Inferring reciprocal effects or causality between variables is a central aim of behavioral and
psychological research. To address reciprocal effects, a variety of longitudinal models that …

The current study explored the associations between non–cognitive science-related
variables, ie, science interest, utility, self-efficacy, science identity, and science achievement …

Objectives: The aim of this study was to investigate the effects of infant and toddler head
growth on intelligence scores from early childhood to adulthood in very preterm (< 32 weeks …

Measurement invariance (MI) describes the equivalence of a construct across groups. To be
able to meaningfully compare latent factor means between groups, it is crucial to establish …

Abstract Background Growth Mixture Modeling (GMM) is commonly used to group
individuals on their development over time, but convergence issues and impossible values …

Structural equation model (SEM) trees are data-driven tools for finding variables that predict
group differences in SEM parameters. SEM trees build upon the decision tree paradigm by …

Numerous studies have shed some light on the importance of associated factors of
collaborative attitudes. However, most previous studies aimed to explore the influence of …

Latent growth models (LGMs) are an application of structural equation modeling and
frequently used in developmental and clinical research to analyze change over time in …

Dynamic panel models are a popular approach to study interrelationships between
repeatedly measured variables. Often, dynamic panel models are specified and estimated …

This study compared two single-group approaches for assessing measurement invariance
across an observed background variable: restricted factor analysis (RFA) and moderated …