Longitudinal data analysis

TL;DR: Browne et al. as mentioned in this paper showed that the strict requirement of zero cross-loadings in CFA often does not fit the data well and has led to a tendency to rely on extensive model modification to find a wellfitting model.

TL;DR: This paper used a latent profile analysis (LPA) to identify groups of students who had similar profiles for multiple dimensions of academic self-concept (ASC) and related these LPA groups to a diverse set of correlates.

TL;DR: This paper constitutes a companion paper to the R package lcmm by introducing each family of models, the estimation technique, some implementation details and giving examples through a dataset on cognitive aging.

TL;DR: This review provides a survey and synthesis of four areas of active methodological research: (a) mediation analysis for longitudinal data, (b) causal inference for indirect effects, (c) mediationAnalysis for discrete and nonnormal variables, and (d) mediation assessment in multilevel designs.