In the current longitudinal study, we investigated the structure of students' (N = 1,469) specific growth constructs and their broader growth orientation using a bifactor exploratory structural equation model. We also examined the extent to which each of these components was associated with gains in students' academic and nonacademic outcomes…

To evaluate multidimensional factor structure, a popular method that combines features of confirmatory and exploratory factor analysis is Bayesian structural equation modeling with small-variance normal priors (BSEM-N). This simulation study evaluated BSEM-N as a variable selection and parameter estimation tool in factor analysis with sparse…

Multivariate analysis of variance (MANOVA) is widely used to test the null hypothesis of equal multivariate means across 2 or more groups. MANOVA rests upon an assumption that error terms are independent of one another, which can be violated if individuals are clustered or nested within groups, such as schools. Ignoring such nesting can result in…

Although generalizability theory (GT) designs have traditionally been analyzed within an ANOVA framework, identical results can be obtained with structural equation models (SEMs) but extended to represent multiple sources of both systematic and measurement error variance, include estimation methods less likely to produce negative variance…

Traditional observed variable multilevel models for evaluating indirect effects are limited by their inability to quantify measurement and sampling error. They are further restricted by being unable to fully separate within- and between-level effects without bias. Doubly latent models reduce these biases by decomposing the observed within-level…

To advance understanding of international students' psychological well-being and social-emotional experiences, we tested whether specific social influences could enhance international students' belonging and well-being and attenuate loneliness. Graduate and undergraduate international students (N = 126) from two universities in the United States…

Latent growth modeling (LGM) is a popular and flexible technique that may be used when data are collected across several different measurement occasions. Modeling the appropriate growth trajectory has important implications with respect to the accurate interpretation of parameter estimates of interest in a latent growth model that may impact…

Small samples are common in growth models due to financial and logistical difficulties of following people longitudinally. For similar reasons, longitudinal studies often contain missing data. Though full information maximum likelihood (FIML) is popular to accommodate missing data, the limited number of studies in this area have found that FIML…

This study investigated the optimal strategy for model specification search under the latent growth modeling (LGM) framework, specifically on searching for the correct polynomial mean or average growth model when there is no a priori hypothesized model in the absence of theory. In this simulation study, the effectiveness of different starting…

Structured means analysis is a very useful approach for testing hypotheses about population means on latent constructs. In such models, a z test is most commonly used for testing the statistical significance of the relevant parameter estimates or of the differences between parameter estimates, where a z value is computed based on the asymptotic…

We present factor extension procedures for confirmatory factor analysis that provide estimates of the relations of common and unique factors with external variables that do not undergo factor analysis. We present identification strategies that build upon restrictions of the pattern of correlations between unique factors and external variables. The…

We examined two aspects of college students' (N = 385) sense of belonging and its relations with three indicators of self-regulated learning. We also tested the mediating role of achievement goals in these relations. One aspect, sense of belonging to school, functioned as a significant predictor of self-reported metacognitive and academic time…

Using Monte Carlo simulations, this research examined the performance of four missing data methods in SEM under different multivariate distributional conditions. The effects of four independent variables (sample size, missing proportion, distribution shape, and factor loading magnitude) were investigated on six outcome variables: convergence rate,…

The purpose of this study was to investigate the relations among perceived instructional support (provision of relevance and involvement), subjective task value beliefs (utility, attainment, and intrinsic value), and engagement (behavioral and emotional) over the course of a semester for graduate students enrolled in an introductory research…

The purpose of this study was to explore whether conceptual change predicted emotional and attitudinal change while learning about genetically modified foods (GMFs). Participants were 322 college students; half read a refutation text designed to shift conceptual knowledge, emotions, and attitudes, while the other half served as a control group.…