitive reactions. Positive and negative reactions composites were moderately negatively correlated within and across grades (rs ranged .18 to .29, ps < .01). We computed an affective balance composite by subtracting the negative responses score from the positive responses score at each age. The composite was normally distributed (see Table 1) and indicates net positivity in responding after removing negative responding. Our creation of such an index follows a precedent for capturing higher order emotion-related socialization phenomena (Denham, Mitchell-Copeland, Strandber, Auerbach, & Blair, 1997; Valiente et al., 2006). The affective balance of response positivity is expected to be more important for children’s developing EC (and subsequent achievement)—and more closely represents the reality that parents react positively and negatively across contexts, and it is the net positivity that matters—than utilizing either positive responses or

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Table 1 Descriptive Statistics and Correlations

1. Positive reactions (K) 2. Positive reactions (G1) 3. Positive reactions (G2) 4. EC (K) 5. EC (G1) 6. EC (G2) 7. Math achievement (K) 8. Math achievement (G1) 9. Math achievement (G2) M SD Minimum value Maximum value Skewness Kurtosis

1

2

3

4

5

6

7

8

9

.70** .71** .04 .11* .05 .08† .10* .10† 3.82 1.09 .10 5.82 .54 .05

.77** .01 .14* .05 .13† .16* .13** 3.67 1.23 .16 5.79 .58 .23

.02 .09 .08 .14* .17* .15** 3.59 1.19 .36 5.83 .67 .47

.78** .76** .22** .25** .27** 4.86 .72 2.53 6.30 .56 .19

.78** .23** .29** .32** 4.91 .69 3.09 6.61 .31 .24

.22** .26** .31** 4.97 .69 3.03 6.43 .41 .15

.71** .63** 451.82 16.94 388.00 498.00 .16 .29

.76** 471.85 16.84 411.00 518.00 .20 .10

490.14 16.45 436.00 526.00 .46 .34

Note. Parents’ positive reactions is a difference score (i.e., positive minus negative reactions). Values represent results from multilevel analyses accounting for missing values and school-level clustering. EC = effortful control; G1 = Grade 1; G2 = Grade 2; K = kindergarten. † p < .10. *p < .05. **p < .01.

negative responses alone in statistical models. These net positive composites are hereafter referred to as K, G1, or G2 parents’ positive reactions.

interval-scaled ability measures unique to the WJ tests, similar to standardized scores. Analytic Strategy

Effortful Control In fall of each year, parents reported on children’s EC using all items from the attention focusing (e.g., “This child, when picking up toys or doing other jobs, usually keeps at the task until it’s done”) and inhibitory control (e.g., “This child can wait before entering into new activities if she or he is asked to”) scales of the Children’s Behavior Questionnaire (CBQ; Rothbart, Ahadi, Hershey, & Fisher, 2001) on a 7-point Likert scale (1 = extremely false to 7 = extremely true). As with previous reports of the CBQ and because scale mean composite scores were significantly correlated (across-scale, within-grade rs > .52, ps < .01), we created mean composites for K, G1, or G2 parent-reported EC (as = .87, .87, and .86, respectively). Math Achievement In spring of each year, children completed the Applied Problems subtest from the Woodcock–Johnson III Tests of Achievement (WJ–III; Woodcock, McGrew, & Mather, 2000), which assesses analytical and practical mathematical problem-solving skills. With WJ–III computerized scoring technology, we converted children’s raw scores to W scores,

Prior to hypothesis testing, we conducted a series of preliminary analyses to test for sex and age differences and for significant relations among study variables. We estimated all models testing hypotheses in Mplus 7.11 (Muthen & Muthen, 1998–2012). Because complete data were not available for all participants across grades, we estimated models using maximum likelihood robust (MLR) in Mplus, which estimates parameters directly via a full information maximum likelihood procedure. Furthermore, the data were clustered (i.e., students clustered within schools across grades). Clustered data can introduce dependency in the data that may bias standard errors and significance test results. Therefore, we estimated models using the “type = complex” option in Mplus, which adjusts the standard errors of parameter estimates and the chi-square tests of model fit for nonindependence of observations. We created a cross-grade school-cluster variable that identified the possible clusters that would arise from children attending the same schools across grades. We first identified the school(s) each child attended in K, G1, and G2, and then we grouped school attendance across grades, and utilized this variable as the clustering variable in all models (M cluster size = 3.64 students, SD = 6.95,

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range = 1–31 students per school cluster among 80 possible clusters; i.e., 31 students in our sample attended K, G1, and G2 together). To test the hypothesis that EC mediates the relation between reactions and math achievement, we used specifications by Cole and Maxwell (2003) and MacKinnon (2008) regarding how best to test panel mediation models with longitudinal data. To test the significance for the indirect effect of interest, we obtained confidence intervals via the statistical program RMediation, a program that produces confidence intervals based on the distribution of the product method of mediation (Tofighi & MacKinnon, 2011). Confidence intervals provide a range of possible values for the mediated effect and demonstrate the variability in the effect size. In addition, utilizing confidence intervals, compared to normal theory tests of mediation, reduces Type I error, increases statistical power, and produces more accurate confidence limits (MacKinnon, Lockwood, & Williams, 2004). Finally, RMediation accounts for the possibility of nonnormal distribution of the mediated effect (Tofighi & MacKinnon, 2011). A limitation of panel models is that parameter estimates may be biased by omitted variables. For example, parents who provide supportive reactions to negative emotions may also provide other supports unmeasured in the model that specifically pertain to academics, such as working directly with the child on math homework or emphasizing the importance of performing well. That is, parents’ general baseline warmth or supportive tendencies could account for findings attributed to more discrete parenting behaviors measured here. In addition, other omitted environmental variables associated with broader social- or community-level circumstances in which our constructs were assessed may affect relations between study variables. Fixed-effects models help address this limitation. Allison (2005) has developed methods of translating the traditional econometric fixed-effects modeling technique to the SEM framework, and these methods were further extended recently by Bollen and Brand (2010). Fixed-effects models in the SEM framework include a latent variable that is indicated by the outcome variable at each time point with factor loadings fixed at 1. The latent variable represents all omitted time-invariant variables that relate to individual differences in outcome variables (Allison, 2005; Bollen & Brand, 2010). When implemented in SEM, the models control for “all unmeasured (or latent) time-invariant variables that

influence the dependent variable whether these variables are known or unknown” (Bollen & Brand, 2010, p. 1; emphasis added). Specifically, models control for all time-invariant variables that are related to between-person variability in the outcome variables, and they control for the unmeasured variables’ associations with predictor variables (Bollen & Brand, 2010). The models do so through the use of a random intercept that “represents individual heterogeneity that affects the outcome variable” (Bollen & Brand, 2010, p. 4), and through covariances estimated between the random effect and predictors. Given that these fixed-effects SEM techniques are relatively new, they have not often been utilized in the developmental literature, despite their methodological strengths (see Spinrad et al., 2012, for an exception). In the context of our study, then, this modeling approach accounts for between-child variability in EC, for between-child variability in math achievement scores, for the relation between those between-person differences, for the relation between between-person differences in EC or math achievement and parents’ positive reactions, and for associations between (potentially unknown) unmeasured variables’ relations with parents’ positive reactions or with children’s EC. That is, utilizing a fixedeffects modeling approach in the SEM framework allowed for a stronger test of our research questions, which were focused upon within-person differences because these models control for between-person variability in outcomes (Allison, 2005). Thus, we sought to reestimate our hypothesized model as a fixed-effects model to determine whether relations held after controlling for all omitted time-invariant variables. Finally, we computed Box’s Ms to test whether model relations differed for girls versus boys or for families of higher versus lower SES.

Results Preliminary and Zero-Order Correlation Analyses We first examined the study variables for sex and age differences and then for relations with family SES within grade. To test for sex and age differences, we computed multilevel random intercept models (i.e., means-as-outcomes models) with SPSS mixed. Specifically, we ran individual models predicting parents’ reactions, EC, or math achievement at each grade from child’s sex, and then we repeated this process with age as the predictor. Models were clustered by school within grade, as appropriate for the

Parenting, Effortful Control, and Achievement

given dependent variable (e.g., we accounted for K school clustering for models predicting parents’ reactions at K from sex or age). The fixed effect for sex was significant only when predicting EC: Parents rated girls significantly higher in EC than boys at K, c01(243.52) = .26, p < .01, 95% CI [ .43, .08], girls’/boys’ Ms = 2.91/2.53; at G1, c01(227) = .36, p < .01, 95% CI [ .54, .18], girls’/boys’ Ms = 3.29/ 3.09; and at G2, c01(211) = .33, p < .01, 95% CI [ .51, .14], girls’/boys’ Ms = 3.18/3.03. The fixed effect for age was significant only in the model predicting K positive reactions: Parents used significantly lower levels of positive reactions with their older kindergartners, c01(242.46) = .04, p = .02, 95% CI [ .07, .005], younger students’/older students’ Ms = 3.92/3.64 (age based on a median split). At K, family SES was positively associated with parent-reported EC and math achievement, rs(245 and 244) = .17 and .35, ps < .05). At G1, family SES was related to parents’ reports of EC and math achievement, rs(227 and 218) = .19 and .31, ps < .01. At G2, SES was related to math achievement, r(201) = .28, p < .01. Table 1 contains descriptive statistics and zeroorder correlations for primary study variables. Across grades, all constructs exhibited within-construct stability. When mediation is present, the predictor must be significantly related to the mediator, and the mediator must be significantly related to the outcome with the predictor in the model (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). In support of these specifications, K positive reactions were positively related to G1 EC. In turn, G1 EC was positively associated with G2 achievement. As anticipated, at each grade, significant positive relations were apparent between EC and all achievement measures. This pattern of correlations provided initial support to test the hypothesized process of mediation. Hypothesized Panel Models First, we estimated a model with autoregressive paths and with cross-lagged paths between parents’ reactions and EC and between EC and math achievement. We included covariances between corresponding within-source residual terms across grade as a method of accounting for potential biases due to shared-method variance, and all models included within-grade covariances of constructs and their disturbances (Cole & Maxwell, 2003). For example, we estimated intercovariances between K positive reactions, K EC, and K math achievement. Finally, to account for the effect of the association

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between the predictor and outcome variable after accounting for the mediator (i.e., the direct effect), we included a direct path between parents’ K positive reactions and G2 achievement (see MacKinnon, 2008). Modification indices suggested that lag 2 autoregressive paths would improve model fit. Thus, we reestimated the model inclusive of autoregressive paths from K to G2. The full model (see Figure 1) fit the data well, v2(N = 290, df = 9, MLR scaling correction factor = .80) = 7.08, p = .63; comparative fit index (CFI) = 1.00, root mean square error of approximation (RMSEA) = .00, standardized root mean square residual (SRMR) = .03. All lag 1 and lag 2 autoregressive paths were significant. In line with hypothesized mediation, K positive reactions were positively associated with G1 EC, and in turn, G1 EC was positively related to G2 math achievement. In addition, K math achievement was positively related to G1 EC. The path from K EC to G1 math achievement approached significance (p = .07). Other estimated paths were not significant. The 99% confidence limits obtained from RMediation for the indirect effect of interest did not include zero (lower confidence limit = .02, upper confidence limit = .39); thus, G1 EC mediated relations between positive reactions at K and G2 math achievement. Prior to computing fixed-effects models, we reduced the complexity of the full model. The K, G1, and G2 math achievement scores were rescaled by dividing by 100 to aid model convergence. We identified a reduced, more parsimonious baseline model (the reduced model), which did not significantly differ in model fit from the full model according to chi-square difference testing (adjusted for MLR estimation [http://www.statmodel.com/ chidiff.shtml]), v2D(4) = 4.40, p = .36. Specifically, we estimated a model which differed from the full model only slightly: Based on our hypotheses regarding the direction of effects and evidence obtained from the full model, we did not estimate cross-lagged paths from EC to parents’ reactions, or from achievement to EC. The reduced model fit the data well, v2(N = 290, df = 13, MLR scaling correction factor = .86) = 11.67, p = .86; CFI = 1.00, RMSEA = .00, SRMR = .07. All paths in this model were positive and significant, with these exceptions: G1 positive reactions did not predict G2 EC, K EC marginally predicted G1 math achievement (as in the full model; p = .06), and K reactions did not predict G2 math achievement. Proceeding with the reduced model allowed us to estimate the complex fixed-effects models with fewer constraints imposed on the estimates for identification.

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Swanson, Valiente, Lemery-Chalfant, Bradley, and Eggum-Wilkens First Grade

Kindergarten .80** (.70)

Positive Reactions

Positive Reactions R2 = .49

.06** (.10)

.07** (.17)

.17 (.03)

Effortful Control

.74** (.75)

2.36† (.10)

**

2.69 (.22)

.68** (.68)

.18** (.18)

Positive Reactions R2 = .65

.04* (.14) -.04 (-.02)

Effortful Control R2 = .61

-.08 (-.08)

*

.75 (.15)

.48** (.48)

Effortful Control R2 = .67

-.20 (-.03)

2.55** (.11)

.36** (.37)

.10 (.10)

.003* (.08)

Math Achievement

-.01 (-.01)

.36** (.33)

-.04 (-.02) -.09† (-.10)

Second Grade .52** (.55)

.001 (.03)

-.51 (-.04)

Math Achievement R2 = .51

.58** (.60)

Math Achievement R2 = .61

Figure 1. Panel mediation model depicting longitudinal relations between parents’ positive reactions and children’s effortful control and math achievement (the full model). This model fit the data well, v2(N = 290, df = 9, MLR scaling correction factor = .80) = 7.08, p = .63; comparative fit index (CFI) = 1.00, root mean square error of approximation (RMSEA) = .00, standardized root mean square residual (SRMR) = .03. Solid lines represent significant relations, and dashed lines represent nonsignificant relations. Bolded lines represent significant cross-lag paths of interest. Unstandardized estimates are above the standardized estimates, which are in parentheses. † p = .07. *p < .05. **p < .01.

Fixed-Effects Models For the autoregressive fixed-effects model, we modified the reduced model in several ways. When autoregressive paths are included in the model, the traditional fixed-effects model specification must be modified; for example, the latent variable is indicated by the outcome variable at each time point following Time 1 (i.e., G1 and G2 in our study), and the outcome variable at Time 1 is considered predetermined (Bollen & Brand, 2010). Our hypothesized model included autoregressive paths; thus, parents’ positive reactions, EC, and math achievement at G1 and G2 were endogenous variables. Therefore, a latent variable was formed for each of our three constructs of interest. Exogenous covariances were freely estimated. Thus, the three latent variables, as well as K positive reactions, K EC, and K math achievement were allowed to covary. A number of constraints are imposed on the model for model identification purposes (Bollen & Brand, 2010); in our model, the lag 1 autoregressive paths for each variable were constrained to be equal (three constraints). In addition, residual variances for each variable at G1 and G2 were constrained to be equal (three constraints).

The autoregressive fixed-effects model fit the data well, which was not surprising because the model was quite saturated, v2(N = 290, df = 4, MLR scaling correction factor = .75) = .68, p = .95; RMSEA = .00, CFI = 1.00, SRMR = .01. None of the lag 1 or lag 2 autoregressive paths were statistically significant (ps > .15); furthermore, Allison (2005) has suggested that controlling for autoregressive paths often is not necessary in fixedeffects models. Thus, we respecified the model without autoregressive paths toward a more parsimonious model. Omitting the autoregressive paths allowed for a more traditional estimation of the fixed-effects model. In the final fixed-effects model (see Figure 2), G1 and G2 positive reactions were no longer endogenous; thus, positive reactions did not require a latent variable. In addition, we no longer needed to model EC or math achievement at K as predetermined. As a result, the latent variables for EC and for math achievement were indicated by their respective variables at K, G1, and G2, and the factor loadings were fixed at 1. The two latent variables were allowed to covary with one another, as well as with positive reactions at each grade. Positive reactions variables were also allowed to

Parenting, Effortful Control, and Achievement

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.91** (.70) 1.15** (.77)

.96** (.70)

K Positive Reactions

G1 Positive Reactions

G2 Positive Reactions

.04* (.07)

K Effortful Control R2 = .77

.002 (.003)

G2 Effortful Control R2 = .77

G1 Effortful Control R2 = .78 1

1

1

Effortful Control .03** (.33)

.01 (.03)

.02 (.06)

Math Achievement 1

K Math Achievement R2 = .70

1

1

G2 Math Achievement R2 = .71

G1 Math Achievement R2 = .70 -.001 (-.01)

Figure 2. Fixed-effects model depicting longitudinal relations between parents’ positive reactions and children’s effortful control and math achievement (the final fixed-effects model). This model fit the data well on most indices, v2(N = 290, df = 23, MLR scaling correction factor = .99) = 35.03, p = .99; comparative fit index (CFI) = .99, root mean square error of approximation (RMSEA) = .04, standardized root mean square residual (SRMR) = .17. Solid lines represent significant relations, and dashed lines represent nonsignificant relations. Bolded lines represent significant cross-lag paths of interest. Unstandardized estimates are above the standardized estimates, which are in parentheses. Covariances were estimated between the effortful control latent variable and positive reactions at each time point (none were significant), as well as between the math achievement latent variable and positive reactions at each time point (negative and marginal at K, and negative and significant at G1 and G2), but are not depicted for clarity. G1 = Grade 1; G2 = Grade 2; K = kindergarten. *p < .05. **p < .01.

covary across grades. The residual variances of EC and math achievement were constrained equal across grades (two constraints). The final fixed-effects model fit the data well according to most fit indices with the exception of the SRMR, v2(N = 290, df = 23, MLR scaling correction factor = .99) = 35.03, p = .052; RMSEA = .04, CFI = .99, SRMR = .17. The final fixed-effects model was not nested within the autoregressive fixedeffects model; hence, a chi-square difference test was not computed. Parents’ positive reactions at K positively predicted G1 EC, but not from G1 to G2. EC did not predict later math achievement at any grade. The direct effect remained nonsignificant: Positive reactions at K did not predict G2 math achievement. Thus, all the relations in the final fixed-effects model were consistent with the reduced model with one exception: The positive relation between G1 EC and G2 math achievement was no longer significant when controlling for all

omitted time-invariant variables producing individual differences in EC and math achievement and their relations with parents’ positive reactions. Tests of Moderation We computed Box’s Ms to examine whether model relations differed by child’s sex or by family SES (based on a median split). A nonsignificant Box’s M result indicates that covariance/variance matrices are similar across groups, but a significant Box’s M warrants follow-up tests to probe the possibility of moderation of paths. Results suggested that relations between variables were similar for girls and boys, F(78, 65802) = 1.04, p = .39, and for families of lower and higher SES at K, G1, and G2, Fs(78, 78014; 78, 82961; and 78, 88720) = .92, .99, and .77, ps = .67, .49, and .94. The nonsignificance of these tests indicates that model paths do not significantly differ across groups.

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Swanson, Valiente, Lemery-Chalfant, Bradley, and Eggum-Wilkens

Discussion Early academic achievement has long been considered important for adult well-being. The fact that U.S. students continue to trail behind students in many other industrialized nations in mathematics performance (Mullis et al., 2008) has prompted researchers, educators, and legislators to identify characteristics and environmental factors that foster or hinder the development of mathematical competence. We utilized three waves of longitudinal data across K, G1, and G2 to test mediated relations between parents’ positive reactions to their children’s displays of negative emotions, EC, and math achievement in panel mediation models and fixedeffects models. We also examined the possibility of bidirectional relations between positive reactions and EC or between EC and achievement. We offer new evidence that G1 EC mediated relations between parents’ positive reactions at K and G2 math achievement in the full model, but G1 EC no longer predicted G2 math achievement when all measured and unmeasured time-invariant variables were controlled in the final fixed-effects model. Bidirectional paths showed that parents’ positive reactions were not predicted from EC, but K achievement predicted G1 EC. Consistent with expectations, we found preliminary evidence that G1 EC mediated relations between parents’ positive reactions at K and math achievement at G2. Supportive parenting practices generally, and ERSBs specifically, have been linked to EC among older students (e.g., Eisenberg et al., 1999; Valiente et al., 2006), and there is growing evidence that EC is predictive of school success. We provide an important extension of previous work. All constructs were assessed at each of three data waves, and models allowed for cross-lag predictions from parents’ reactions to EC (and vice versa) and from EC to achievement (and vice versa), according to recommendations set forth by Cole and Maxwell (2003) and MacKinnon (2008) for testing mediation longitudinally. This methodology permits tests of a specific directional mediation hypothesis and of alternative directions of effects. The nonsignificant direct path from parents’ reactions at K to achievement at G2 indicates that EC fully mediated the relation from positive reactions to math achievement across K–G2. Critically, the absence of significant paths from EC to parental reactions and the absence of a significant path from G1 math achievement to G2 EC offer support for the hypothesized direction of effects.

Findings support the premise that parenting involving emotionally supportive practices is associated with children’s EC during early childhood. Whereas previous work has examined parents’ general emotional expressivity, we tapped parents’ reactions in the context of children’s expressions of negative emotions specifically. In line with expectations, parental reactions at K were associated with G1 EC, although G1 parental reactions were not related to G2 EC. This finding extends previous links between ERSBs and older children’s EC (Valiente et al., 2006). Notably, the relation between parents’ positive reactions at K and G1 EC remained significant in the final fixed-effects model, which controlled for all unmeasured time-invariant variables and between-person sources of variance. The fact that this relation remained robust beyond these strict controls suggests that parents’ responses to their young children’s negative emotional displays has a profound effect on EC at the start of school, and that EC is malleable to this socialization behavior. Indeed, positive emotion-related responding may be a prime target for prevention and intervention efforts for children showing signs of problems with attention or self-regulation. Results from this study represent an important step forward in clarifying the nature of relations among these constructs and the stability of EC during this developmental period. A caveat to this is that the zero-order correlations suggested there may be a shifting pattern of relations between how parents respond to children’s displays of negative emotion across the early years of school. As children age and require less self-regulatory guidance, parental expectations regarding displays of negativity may change, and as more children acquire higher levels of EC, there may be less room for parents to exert consequential influence. The lack of a significant positive link at the second lag between parents’ positive reactions and EC in the zero-order correlations and both the full and final fixed-effects models suggests that efforts to promote children’s developing EC may be especially beneficial at school transition and entry. EC develops rapidly during this period (Rothbart et al., 2003); thus, it may be particularly amenable to parental influence. Supporting this idea, much of the prior research linking parental behaviors to EC has been conducted with samples of toddlers and preschoolers (Li-Grining, 2007). As the development of EC slows down and children are exposed to the different affordances present in the classroom, the power of parental behavior to shape the course of EC may abate. This study provides important evi-

Parenting, Effortful Control, and Achievement

dence to suggest that parents’ reactions may foster EC at the start of formal schooling and in line with experimental evidence that interventions affect individual differences in EC-related abilities (e.g., Bierman et al., 2008). Integrating varied parenting practices or comparing practices across caregivers as children transition to the classroom context may elucidate under what circumstances and which socializers promote EC, particularly given that early student–teacher interactions can have long-reaching social and academic consequences (Curby, RimmKaufman, & Ponitz, 2009; O’Connor, Dearing, & Collins, 2011). Like the majority of investigations of links between parental behavior and EC, there was no evidence that EC predicted positive reactions 1 year later. Furthermore, positive reactions and EC were not related between G1 and G2. Zero-order correlations supported the pattern of significant findings in the full model, suggesting that over time, relations between positive reactions and EC weaken. Across this 3-year period, children may require direct guidance and emotion-regulation modeling less frequently from their parents: Children and parents may jointly perceive that by school age, children are increasingly responsible for enacting self-regulatory processes autonomously. It is also possible that tests of relations between constructs with lags of 1 year or more dilute the influence of day-to-day and context-specific parent–child interactions. As suggested earlier, by G2, children have considerably more experience with the classroom context and social ecology—important spheres of influence not measured in this study—and G2 teachers likely have higher expectations than previous teachers for children’s exhibited EC. As a result, children receive sources of socialization for EC outside the home, even as their EC abilities are developing further, and parents’ reactions may play a less central role. Shortening the window between assessments and incorporating school-level mechanisms may offer insights regarding precisely when and why parental behaviors foster EC. Findings partly replicate work that children’s EC is associated with math performance. A growing literature has highlighted the contributions of EC and related abilities to emergent mathematics skills (Blair et al., 2008; Espy et al., 2004), perhaps because of neurological linkages. In neuroimaging studies conducted with adults, mathematical ability is related to activation of the prefrontal cortex, the location of the executive attention network (Fulbright et al., 2000). Whether EC is similarly associated with emergent achievement skills in other

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domains (e.g., reading comprehension capacities, science reasoning, written composition) is an area for additional investigation. Early mastery of regulatory skills and early achievement are theorized to be mutually reinforcing (Blair et al., 2010), yet empirical tests of reciprocal relations are few (Poehlmann et al., 2010), and have not previously been tested in elementary school. We show that cross-grade associations existed between EC and math achievement at K and G1 in both directions in the full model. In fact, math achievement at K predicted G1 EC, which in turn was positively associated with G2 math achievement, supporting the premise that transactional relations may exist: Blair et al. (2010) posited that the regulating self (Bandura, 2001) influences the development of processes associated with achievement (e.g., memory, inhibitory control, content knowledge, motivation) as a function of feedback from the environment. Heightened attention to methods of testing reciprocal relations between social-emotional competence and academic functioning would benefit this line of research. Given the growing literature base supporting relations between EC—or more broadly, self-regulation, including executive functioning skills and inhibitory control capacities—and academic competence, it was surprising that the significant relation between G1 EC and G2 math achievement in the full model was no longer significant in the final fixed-effects model. Recall that the final fixed-effects model accounted for all omitted time-invariant variables related to EC and math achievement and their associations with parents’ positive reactions. It may be that, in fact, the relation linking EC to math achievement is partly due to confounding with at least one other variable. It is possible that a higher order process or set of processes is important for observing both high levels of EC and high levels of math performance. For example, cognitive efficiency, which has also been described as mental, neurological, instructional, learning, or processing efficiency, involves optimal cognitive processing (Hoffman, 2012), and seems important for both the manifestation of EC skills and achieving in math. The anterior cingulate gyrus system (i.e., the executive attention network), located in the midprefrontal cortex and responsible for attention to spatial and semantic information (Derryberry & Rothbart, 1997) and abilities to detect errors and plan (Rothbart, 2007), appears to be the site at which the brain exerts control over information processing and houses information to be processed (Posner & Raichle, 1994). EC reflects the output of individual differences in this system. Perhaps some of the

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Swanson, Valiente, Lemery-Chalfant, Bradley, and Eggum-Wilkens

observed relation between EC and math achievement reflects the efficiency with which individuals process information and learn, important for both the observable manifestation of EC-related skills and for high performance on a math achievement test. In addition, other constructs in the school environment context important for academic functioning have mediated the relation between EC and achievement, such as academic relationships with the teacher and peers or facets of academic engagement in school (Liew, Chen, & Hughes, 2010; Swanson et al., 2012; Valiente et al., 2008), and these unmeasured, timevarying constructs may have partly accounted for why the relation was lost in the final fixed-effects model with this sample. Clearly, forthcoming investigations must account for constructs unmeasured, but likely related to, indices of both self-regulatory and academic competencies. Although not among the primary research questions addressed, it is interesting that zero-order correlations illustrated the direct relation between parents’ reactions and math achievement grew increasingly negative across grades. It may be that when children show early signs of academic difficulties, parents are inclined to invest substantial energy in sensitive, supportive reactions, hoping to encourage their children’s investment in school. Although these efforts may relate positively to EC generally, they may not have a notable positive effect on mathematics. Because the lagged relation between K positive parental reactions and G2 math achievement was not significant in the full model, as a result of mediation by EC, this interpretation is tentative and in need of empirical examination. Other factors are likely at play. Study Strengths, Limitations, and Future Directions Results from this study extend previous work investigating relations of parenting to children’s EC and of EC to achievement (Eisenberg et al., 1999; Rudasill et al., 2010) by testing associations with panel mediation and fixed-effects models. The fact that cross-grade prediction of constructs was obtained beyond the strong within-construct stabilities in the full model lends support to the premise that EC mediates relations between parents’ positive reactions and math; however, the loss of one of the critical paths for mediation in the final fixedeffects model indicates that there is more research to be done. In addition, testing relations during this developmental period is novel and relevant, given the expected importance of EC to general school success and of early achievement to positive aca-

demic trajectories. The inclusion of multiple methods and strict analytical controls reduced the likelihood that shared method variance accounted for relations. Despite strengths of the methodological design and statistical strategy, some study facets limit the impact of the findings. First, the sample was composed of predominantly White, middle-class, educated families. Relations among related constructs have been shown to be similar among more ethnically and culturally diverse samples (Swanson et al., 2012; Zhou et al., 2004), but the extent to which ERSBs influence EC likely depends on the meaning parents attribute to the context and to responding, which is culturally influenced (Eisenberg et al., 1998). Findings may or may not generalize across groups. Next, we focused on one index of parenting representing one ERSB. Future investigations would benefit from considering a combination of ERSBs or of other parenting practices. Third, although reliable, valid, and well established, measures of parents’ reactions and EC constructs were assessed with questionnaires only. Integrating multiple informational sources beyond reports, such as observational and behavioral assessments of parent–child interactions or of EC skills, would help ensure construct validity. Fourth, EC has been found partly heritable, although it is significantly environmentally influenced (Lemery-Chalfant, Doelger, & Goldsmith, 2008). Parent–child genetic similarity likely accounts for a portion of the relations between positive reactions and EC. Examining relations while accounting for genetic associations could firm up conclusions regarding parents’ socialization of EC. This study offers novel empirical evidence associating parental socialization behavior, self-regulation, and math achievement in the first years of formal schooling. Nonetheless, precisely how mathematical competence develops in the United States in the early years of school remains unclear. Continued work is necessary to fine-tune the model via inclusion of additional theory-based variables, diverse time spans between assessments, and more complete and nuanced measurement of constructs under study.

References Alexander, K. L., Entwisle, D. R., & Horsey, C. S. (1997). From first grade forward: Early foundations of high school dropout. Sociology of Education, 70, 87–107. doi:10.2307/2673158 Allison, P. D. (2005). Fixed-effects regression models for longitudinal data using SASâ. Carey, NC: SAS Institute.

Parenting, Effortful Control, and Achievement Aud, S., Hussar, W., Planty, M., Snyder, T., Bianco, K., & Fox, M., . . . Drake, L. (2010). The condition of education 2010 (NCES 2010-028). Washington, DC: National Center for Education Statistics, Institute of Educational Services, U.S. Department of Education. Retrieved from http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2010028 Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. doi:10.1146/annurev.psych.52.1.1 Bierman, K. L., Nix, R. L., Greenberg, M. T., Blair, C., & Domitrovich, C. E. (2008). Executive functions and school readiness intervention: Impact, moderation, and mediation in the Head Start REDI program. Development and Psychopathology, 20, 821–843. doi:10.1017/ S0954579408000394 Blair, C., Calkins, S., & Kopp, L. (2010). Self-regulation as the interface of emotional and cognitive development: Implications for education and academic achievement. In R. H. Hoyle (Ed.), Handbook of personality and self-regulation (pp. 64–90). Chichester, UK: Wiley-Blackwell. doi:10.1002/9781444318111.fmatter Blair, C., Knipe, H., & Gamson, D. (2008). Is there a role for executive functions in the development of mathematics ability? Mind, Brain, & 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 Bollen, K. A., & Brand, J. E. (2010). A general panel model with random and fixed-effects: A structural equations approach. Social Forces, 89, 1–34. doi:10.1353/ sof.2010.0072 Bureau of Labor Statistics. (2010). Tabulations. Retrieved from http://ftp.bls.gov/pub/special.requests/lf/aat7.txt Chen, X., Dong, Q., & Zhou, H. (1997). Authoritative and authoritarian parenting practices and social and school performance in Chinese children. International Journal of Behavioral Development, 21, 855–873. doi:10.1080/ 016502597384703 Cole, D. A., & Maxwell, S. E. (2003). Testing meditational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558–577. doi:10.1037/ 0021-843X.112.4.558 Curby, T. W., Rimm-Kaufman, S. E., & Ponitz, C. C. (2009). Teacher-child interactions and children’s achievement trajectories across kindergarten and first grade. Journal of Educational Psychology, 101, 912–925. doi:10.1037/a0016647 Davis-Kean, P. E. (2005). The influence of parent education and family income on child achievement: The indirect role of parental expectations and the home environment. Journal of Family Psychology, 19, 294–304. doi:10.1037/0893-3200.19.2.294 Deater-Deckard, K., Mullineaux, P. Y., Petrill, S. A., & Thompson, L. A. (2009). Effortful control, surgency,

1945

and reading skills in middle childhood. Reading and Writing, 22, 107–116. doi:10.1007/s11145-007-9111-9 Denham, S. A., Mitchell-Copeland, J., Strandber, K., Auerbach, S., & Blair, K. (1997). Parental contributions to preschoolers’ emotional competence: Direct and indirect effects. Motivation and Emotion, 21, 65–86. doi:10.1023/ A:1024426431247 Derryberry, D., & Rothbart, M. K. (1997). Reactive and effortful processes in the organization of temperament. Development and Psychopathology, 9, 633–652. doi:10. 1017/S0954579497001375 Doyle, A. B., & Markiewicz, D. (2005). Parenting, marital conflict, and adjustment from early- to mid-adolescence: Mediated by adolescent attachment style? Journal of Youth and Adolescence, 34, 97–110. doi:10.1007/ s10964-005-3209-7 Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., . . . Japel, C. (2007). School readiness and later achievement. Developmental Psychology, 43, 1428–1446. doi:10.1037/0012-1649.43.6.1428 Eisenberg, N., Cumberland, A., & Spinrad, T. L. (1998). Parental socialization of emotion. Psychological Inquiry, 9, 241–273. doi:10.1207/s15327965pli0904_1 Eisenberg, N., Fabes, R. A., Shepard, S. A., Guthrie, I. K., Murphy, B. C., & Reiser, M. (1999). Parental reactions to children’s negative emotions: Longitudinal relations of quality to children’s social functioning. Child Development, 70, 513–534. doi:10.1111/1467-8624.00037 Eisenberg, N., Smith, C. L., & Spinrad, T. L. (2011). Effortful control: Relations with emotion regulation, adjustment, and socialization in childhood. In R. F. Baumeister & K. D. Vohs (Eds.), Handbook of self-regulation: Research, theory and applications (2nd ed., pp. 263–283). New York, NY: Guilford Press. Eisenberg, N., Vidmar, M., Spinrad, T. L., Eggum, N. D., Edwards, A., Gaertner, B., . . . Kupfer, A. (2010). Mothers’ teaching strategies and children’s effortful control: A longitudinal study. Developmental Psychology, 46, 1294–1308. doi:10.1037/a0020236 Eisenberg, N., Zhou, Q., Spinrad, T. L., Valiente, C., Fabes, R. A., & Liew, J. (2005). Relations among positive parenting, children’s effortful control, and externalizing problems: A three-wave longitudinal study. Child Development, 76, 1055–1071. doi:10.1111/j.1467-8624.2005.00897.x Else-Quest, N. M., Hyde, J. S., Goldsmith, H. H., & Van Hulle, C. A. (2006). Gender differences in temperament: A meta-analysis. Psychological Bulletin, 132, 33–72. doi:10.1037/0033-2909.132.1.33 Espy, K., McDiarmid, M. D., Cwik, M. F., Stalets, M. M., Hamby, A., & Senn, T. E. (2004). The contribution of executive functions to emergent mathematic skills in preschool children. Developmental Neuropsychology, 26, 465–486. doi:10.1207/s15326942dn2601_6 Fabes, R. A., Martin, C. L., Hanish, L. D., Anders, M. C., & Madden-Dierdich, D. A. (2003). Early school competence: The roles of sex-segregated play and effortful control. Developmental Psychology, 39, 848–858. doi:10.1037/0012-1649.39.5.848

1946

Swanson, Valiente, Lemery-Chalfant, Bradley, and Eggum-Wilkens

Fabes, R. A., Poulin, R. E., Eisenberg, N., & MaddenDerdich, D. A. (2002). The Coping with Children’s Negative Emotions Scale (CCNES): Psychometric properties and relations with children’s emotional competence. Marriage and Family Review, 34, 285–310. doi:10.1300/ J002v34n03_05 Fiscella, K., & Kitzman, H. (2009). Disparities in academic achievement and health: The intersection of child education and health policy. Pediatrics, 123, 1073–1080. doi:10.1542/peds.2008-0533 Fulbright, R. K., Molfese, D. L., Stevens, A. A., Skudlarski, P., Lacadie, C. M., & Gore, J. C. (2000). Cerebral activation during multiplication: A functional MR imagining study of number processing. American Journal of Neuroradiology, 21, 1048–1054. Gadeyne, E., Ghesquiere, P., & Onghena, P. (2004). Longitudinal relations between parenting and child adjustment in young children. Journal of Clinical Child and Adolescent Psychology, 33, 347–358. doi:10.1207/ s15374424jccp3302_16 Grusec, J. E. (2011). Socialization processes in the family: Social and emotional development. Annual Review of Psychology, 62, 243–269. doi:10.1146/annurev.psych. 121208.131650 Grusec, J. E., & Davidov, M. (2010). Integrating different perspectives on socialization theory and research: A domain-specific approach. Child Development, 81, 687– 709. doi:10.1111/j.1467-8624.2010.01426.x Gurland, S. T., & Grolnick, W. S. (2005). Perceived threat, controlling parenting, and children’s achievement orientations. Motivation and Emotion, 29, 103–121. doi:10. 1007/s11031-005-7956-2 Halpern, D. F., & LaMay, M. L. (2000). The smarter sex: A critical review of sex differences in intelligence. Educational Psychology Review, 12, 229–246. doi:10.1023/A: 1009027516424 Hofer, C., Eisenberg, N., & Reiser, M. (2010). The role of socialization, effortful control, and ego resiliency in French adolescents’ social functioning. Journal of Research on Adolescence, 20, 555–582. doi:10.1111/j. 1532-7795.2010.00650.x Hoffman, B. (2012). Cognitive efficiency: A conceptual and methodological comparison. Learning and Instruction, 22, 133–144. doi:10.1016/j.learninstruc.2011. 09.001 Kim, S., Brody, G. H., & Murry, V. M. (2003). Longitudinal links between contextual risks, parenting, and youth outcomes in rural African American families. Journal of Black Psychology, 29, 359–377. doi:10.1177/ 0095798403256887 Lemery-Chalfant, K., Doelger, L., & Goldsmith, H. H. (2008). Genetic relations between effortful and attentional control and symptoms of psychopathology in middle childhood. Infant and Child Development, 17, 365–385. doi:10.1002/icd.581 Liew, J., Chen, Q., & Hughes, J. N. (2010). Child effortful control, teacher-student relationships, and achievement in academically at-risk children: Additive and interac-

tive effects. Early Childhood Research Quarterly, 25, 51–64. doi:10.1016/j.ecresq.2009.07.005 Li-Grining, C. P. (2007). Effortful control among lowincome preschoolers in three cities: Stability, change, and individual differences. Developmental Psychology, 43, 208–221. doi:10.1037/0012-1649.43.1.208 MacKinnon, D. P. (2008). Introduction to statistical mediation analyses. Mahwah, NJ: Erlbaum. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83–104. doi:10.1037/ 1082-989X.7.1.83 MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99–128. doi:10.1207/ s15327906mbr3901_4 Magel, R. C., & Wibowo, S. H. (1997). Comparing the powers of the Wald-Wolfowitz and Kolmogorov-Smirnov tests. Biometric Journal, 39, 664–675. doi:10.1002/ bimj.4710390605 Martini, M. (1995). Features of home environments associated with children’s school success. Early Child Development and Care, 111, 49–68. doi:10.1080/0300443951110105 Mullis, I. V. S., Martin, M. O., Foy, P., Olson, J. F., Preuschoff, C., & Erberber, E., . . . Gala, J. (2008). TIMSS 2007 International mathematics report: Findings from IEA’s Trends in International Mathematics and Science Study at the fourth and eighth grades. Retrieved from http:// timss.bc.edu/TIMSS2007/mathreport.html Muthen, L. K., & Muthen, B. O. (1998-2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Authors. National Institute of Child Health and Human Development Early Child Care Research Network. (2003). Do children’s attention processes mediate the link between family predictors and school readiness? Developmental Psychology, 39, 581–593. doi:10.1037/0012-1649.39.3.581 National Institute of Child Health and Human Development Early Child Care Research Network. (2005). Predicting individual differences in attention, memory, and planning in first graders from experiences at home, child care, and school. Developmental Psychology, 41, 99– 114. doi:10.1037/0012-1649.41.1.99 National Institute of Child Health and Human Development Early Child Care Research Network. (2008). Mothers’ and fathers’ support for child autonomy and early school achievement. Developmental Psychology, 44, 895–907. doi:10.1037/0012-1649.44.4.895 O’Connor, E. E., Dearing, E., & Collins, B. A. (2011). Teacher-child relationship and behavior problem trajectories in elementary school. American Educational Research Journal, 48, 120–162. doi:10.3102/0002831210365008 Poehlmann, J., Schwichtenberg, A. J. M., Shah, P. E., Shlafer, R. J., Hahn, E., & Maleck, S. (2010). The development of effortful control in children born preterm. Journal of Clinical Child and Adolescent Psychology, 39, 522–536. doi:10.1080/15374416.2010.486319

Parenting, Effortful Control, and Achievement Posner, M. I., & Raichle, M. E. (1994). Images of mind. New York, NY: Scientific American Library. Robertson, D. L., & Reynolds, A. J. (2010). Family profiles and educational attainment. Children and Youth Services Review, 32, 1077–1085. doi:10.1016/j.childyouth.2009.10. 021 Rothbart, M. K. (2007). Temperament, development, and personality. Current Directions in Psychological Science, 16, 207–212. doi:10.1111/j.1467-8721.2007.00505.x Rothbart, M. K., Ahadi, S. A., Hershey, K. L., & Fisher, P. (2001). Investigations of temperament at three to seven years: The Children’s Behavior Questionnaire. Child Development, 72, 1394–1408. doi:10.1111/1467-8624. 00355 Rothbart, M. K., Ellis, L. K., Rueda, M. R., & Posner, M. I. (2003). Developing mechanisms of temperamental effortful control. Journal of Personality, 71, 1113–1144. doi:10.1111/1467-6494.7106009 Rudasill, K. M., Gallagher, K. C., & White, J. M. (2010). Temperamental attention and activity, classroom emotional support, and academic achievement in third grade. Journal of School Psychology, 48, 113–134. doi:10. 1016/j.jsp.2009.11.002 Sameroff, A. J., & Haith, M. M. (Eds.) (1996). The five to seven year shift: The age of reason and responsibility. Chicago, IL: University of Chicago Press. Spelke, E. S., & Grace, A. D. (2007). Sex, math, and science. In S. J. Ceci & W. M. Williams (Eds.), Why aren’t more women in science: Top researchers debate the evidence (pp. 57–67). Washington, DC: American Psychological Association. Spinrad, T. L., Eisenberg, N., Silva, K. M., Eggum, N. D., Reiser, M., Edwards, A., . . . Geartner, B. M. (2012). Longitudinal relations among maternal behaviors, effortful control, and young children’s compliance. Developmental Psychology, 48, 552–566. doi:10.1037/a0025898 Stormshak, E. A., Fosco, G. M., & Dishion, T. J. (2010). Implementing interventions with families in schools to increase youth school engagement: The family check-up model. School Mental Health, 2, 82–92. doi:10.1007/ s12310-009-9025-6 Swanson, J., Valiente, C., & Lemery-Chalfant, K. (2012). Predicting academic achievement from cumulative

1947

home risk: The mediating roles of effortful control, relationships, and school avoidance. Merrill-Palmer Quarterly, 58, 375–408. doi:10.1353/mpq.2012.0014 Swanson, J., Valiente, C., Lemery-Chalfant, K., & O’Brien, T. C. (2011). Predicting early adolescents’ academic achievement, social competence, and physical health from parenting, ego resilience, and engagement coping. Journal of Early Adolescence, 31, 548–576. doi:10.1177/ 0272431610366249 Tofighi, D., & MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 695–700. doi:10.3758/ s13428-011-0076-x Valiente, C., Eisenberg, N., Spinrad, T. L., Reiser, M., Cumberland, A., Losoya, S. H., . . . Lew, J. (2006). Relations among mothers’ expressivity, children’s effortful control, and their problem behaviors: A four-year longitudinal study. Emotion, 6, 459–472. doi:10.1037/ 1528-3542.6.3.459 Valiente, C., Lemery-Chalfant, K., Swanson, J., & Reiser, M. (2008). Prediction of children’s academic competence from their effortful control, relationships, and classroom participation. Journal of Educational Psychology, 100, 67–77. doi:10.1037/0022-0663.100.1.67 Winer, B. J. (1971). Statistical principles in experimental design. New York, NY: McGraw-Hill. Woodcock, R. W., McGrew, K. S., & Mather, K. (2000). Woodcock-Johnson III. Itasca, IL: Riverside. Zhou, Q., Chen, S. H., & Main, A. (2012). Commonalities and differences in the research on children’s effortful control and executive function: A call for an integrated model of self-regulation. Child Development Perspectives, 6, 112–121. doi:10.1111/j.1750-8606.2011.00176.x Zhou, Q., Eisenberg, N., Wang, Y., & Reiser, M. (2004). Chinese children’s effortful control and dispositional anger/frustration: Relations to parenting styles and children’s social functioning. Developmental Psychology, 40, 352–366. doi:10.1037/0012-1649.40.3.352 Zhou, Q., Main, A., & Wang, Y. (2010). The relations of temperamental effortful control and anger/frustration to Chinese children’s academic achievement and social adjustment: A longitudinal study. Journal of Educational Psychology, 102, 180–196. doi:10.1037/a0015908