Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, noniterative estimator for latent variable models. Associated with this estimator are equation-specific tests of model misspecification. One issue with equation-specific tests is that they lack specificity, in that they indicate…

We extended current knowledge by examining the performance of several Bayesian model fit and comparison indices through a simulation study using the confirmatory factor analysis. Our goal was to determine whether commonly implemented Bayesian indices can detect specification errors. Specifically, we wanted to uncover any differences in detecting…

In factorial survey designs, respondents evaluate multiple short descriptions of social objects (vignettes) that experimentally vary different levels of attributes of interest. Analytical methods (including individual-level regression analysis and multilevel models) estimate the weights (or utilities) assigned to the levels of the different…

The multiple mechanisms of entrepreneurial intention are still an open issue, and few have explored whether the relationship between entrepreneurial intention and proactive personality is influenced by entrepreneurial passion. This study aims to reveal the mediation role of entrepreneurial passion between proactive personality and entrepreneurial…

Piecewise latent growth modeling (PLGM) is a class of longitudinal models using a structural equation modeling framework to describe stage-like, discontinuous change of individuals over time. PLGM breaks the overall time window into non-overlapped segments where separate functions can be fitted to represent differential growth patterns for each…

This study examined the relationship between demands, resources, meaningful work, and burnout of counselors-in-training. The results of structural equation modeling indicated that demands and resources significantly predicted burnout of counselors-in-training, whereas meaningful work did not mediate the relationship between resources and burnout.…

Given the recent attention to language-domain-specific grit in the field of SLA and the scarcity of research on the antecedents of L2 grit, we proposed a model that links L2 learners' basic psychological needs (BPN) (i.e. autonomy, competence, and relatedness), L2 grit (i.e. perseverance of effort (PE) and consistency of interest (CI)), and L2…

When comparing relations and means of latent variables, it is important to establish measurement invariance (MI). Most methods to assess MI are based on confirmatory factor analysis (CFA). Recently, new methods have been developed based on exploratory factor analysis (EFA); most notably, as extensions of multi-group EFA, researchers introduced…

The integration of AI-chatbots into higher education offers the potential to enhance learning practices. This research aims to explore the factors influencing AI-chatbots adoption within higher education, with a focus on the moderating roles of technological proficiency and academic discipline. Utilizing a survey-based approach and advanced…

Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter--a parameter akin to the correlation root mean squared residual. The misspecification parameter can be interpreted on its…

This research examines the influence of transformational leadership on role ambiguity and work-life balance of university employees in the Philippines during the COVID-19 pandemic. The study also analyzes the moderating effect of employee involvement on the link between transformational leadership and role ambiguity. Online survey data were…

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the…

A full structural equation model (SEM) typically consists of both a measurement model (describing relationships between latent variables and observed scale items) and a structural model (describing relationships among latent variables). However, often researchers are primarily interested in testing hypotheses related to the structural model while…

Common ways to test associations between two repeatedly measured constructs have two primary limitations. Studies often report the average effects and ignore the heterogeneity. Independently interpreted autoregression and cross-lagged coefficients (i.e. local effects) may not match the holistic dynamic patterns (i.e. considering all coefficients…

Although generalizability theory (GT) designs typically are analyzed using analysis of variance (ANOVA) procedures, they also can be integrated into structural equation models (SEMs). In this tutorial, we review basic concepts for conducting univariate and multivariate GT analyses and demonstrate advantages of doing such analyses within SEM…