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Laura Castro-Schilo

Researcher at SAS Institute

Publications -  25
Citations -  1024

Laura Castro-Schilo is an academic researcher from SAS Institute. The author has contributed to research in topics: Confirmatory factor analysis & Cognition. The author has an hindex of 12, co-authored 24 publications receiving 720 citations. Previous affiliations of Laura Castro-Schilo include University of North Carolina at Chapel Hill & University of California, Davis.

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Determinants of mental health and self-rated health: a model of socioeconomic status, neighborhood safety, and physical activity.

TL;DR: Identifying the psychological (neighborhood safety) and behavioral (physical activity) factors that influence mental health and SRH is critical for tailoring interventions and designing programs that can improve overall health.
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Using residualized change versus difference scores for longitudinal research

TL;DR: In this article, the authors compared whether a residualized change model versus a difference score model should be used to assess the effect of change over time, and found that the difference score was more accurate than the residual change model.
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Distinguishing ordinal and disordinal interactions.

TL;DR: This work presents a re-parameterized regression model for the Linear × Linear interaction of 2 quantitative predictors that yields point and interval estimates of 1 key parameter-the crossover point of predicted values-and leaves certain other parameters unchanged, and explains how resulting parameter estimates provide direct evidence for distinguishing ordinal from disordinal interactions.
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Preliminary Detection of Relations Among Dynamic Processes With Two-Occasion Data

TL;DR: This article proposed a two-wave latent change score (2W-LCS) model, a technique appropriate for preliminary detection of relations among dynamic processes with two-occasion data, which is well suited for the investigation of hypotheses in which changes in a construct are posited as predictors of changes in another construct.
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Nonlinear Gompertz curve models of achievement gaps in mathematics and reading

TL;DR: This article examined achievement trajectories in mathematics and reading from school entry through the end of middle school with linear and nonlinear growth curves in two large longitudinal data sets (National Longitudinal Study of Youth-Children and Young Adults and Early Childhood Longitudinal study-Kindergarten Cohort [ECLS-K]).