GSCA Pro is free, user-friendly software for generalized structured component analysis structural equation modeling (GSCA-SEM), which implements three statistical methods for estimating models with factors only, models with components only, and models with both factors and components. This tutorial aims to provide step-by-step illustrations of how…

Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has…

In the last decade, research on the connection between curiosity and creativity has surged revealing a positive correlation. However, these findings are primarily based on cross-sectional studies, which do not establish the direction of the relationship between creativity and curiosity. Is curiosity the driving force behind creativity, or does…

Several variants of the autoregressive structural equation model were suggested over the past years, including, for example, the random intercept autoregressive panel model, the latent curve model with structured residuals, and the STARTS model. The present work shows how to place these models into a mixed-effects model framework and how to…

Imaginary latent variables are variables with negative variances and have been used to implement constraints in measurement models. This article aimed to advance this practice and rationalize the imaginary latent variables as a method to detect possible latent deficiencies in measurement models. This rationale is based on the theory of complex…

Penalized structural equation models (PSEM) is a new powerful estimation technique that can be used to tackle a variety of difficult structural estimation problems that can not be handled with previously developed methods. In this paper we describe the PSEM framework and illustrate the quality of the method with simulation studies.…

Small sample sizes pose a severe threat to convergence and accuracy of between-group level parameter estimates in multilevel structural equation modeling (SEM). However, in certain situations, such as pilot studies or when populations are inherently small, increasing samples sizes is not feasible. As a remedy, we propose a two-stage regularized…

Many education tests and psychological surveys elicit respondent views of similar constructs across scenarios (e.g., story followed by multiple choice questions) by repeating common statements across scales (one-statement-multiple-scale, OSMS). However, a respondent's earlier responses to the common statement can affect later responses to it…

Domain-specificity is a topic of debate within the field of creativity. To shed light on this issue, we conducted a meta-analysis of cross-domain correlations based on the Kaufman Domains of Creativity Scale (K-DOCS). To evaluate the model fit of one general factor versus two factors that encompass the primary K-DOCS subscales (Scholarly,…

Compared to traditional confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM) has been shown to result in less structural parameter bias when cross-loadings (CLs) are present. However, when model fit is reasonable for CFA (over ESEM), CFA should be preferred on the basis of parsimony. Using simulations, the current…

Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful group comparisons. The most common way to investigate MI is multi-group confirmatory factor analysis (MG-CFA). Although numerous guides exist, a recent…

Structural Equation Modeling (SEM) continues to grow in popularity with numerous articles, books, courses, and workshops available to help researchers become proficient with SEM quickly. However, few resources are available to help users gain a deep understanding of the analytic steps involved in SEM, with even fewer providing reproducible syntax…

Exploratory structural equation (ESEM) has received increased attention in the methodological literature as a promising tool for evaluating latent variable measurement models. It overcomes many of the limitations attached to exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), while capitalizing on the benefits of each. Given…

We demonstrate how to analyze complete multivariate generalizability theory (GT) designs within structural equation modeling frameworks that encompass both individual subscale scores and composites formed from those scores. Results from numerous analyses of observed scores obtained from respondents who completed the recently updated form of the…

Precisely estimating factor scores is challenging, especially when models are mis-specified. Stemming from network analysis, centrality measures offer an alternative approach to estimating the scores. Using a two-fold simulation design with varying availability of a priori theoretical knowledge, this study implemented hybrid centrality to estimate…