Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time,…

Parallel process latent growth curve mediation models (PP-LGCMMs) are frequently used to longitudinally investigate the mediation effects of treatment on the level and change of outcome through the level and change of mediator. An important but often violated assumption in empirical PP-LGCMM analysis is the absence of omitted confounders of the…

Conditional process models, including moderated mediation models and mediated moderation models, are widely used in behavioral science research. However, few studies have examined approaches to conduct statistical power analysis for such models and there is also a lack of software packages that provide such power analysis functionalities. In this…

Mediation analysis is widely used to study whether the effect of an independent variable on an outcome is transmitted through a mediator. Bayesian methods have become increasingly popular for mediation analysis. However, limited research has been done on formal Bayesian hypothesis testing of mediation. Although hypothesis testing using Bayes…

A Q-matrix is a binary matrix that defines the relationship between items and latent variables and is widely used in diagnostic classification models (DCMs), and can also be adopted in multidimensional item response theory (MIRT) models. The construction process of the Q-matrix is typically carried out by experts in the subject area of the items…

Accurate item parameters and standard errors (SEs) are crucial for many multidimensional item response theory (MIRT) applications. A recent study proposed the Gaussian Variational Expectation Maximization (GVEM) algorithm to improve computational efficiency and estimation accuracy (Cho et al., 2021). However, the SE estimation procedure has yet to…

Despite decades of research related to teaching and learning, the findings have made little impact on classroom teaching and learning. This paper briefly describes the four existing methods to close this gap, with more extensive analyses of the limitations of one of the four methods, which is to consolidate and distill robust laboratory findings…

Multisite trials that randomize individuals (e.g., students) within sites (e.g., schools) or clusters (e.g., teachers/classrooms) within sites (e.g., schools) are commonly used for program evaluation because they provide opportunities to learn about treatment effects as well as their heterogeneity across sites and subgroups (defined by moderating…

In causal mediation studies that decompose an average treatment effect into indirect and direct effects, examples of post-treatment confounding are abundant. In the presence of treatment-by-mediator interactions, past research has generally considered it infeasible to adjust for a post-treatment confounder of the mediator-outcome relationship due…

Diagnostic classification models (DCMs) are psychometric models that yield probabilistic classifications of respondents according to a set of discrete latent variables. The current study examines the recently introduced one-parameter log-linear cognitive diagnosis model (1-PLCDM), which has increased interpretability compared with general DCMs due…

Diagnostic classification models (DCMs) have grown in popularity as stakeholders increasingly desire actionable information related to students' skill competencies. Longitudinal DCMs offer a psychometric framework for providing estimates of students' proficiency status transitions over time. For both cross-sectional and longitudinal DCMs, it is…

Factor analysis is often used to model scales created to measure latent constructs, and internal structure validity evidence is commonly assessed with indices like SRMR, RMSEA, and CFI. These indices are essentially effect size measures and definitive benchmarks regarding which values connote reasonable fit have been elusive. Simulations from the…

Latent class models with covariates are widely used for psychological, social, and educational research. Yet the fundamental identifiability issue of these models has not been fully addressed. Among the previous research on the identifiability of latent class models with covariates, Huang and Bandeen-Roche (Psychometrika 69:5-32, 2004) studied the…

This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for…

Mixed Membership Models (MMMs) are a popular family of latent structure models for complex multivariate data. Instead of forcing each subject to belong to a single cluster, MMMs incorporate a vector of subject-specific weights characterizing partial membership across clusters. With this flexibility come challenges in uniquely identifying,…