Factor structure of self-regulation in preschoolers: Testing models of a field-based assessment for predicting early school readiness

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Abstract

The importance of early self-regulatory skill has seen increased focus in the applied research literature given the implications of these skills for early school success. A three-factor latent structure of self-regulation consisting of compliance, cool executive control, and hot executive control was tested against alternative models and retained as best fitting. Tests of model equivalence indicated that the model held invariant across Head Start and private child-care samples. Partial invariance was supported for age and gender. In the validity model, because of a substantial amount of shared variance among latent factors, we included a second-order factor explaining the two types of executive control. Higher order executive control positively predicted teacher report of learning behaviors and social competence in the classroom. These findings are discussed in light of their practical and theoretical significance.

Highlights

► Tested a three-factor latent structure of self-regulation. ► Best-fitting model included cool executive control, hot executive control, and compliance. ► Model equivalence for held across risk, gender, and age. ► Model was revised to include a second-order factor (i.e., Higher-Order Regulation). ► Second-order model predicted learning and social competence in the classroom.

Introduction

Self-regulatory skills have important implications for early school success (Blair, 2002, Blair and Diamond, 2008, Blair and Razza, 2007, Morrison et al., 2010, Raver et al., 2007a). For example, at school entry, young children are expected to self-regulate in many ways—to internalize and follow rules, listen to directions and respond accordingly, share toys, and wait their turn, all while facing a myriad of new and competing stimuli in the context of the preschool classroom (Cole et al., 2004, Eisenberg et al., 2007, Kochanska et al., 2001, Raver, 2002, Smith-Donald et al., 2007). Enthusiastic inquiry into the ontogeny and sequelae of self-regulation has uncovered the need for high-quality, cost-effective instruments to detect change in its various indicators (Denham, 2006, Raver et al., 2009).

Despite clear recognition and empirical evidence (see, e.g., Brock et al., 2009, Willoughby et al., 2011) that self-regulation plays a key role in young children’s early school success, developmentalists have struggled with its nomenclature and true nature (Carlson, 2005, Raver et al., 2009). Exemplifying these contrasting approaches, some theorists consider that a most important distinction is between (a) cortically controlled executive function (i.e., maintaining and internally guiding behavior via specific information processing strategies, without immediate reinforcement, while inhibiting automatized or prepotent responses to stimuli) and (b) subcortically controlled, temperamentally based effortful control (i.e., quickly inhibiting a dominant response to perform a subdominant one in the face of reward or punishment) (Blair et al., 2010, Raver et al., 2011). Others may, for example, give more weight to individual differences in temperamental effortful control and motivation (giving less consideration to executive function) (e.g., Kochanska & Aksan, 2006) or to other definitions of executive function or executive attention. However, despite these differences, virtually all self-regulation theorists and researchers emphasize, to various degrees, modulating systems of attention, emotion, and behavior in response to a given situation or stimulus (e.g., Jahromi and Stifter, 2008, Smith-Donald et al., 2007). As noted by Willoughby and colleagues (2011), there is much overlap in these constructs (although processes inherent in inhibitory control, working memory, and attentional flexibility are almost always involved) and much confusion in terminology (e.g., If effortful control is fast-acting, why is it termed “effortful”? How are executive function and effortful control to be distinguished if both involve inhibiting a prepotent or dominant response?).

For our purposes in capturing the key aspects of self-regulation, the current state of inquiry suggests that it may be fruitful to consider the preponderance of neurobiological and behavioral evidence and to specify as clearly as possible the distinctions that we consider most empirically defensible. In doing so, we draw from current literature and methods but attempt to transcend theoretical demarcations that obfuscate rather than clarify.

We base our constructs on the actual demands made on preschool children—entering the peer arena, independently responding to new adults (e.g., their teacher), and attempting new developmental tasks. All of these new, increasingly expected requirements tax young children’s developing self-regulatory systems that strive for equilibrium. The more plausible distinctions among constructs of self-regulation tasks that confront preschoolers daily may best be based on the predominance of cognitive, affective/motivational, and behavioral processes.

Such cognitive and affective/motivational processes, supported as they are by cortical involvement, are implicated in what we term executive control.2 Recent advances in both developmental psychobiological theorizing and research and neuroimaging suggest that two types of executive control are distinguishable, both neurally and behaviorally, and that such distinctions are important both theoretically and practically (Willoughby et al., 2011). Therefore, the first two aspects of self-regulation within our model are cool executive control (CEC: more affectively neutral, slow acting, and developing) and hot executive control (HEC: more reflexive, fast acting, early developing, and under stimulus control) (Willoughby et al., 2011).

Both CEC and HEC are tightly related because of the central role played by the prefrontal cortex (PFC) in both. The PFC is responsible for higher order cognitive processes such as the activation of information in working memory, the flexible use of attention (i.e., focusing or shifting), and inhibiting a prepotent response while activating an alternative subdominant response (Bernier et al., 2010, Blair et al., 2005, Garon et al., 2008). In particular, the dorsolateral prefrontal cortex (DL–PFC) plays an important role in CEC (Best and Miller, 2010, Garon et al., 2008, Happaney et al., 2004). Thus, the PFC is central to these aspects of self-regulation, but it is not the only neural structure that plays a role in these skills.

CEC encompasses a wide array of increasingly organized, flexible, goal-directed cognitive processes in response to relatively nonaffective and novel situations as well as complex cognitive tasks (Blair, 2002, Diamond, 2006). Children entering school need CEC abilities to purposefully shift or focus their attention to respond more flexibly to conflicting stimuli in the new and stressful situations they face in school (Blair et al., 2005, Ruff and Rothbart, 1996). Not surprisingly, CEC skills have been shown to predict early literacy skills (McClelland et al., 2007a, Willoughby et al., 2011) and to mediate intervention effects of research-based curricula implemented in Head Start programs (Bierman, Nix, Greenberg, Blair, & Domitrovich, 2008).

Regulating cognitive, emotional, and behavioral outcomes during learning activities, as indicated by competent CEC, is important, but young children also need to demonstrate self-regulation requiring more affective and motivational processes (e.g., not touching a toy that belongs to someone else). This kind of self-regulation is indicated by the second component of self-regulation, HEC. In contrast to CEC, HEC involves emotional and appetitive/motivational processes. Neurally, such processes involve orbitofrontal cortical (OFC) and limbic control in addition to PFC control (Calkins and Marcovitch, 2010, Lewis and Todd, 2007, Willoughby et al., 2011). The complexity of self-regulatory tasks requiring HEC necessitates a more intricate set of neural processes.

Whereas earlier theorizing considered CEC as “top-down” and HEC as “bottom-up” in terms of brain function, current thinking paints a more reasonable picture of what happens when children self-regulate. That is, given that both cortical and limbic structures already mentioned can be active when children are confronted with stimuli, tasks, and social mandates requiring regulation, some sort of bridging mechanism would seem to be necessary. Recent research in neurodevelopmental and biobehavioral perspectives has brought attention to the role of the anterior cingulate cortex (ACC) on deliberate, higher order self-regulation (Lewis & Todd, 2007). The ACC includes a cognitive (anterior) subdivision, which has connection to the PFC, and an emotional (posterior) subdivision connected to the limbic system (Bush et al., 2000, Calkins and Marcovitch, 2010, Lewis and Todd, 2007). As such, the ACC can be a bridge managing information incoming between higher cortical and limbic systems.

Thus, HEC, guided by both emotional information from the limbic system and cortical “braking” (and assisted by the ACC), enables children to regulate their anger and approach systems and purposefully deploy attention during emotional arousal (Rothbart, 1989, Rothbart and Bates, 1998). A decades-long program of research provides exceptionally strong empirical evidence that the demonstration during preschool of one aspect of HEC, the ability to delay gratification, predicts a myriad of outcomes through adulthood (e.g., Eigsti et al., 2006, Mischel et al., 1989, Shoda et al., 1990). Regarding our focus on school readiness, preschoolers better able to control their impulses and balance their own self-defined needs with societal norms are considered as more “well-regulated” and ready to be motivated and engaged in schooling (Blair and Razza, 2007, Raver, 2002, Raver and Knitzer, 2002, Rothbart and Jones, 1998). However, as yet little research shows that HEC contributes to school readiness over and above CEC (Brock et al., 2009, Howse et al., 2003, Willoughby et al., 2011).

CEC and HEC represent cognitive and affective/motivational aspects of self-regulation.3 But another aspect of self-regulation, behavioral, may be deemed as central during the preschool period. Thus, the processes underlying this aspect of self-regulation are neither cognitive nor affective/motivational but rather behavioral. That is, complying with adults is an overt behavioral index of children’s ability to generate appropriate behavior that may run counter to their wishes (similar to McClelland, Cameron, Wanless, & Murray’s (2007) “behavioral regulation”; see also Ramani, Brownell, & Campbell, 2010). Specifically, compliance is considered as the ability to use internalized rules and standards to help regulate behavior adaptively and flexibly and is included as the third component of self-regulation (Blair, 2002, Eisenberg and Spinrad, 2004, Kochanska, 2002).

Being able to initiate, maintain, and cease one’s own behavior according to adult requests, without adult support, is an emerging, developmentally appropriate aspect of self-regulation (Kopp, 1982). Parents and teachers socialize young children to act in a prosocial and cooperative manner, and it is with these skills that children can promote a harmonious learning environment for themselves and others (Crockenberg and Litman, 1990, Rimm-Kaufman et al., 2000). Children must learn to negotiate the needs of their independence with that of societal expectations to be considered as “good citizens” in their early learning environment.

Given this framework, our first goal for the current study was to identify and test a model of early self-regulatory constructs. Our model measures hypothesized latent constructs that comprise specific aspects of self-regulation, which we propose as facilitative of children’s early academic behavior and social functioning at school entry. Because they can be clearly discriminably defined, and given their particular importance at school entry, we consider the following three constructs as central in the measurement of preschoolers’ self-regulation: CEC, HEC, and compliance.

We met this aim empirically by obtaining directly observed indicators of self-regulation obtained from a portable, easily administered, and reliable measurement instrument administered to children attending either Head Start or private child-care programs. Collecting multiple indicators of three aspects of self-regulation enabled us to test the underlying structure of the construct. Specifically, we used all of the tasks in Smith-Donald and colleagues’ (2007) Preschool Self-Regulation Assessment (PSRA), a battery tapping early self-regulatory behaviors and designed for use in field-based research. The PSRA includes tasks gathered from the self-regulation literature (Blair, 2002, Diamond and Taylor, 1996, Murray and Kochanska, 2002), and we test our tripartite model’s ability to adequately describe variability in children’s performance on these tasks. This methodology presents a unique opportunity to validate our models of self-regulation using a brief, age-appropriate battery that is a promising contribution to applied research in educational settings.

Because self-regulation is so important for promoting children’s long-term success, assessments tapping predictors of children’s school readiness are sorely needed (Denham, 2006, National Research Council of the National Academies, 2009). Thus, we examined both factorial and predictive validity of our model using the PSRA. First, in terms of factorial validity, we tested the structure of the model (Do the tasks fit a model of CEC, HEC, and compliance?). Part of assessing factorial validity also includes determining the equivalence of the measure’s latent structure across varying groups. Specifically, normative models of development may fit differently for children of different sociocultural contexts, and careful consideration of the appropriateness of the assessment tool for evaluating school readiness is needed (Denham, 2006, Garcia Coll et al., 1996, Raver et al., 2007b). Although ample research evidence substantiates the crucial foundational nature of the transition into formal schooling for setting all children on a cycle of success or failure, racial minorities already demonstrate a significant achievement gap as early as kindergarten (Brooks-Gunn and Duncan, 1997, Campbell and von Stauffenberg, 2008, Lee and Burkham, 2002, McClelland et al., 2006, Ryan et al., 2006, Stipek and Ryan, 1997). Given these facts, can we use the same self-regulation measure with young children from different backgrounds and feel comfortable in making inferences from the data generated? The onus on researchers is to ensure that we do not introduce large amounts of error into models of early developing skills when examining groups of boys and girls and at different ages and levels of socioeconomic risk.

Thus, in the current study, our second aim was to evaluate the psychometric measurement equivalence of our model in multiple groups (age, gender, and risk status) (see also Bingenheimer et al., 2005, Raver et al., 2009, Raver et al., 2007b, Wiebe et al., 2008). Our large diverse sample allowed us to answer important questions regarding the “universality” of this field-based measure by systematically testing its measurement equivalence across various groups. Armed with an empirically supported measurement model, our third aim was to test its predictive utility for understanding young children’s early school readiness. Specifically, given the hypothesized structure of the PSRA, we examined whether CEC, HEC, and compliance predicted learning behaviors and social–emotional competence.

In accord with these aims, we predicted that the hypothesized model would fit well and be invariant across gender and socioeconomic risk and at least partially equivalent across age (Raver et al., 2009, Wiebe et al., 2008). Regarding the predictive utility of the hypothesized structure of the PSRA, we predicted that HEC and compliance would be related to social competence, whereas CEC would be related to learning behaviors.

Section snippets

Participants

The current study was part of a larger study focused on developing a portable assessment tool for measuring the social and emotional aspects of school readiness. Head Start and private child-care centers in the greater northern Virginia area were selected due to variability in race, ethnicity, and income as well as access to a large number of children. Within these sites, participants were recruited at parent and teacher meetings and through flyers posted in classrooms. The rate of

Data preparation

Following Smith-Donald and colleagues (2007), a score for each task was created (see Table 2 for descriptive results). Many tasks showed moderate significant intercorrelations (see Table 3). Toy Wait (skew = 14.21) and Toy Return (skew = 18.16) demonstrated absolute skew indexes greater than 3, suggesting extreme skew (Kline, 2005). With kurtosis indexes of 28.69 and 34.45, respectively, Toy Return and Tower Cleanup (latency to start) also demonstrated serious kurtosis based on Kline’s (2005)

Discussion

One of the greatest challenges for contemporary investigations of self-regulation involves the definition and measurement of these skills, particularly in young populations. The first aim of this study was to amass empirical support for a coherent factor structure of self-regulation during early childhood. Specifically, we examined a model positing three a priori hypothesized dimensions of self-regulation (CEC, HEC, and compliance) in comparison with alternative, more parsimonious models of the

Conclusions

Our analyses suggest the juxtaposition of separate yet unitary facets in the definition of self-regulation. Our theoretically derived model of self-regulation, composed of three related but distinct factors, provided the best fit for the data. Then we demonstrated measurement equivalence on the three-factor model across various groups of young children differing on gender, age, and program type. We were able to use this model to predict children’s learning behaviors and social competence in the

Acknowledgments

This study was funded by National Institute of Child Health and Human Development (NICHD) Grant R01HD51514. We are grateful to the many children, families, and teachers who participated in this study and to the directors of the facilities who worked with us so cooperatively. We also thank Charlotte Anderson, Chavaughn Brown, Kelly Graling, Sara Kalb, Melissa Mincic, Carol Morris, Yana Segal, and Erin Way for their unstinting assistance in study organization and data collection.

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    Present address: Outside The Classroom, Needham, MA 02494, USA.

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