Human Development & Quantitative Methodology

Permanent URI for this communityhttp://hdl.handle.net/1903/2248

The departments within the College of Education were reorganized and renamed as of July 1, 2011. This department incorporates the former departments of Measurement, Statistics & Evaluation; Human Development; and the Institute for Child Study.

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Subject: search.filters.subject.longitudinal

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    Social versus non-social behavioral inhibition: Differential prediction from early childhood of long-term psychosocial outcomes
    (Wiley, 2023-06-22) Tan, Enda; Zeytinoglu, Selin; Morales, Santiago; Buzzell, George A.; Almas, Alisa N.; Degnan, Kathryn A.; Chronis-Tuscano, Andrea; Henderson, Heather; Pine, Daniel S.; Fox, Nathan A.
    Behavioral inhibition (BI) is a temperamental style characterized by cautious and fearful behaviors in novel situations. The present multi-method, longitudinal study examined whether young children's observed and parent-reported BI in social versus non-social contexts predicts different long-term psychosocial outcomes. Participants (N = 279) were drawn from a longitudinal study of socioemotional development. BI in social contexts (“social BI”) was measured via children's observed wariness toward unfamiliar adults and peers at 24 and 36 months and parents’ reports of children's social fear/shyness at 24, 36, and 48 months. BI in non-social contexts (“non-social BI”) was measured via children's observed fearful responses to masks and novel toys, and parents’ reports of children's distress to non-social novelty at 9 months and non-social fear at 48 months. At 15 years, anxiety was assessed via adolescent- and parent-reports, and global internalizing and externalizing problems were assessed via parent-reports. Confirmatory factor analysis showed that a two-factor model fit the BI data significantly better than a single-factor model, providing evidence for the dissociation of BI in social versus non-social contexts. Social BI was uniquely associated with adolescent social anxiety, whereas non-social BI was specifically associated with adolescent separation anxiety. Neither social BI nor non-social BI predicted global internalizing and externalizing problems, providing evidence for the specific relations between BI and anxiety problems. Together, these results suggest that young children's inhibited responses in social versus non-social situations predict different subtypes of anxiety problems in adolescence, highlighting the multifaceted nature of BI and the divergent trajectories of different anxiety problems.
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    INVESTIGATING MODEL SELECTION AND PARAMETER RECOVERY OF THE LATENT VARIABLE AUTOREGRESIVE LATENT TRAJECTORY (LV-ALT) MODEL FOR REPEATED MEASURES DATA: A MONTE CARLO SIMULATION STUDY
    (2023) Houser, Ari; Harring, Jeffrey R; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Over the past several decades, several highly generalized models have been developed which can reduce, through parameter constraints, to a variety of classical models. One such framework, the Autoregressive Latent Trajectory (ALT) model, is a combination of two classical approaches to longitudinal modeling: the autoregressive or simplex family, in which trait scores at one occasion are regressed on scores at a previous occasion, and latent trajectory or growth curve models, in which individual trajectories are specified by a set of latent factors (typically a slope and an intercept) whose values vary across the population.The Latent Variable-Autoregressive Latent Trajectory (LV-ALT) model has been recently proposed as an extension of the ALT model in which the traits of interest are latent constructs measured by one or more indicator variables. The LV-ALT is presented as a framework by which one may compare the fit of a chosen model to alternative possibilities or use to empirically guide the selection of a model in the absence of theory, prior research, or standard practice. To date, however, there has not been any robust analysis of the efficacy or usefulness of the LV-ALT model for this purpose. This study uses a Monte Carlo simulation study to evaluate the efficacy of the basic formulation of the LV-ALT model (univariate latent growth process, single indicator variable) to identify the true model, model family, and key characteristics of the model under manipulated conditions of true model parameters, sample size, measurement reliability, and missing data. The performance of the LV-ALT model for model selection is mixed. Under most manipulated conditions, the best-fitting of nine candidate models was different than the generating model, and the cost of model misspecification for parameter recovery included significant increases in bias and loss of precision in parameter estimation. As a general rule, the LV-ALT should not be relied upon to empirically select a specific model, or to choose between several theoretical plausible models in the autoregressive or latent growth families. Larger sample size, greater measurement reliability, larger parameter magnitude, and a constant autoregressive parameter are associated with greater likelihood of correct model selection.