Joel S. Steele

Fine Motor Skills and Early Comprehension of the World: Two New School Readiness Indicators.

Duncan et al. (2007) presented a new methodology for identifying kindergarten readiness factors and quantifying their importance by determining which of childrens developing skills measured around kindergarten entrance would predict later reading and math achievement. This article extends Duncan et al.s work to identify kindergarten readiness factors with 6 longitudinal data sets. Their results identified kindergarten math and reading readiness and attention as the primary long-term predictors but found no effects from social skills or internalizing and externalizing behavior. We incorporated motor skills measures from 3 of the data sets and found that fine motor skills are an additional strong predictor of later achievement. Using one of the data sets, we also predicted later science scores and incorporated an additional early test of general knowledge of the social and physical world as a predictor. We found that the test of general knowledge was by far the strongest predictor of science and reading and also contributed significantly to predicting later math, making the content of this test another important kindergarten readiness indicator. Together, attention, fine motor skills, and general knowledge are much stronger overall predictors of later math, reading, and science scores than early math and reading scores alone.

Do Sensitive Parents Foster Kind Children, or Vice Versa? Bidirectional Influences between Children's Prosocial Behavior and Parental Sensitivity.

Bidirectional theories of social development have been around for over 40 years (Bell, 1968), yet they have been applied primarily to the study of antisocial development. In the present study, the reciprocal relationship between parenting behavior and childrens socially competent behaviors were examined. Using the National Institute of Child Health and Development Study of Early Child Care data set (NICHD Early Child Care Research Network, 2005), bidirectional relationships between parental sensitivity and childrens prosocial behavior were modeled using latent variables in structural equation modeling for mothers and fathers, separately. Children and their parents engaged in structured interactions when children were 54-month-olds, 3rd graders, and 5th graders, and these interactions were coded for parental sensitivity. At 3rd, 5th, and 6th grades, teachers and parents reported on childrens prosocial behavior. Parental education and child gender were entered as covariates in the models. The results provide support for a bidirectional relationship between childrens prosocial behavior and maternal sensitivity (but not paternal sensitivity) in middle childhood. The importance of using a bidirectional approach to examine the development of social competence is emphasized.

Early Behavioral Associations of Achievement Trajectories.

Duncan et al. (2007) examined associations between early behavioral and cognitive skills with later achievement. These associations were examined in 6 different data sets and results converged to suggest that early behavioral competences or problems had little, if any, prediction to later achievement and that attentional competences had small positive relations with later achievement. In contrast, cognitive abilities were by far the strongest predictors of achievement. We provide and investigate potential reasons why Duncan et al. found little to no association between behavior and later achievement in a reanalysis of data from 3 studies previously analyzed by Duncan et al. Potential reasons include the validity of the behavioral measures, treatment of the behavioral measures as continuous as opposed to categorical, and the choice of data analytic method. In this article, we discuss these issues at greater length and address them in our reanalysis. We also bring into question the nature of the relationship between behavior and achievement. Generally, our reanalysis supports the idea that attention measures are more predictive than behavioral measures; however, certain behavior measures showed small to moderate associations to concurrent levels of academic achievement and changes in academic achievement through elementary school.

Coping as Part of Motivational Resilience in School A Multidimensional Measure of Families, Allocations, and Profiles of Academic Coping

A study was designed to examine a multidimensional measure of children’s coping in the academic domain as part of a larger model of motivational resilience. Using items tapping multiple ways of dealing with academic problems, including five adaptive ways (strategizing, help-seeking, comfort-seeking, self-encouragement, and commitment) and six maladaptive ways (confusion, escape, concealment, self-pity, rumination, and projection), analyses of self-reports collected from 1,020 third through sixth graders in fall and spring of the same school year showed that item sets marking each way of coping were generally unidimensional and internally consistent; and confirmatory analyses showed that multidimensional models provided a good fit to the data for both adaptive and maladaptive coping at both time points. Of greatest interest were the connections of these ways of coping to the constructs from a model of motivational resilience. As predicted, adaptive coping was positively correlated with students’ self-system processes of relatedness, competence, and autonomy as well as their ongoing engagement and reengagement, and negatively correlated with their catastrophizing appraisals and emotional reactivity. Maladaptive coping showed the opposite pattern of correlations. The potential utility of the measure, the different scores derived from it, and the role of constructive coping in motivational resilience are discussed.

White matter maturation supports the development of reasoning ability through its influence on processing speed

The structure of the human brain changes in several ways throughout childhood and adolescence. Perhaps the most salient of these changes is the strengthening of white matter tracts that enable distal brain regions to communicate with one another more quickly and efficiently. Here, we sought to understand whether and how white matter changes contribute to improved reasoning ability over development. In particular, we sought to understand whether previously reported relationships between white matter microstructure and reasoning are mediated by processing speed. To this end, we analyzed diffusion tensor imaging data as well as data from standard psychometric tests of cognitive abilities from 103 individuals between the ages of 6 and 18. We used structural equation modeling to investigate the network of relationships between brain and behavior variables. Our analyses provide support for the hypothesis that white matter maturation (as indexed either by microstructural organization or volume) supports improved processing speed, which, in turn, supports improved reasoning ability.

Analyzing the Dynamics of Affective Dyadic Interactions Using Patterns of Intra- and Interindividual Variability

There are many compelling accounts of the ways in which the emotions of 1 member of a romantic relationship should influence and be influenced by the partner. However, there are relatively few methodological tools available for representing the alleged complexity of dyad level emotional experiences. In this article, we present an algorithm for examining such affective dynamics based on patterns of variability. The algorithm identifies periods of stability based on length of time and amplitude of emotional fluctuations. The patterns of variability and stability are quantified at the individual and dyadic level, and the approach is illustrated using data of the daily emotional experiences of individuals in romantic couples. With this technique, we examine the fluctuations of the emotions for each person and inspect the overlap fluctuations between both individuals in the dyad. The individual and dyadic indices of variability are then used to predict the status of the dyads (i.e., together, apart) 1 year later.

Exploratory Latent Growth Models in the Structural Equation Modeling Framework

Latent growth modeling is often conducted using a confirmatory approach whereby specific structures of individual change (e.g., linear, quadratic, exponential, etc.) are fit to the observed data, the best fitting model is chosen based on fit statistics and theoretical considerations, and parameters from this model are interpreted. This confirmatory approach is appropriate when a strong theory guides the model fitting process. However, this approach is often also used when there is not a strong theory to guide the model fitting process, which might lead researchers to misrepresent or miss key change characteristics present in their data. We discuss Tuckerized curves (Tucker, 1958, 1966) as an exploratory way of modeling change processes based on principal components analysis and propose an exploratory approach to latent growth modeling whereby minimal constraints are imposed on the structure of within-person change. These methods are applied to longitudinal data on cortisol response during a controlled experimental manipulation and height changes from early childhood through adulthood collected from 2 different studies. We highlight the additional insights gained, some of the benefits, limitations, and potential extensions of the exploratory growth curve approach and suggest there is much to be gained from using such models to generate new and potentially more precise theories about change and development.

An Idiographic Approach to Estimating Models of Dyadic Interactions with Differential Equations

We present an idiographic approach to modeling dyadic interactions using differential equations. Using data representing daily affect ratings from romantic relationships, we examined several models conceptualizing different types of dyadic interactions. We fitted each model to each of the dyads and the resulting AICc values were used to classify the most likely configuration of interaction for each dyad. Additionally, the AICc from the different models were used in parameter averaging across models. Averaged parameters were used in models involving predictors of relationship dynamics, as indexed by these parameters, as well as models wherein the parameters predicted distal outcomes of the dyads such as relationship satisfaction and status. Results indicated that, within our sample, the most likely interaction style was that of independence, without evidence of emotional interrelations between the two individuals in the couple. Attachment-related avoidance and anxiety showed significant relations with model parameters, such that ideal levels of affect for males were negatively influenced by higher levels of avoidance from their partner while their own levels of anxiety had positive effects on their levels of dyadic coregulation. For females coregulation was negatively influenced by both time in the relationship and their partner’s level of avoidance. Analysis involving distal outcomes showed modest influences from the individual’s level of ideal affect.

An intervention to improve sibling relationship quality among youth in foster care: Results of a randomized clinical trial

Sibling programming is an important part of a prevention framework, particularly for youth in foster care. After children are removed from their families and placed into foster care in the aftermath of maltreatment, the sibling relationship is often the most viable ongoing relationship available to the child, and may be critical to a youths sense of connection, emotional support, and continuity. The promise of dyadic sibling programming in particular rests on the ability of interventions to enhance the quality of sibling relationships; yet little research exists that suggests that sibling interventions can improve relationship quality among foster youth. The primary aim of the current study was to examine the effects of a specific dyadic sibling-focused intervention for older and younger siblings on sibling relationship quality. One hundred sixty four dyads (328 youth) participated in the study, with each dyad consisting of an older sibling between 11 and 15 years of age at baseline and a younger sibling separated in age by less than 4 years. Hierarchical linear models were applied to self-reported, observer-reported and observational data over the 18-month study period. Findings suggest that the sibling intervention holds promise for improving sibling relationship quality among youth in foster care. Implications and future directions for research are discussed.

Evaluation of a Bayesian Approach to Estimating Nonlinear Mixed-Effects Mixture Models

The growth mixture model has become increasingly popular, given the willingness to acknowledge developmental heterogeneity in populations. Typically, linear growth mixture models, based on polynomials or piecewise functions, are used in substantive applications and evaluated quantitatively through simulation. Growth mixture models that follow inherently nonlinear trajectories, referred to as nonlinear mixed-effects mixture models, have received comparatively little attention—likely due to estimation complexity. Previous work on the estimation of these models has involved multistep routines (Kelley, 2008), maximum likelihood estimation (MLE) via the E-M algorithm (Harring, 2005, 2012), Taylor series expansion and MLE within the structural equation modeling framework (Grimm, Ram, & Estabrook, 2010), and MLE by adaptive Gauss–Hermite quadrature (Codd & Cudeck, 2014). This article proposes and evaluates the use of Bayesian estimation with OpenBUGS (Lunn, Spiegelhalter, Thomas, & Best, 2009), a free program, and compares its performance with the Taylor series expansion approach. Finally, these estimation routines are used to evaluate the need for multiple latent classes to account for between-child differences in the development of reading ability.