Better together: novel methods for measuring and modeling development of executive function diversity while accounting for unity.

Introduction: Executive functions (EFs) are linked to positive outcomes across the lifespan. Yet, methodological challenges have prevented precise understanding of the developmental trajectory of their organization. Methods: We introduce novel methods to address challenges for both measuring and modeling EFs using an accelerated longitudinal design with a large, diverse sample of students in middle childhood (N = 1,286; ages 8 to 14). We used eight adaptive assessments hypothesized to measure three EFs, working memory, context monitoring, and interference resolution. We deployed adaptive assessments to equate EF challenge across ages and a data-driven, network analytic approach to reveal the evolving diversity of EFs while simultaneously accounting for their unity. Results and discussion: Using this methodological paradigm shift brought new precision and clarity to the development of these EFs, showing these eight tasks are organized into three stable components by age 10, but refinement of composition of these components continues through at least age 14.

Use of Composites in Analysis of Individual Time Series: Implications for Person-Specific Dynamic Parameters.

Psychology has seen an increase in the study of intra-individual processes through time series. Although timely and relevant, modeling variance from unique sources in time series may lead to model non-convergence, making it tempting to reduce the number of parameters in the model. The purpose of this paper was to investigate use of composite scores in analysis of individual time series. To meet this goal, we compared the dynamic factor analysis (DFA) model using multiple indicators with the autoregressive (AR) and autoregressive + white noise (AR + WN) models using composites. We conducted a Monte Carlo study in which a DFA(1,1) model was used to generate the data with varying conditions of size of factor loadings, number of indicators, size of AR parameters, and time series length. We also conducted analysis of empirical data from six individuals' daily self-reports of mood. Findings indicated that the DFA(1,1) and AR(1) + WN models performed comparably in their recovery of AR parameters, while the AR(1) model underestimated the parameter under nearly all conditions. However, variability of estimates may make the AR(1) + WN model less viable for researchers conducting individual-level analyses when the true data-generating mechanism is the DFA(1, 1) model.

Cross-Sectional Analysis of Longitudinal Mediation Processes.

Statistical mediation analysis can help to identify and explain the mechanisms behind psychological processes. Examining a set of variables for mediation effects is a ubiquitous process in the social sciences literature; however, despite evidence suggesting that cross-sectional data can misrepresent the mediation of longitudinal processes, cross-sectional analyses continue to be used in this manner. Alternative longitudinal mediation models, including those rooted in a structural equation modeling framework (cross-lagged panel, latent growth curve, and latent difference score models) are currently available and may provide a better representation of mediation processes for longitudinal data. The purpose of this paper is twofold: first, we provide a comparison of cross-sectional and longitudinal mediation models; second, we advocate using models to evaluate mediation effects that capture the temporal sequence of the process under study. Two separate empirical examples are presented to illustrate differences in the conclusions drawn from cross-sectional and longitudinal mediation analyses. Findings from these examples yielded substantial differences in interpretations between the cross-sectional and longitudinal mediation models considered here. Based on these observations, researchers should use caution when attempting to use cross-sectional data in place of longitudinal data for mediation analyses.