Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, but their analyses are challenging. The present paper mathematically derived, empirically compared, and step-by-step demonstrated three types (i.e.,…

Participatory modeling (PM) is an engaged research methodology for creating analog or computer-based models of complex systems, such as socio-environmental systems. Used across a range of fields, PM centers stakeholder knowledge and participation to create more internally valid models that can inform policy and increase engagement and trust…

The analysis of cross-lagged relationships is a popular approach in prevention research to explore the dynamics between constructs over time. However, a limitation of commonly used cross-lagged models is the requirement of equally spaced measurement occasions that prevents the usage of flexible longitudinal designs and complicates cross-study…

This study proposes a two-stage causal modeling with instrumental variables to mitigate selection bias, provide correct standard error estimates, and address nonnormal and missing data issues simultaneously. Bayesian methods are used for model estimation. Robust methods with Student's "t" distributions are used to account for nonnormal…

Social scientists are often interested in estimating the marginal effects of a time-varying treatment on an end-of-study continuous outcome. With observational data, estimating these effects is complicated by the presence of time-varying confounders affected by prior treatments, which may lead to bias in conventional regression and matching…

Longitudinal models provide researchers with a framework for investigating key aspects of change over time, but rarely is "time" itself modeled as a focal parameter of interest. Rather than treat time as purely an index of measurement occasions, the proposed Time to Criterion (T2C) growth model allows for modeling individual variability…

Latent change score models (LCSMs) proposed by McArdle (McArdle, 2000, 2009; McArdle & Nesselroade, 1994) offer a powerful tool for longitudinal data analysis. They are becoming increasingly popular in social and behavioral research (e.g., Gerstorf et al., 2007; Ghisletta & Lindenberger, 2005; King et al., 2006; Raz et al., 2008). Although…

In this article, we systematically introduce the just another Gibbs sampler (JAGS) software program to fit common Bayesian cognitive diagnosis models (CDMs) including the deterministic inputs, noisy "and" gate model; the deterministic inputs, noisy "or" gate model; the linear logistic model; the reduced reparameterized unified…

The process of talent development with children and young adults who have participated in programs based on the Enrichment Triad and the Schoolwide Enrichment Model (SEM) has been the focus of research by Renzulli and Reis, as well as other scholars and colleagues, for over four decades. Periodic summaries of this extensive research have been…

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module…

Spline growth modelling is a popular tool to model change processes with distinct phases and change points in longitudinal studies. Focusing on linear spline growth models with two phases and a fixed change point (the transition point from one phase to the other), we detail how to find optimal data collection designs that maximize the efficiency…

The recent popularity of models that capture the dynamic coevolution of both network structure and behavior has driven the need for summary indices to assess the adequacy of these models to reproduce dynamic properties of scientific or practical importance. Whereas there are several existing indices for assessing the ability of the model to…

This report provides an overview of the process used to derive a school's growth level and summarizes 2015 math and reading/ELA growth levels for all AISD elementary, middle and high schools. Additionally, longitudinal data are provided for each school level.

Growth curve models are widely used in social and behavioral sciences. However, typical growth curve models often assume that the errors are normally distributed although non-normal data may be even more common than normal data. In order to avoid possible statistical inference problems in blindly assuming normality, a general Bayesian framework is…

Although classical statistical techniques have been a valuable tool in second language (L2) research, L2 research questions have started to grow beyond those techniques' capabilities, and indeed are often limited by them. Questions about how complex constructs relate to each other or to constituent subskills, about longitudinal development in…