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…

Matrix decomposition structural equation modeling (MDSEM) is introduced as a novel approach in structural equation modeling, contrasting with traditional structural equation modeling (SEM). MDSEM approximates the data matrix using a model generated by the hypothetical model and addresses limitations faced by conventional SEM procedures by…

Academic engagement plays a crucial role in students' learning and performance. One of the most popular measures for assessing this construct is the Utrecht Work Engagement Scale for Students (UWES-S), which is based on a tridimensional conceptualization consisting of dedication, vigor, and absorption. However, prior research on its factor…

Cross-loadings are common in multiple-factor confirmatory factor analysis (CFA) but often ignored in measurement invariance testing. This study examined the impact of ignoring cross-loadings on the sensitivity of fit measures (CFI, RMSEA, SRMR, SRMRu, AIC, BIC, SaBIC, LRT) to measurement noninvariance. The manipulated design factors included 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…

This study is aimed at validating the French version of the Effort-Reward Imbalance Questionnaire for Teachers. The instrument was pretested before being administered in a large-scale study with elementary school teachers. Dimensionality of the instrument was examined using the bifactor exploratory structural equation modeling (ESEM) framework.…

Chi-square tests based on maximum likelihood (ML) estimation of covariance structures often incorrectly over-reject the null hypothesis: [sigma] = [sigma(theta)] when the sample size is small. Reweighted least squares (RLS) avoids this problem. In some models, the vector of parameter must contain means, variances, and covariances, yet whether RLS…

Bayesian structural equation modeling (BSEM) is a flexible tool for the exploration and estimation of sparse factor loading structures; that is, most cross-loading entries are zero and only a few important cross-loadings are nonzero. The current investigation was focused on the BSEM with small-variance normal distribution priors (BSEM-N) for both…

This study offers methodological synergy in the examination of factorial structure in second language (L2) research. It illustrates the effectiveness and flexibility of the recently developed exploratory structural equation modeling (ESEM) method, which integrates the advantages of exploratory factor analysis (EFA) and confirmatory factor analysis…

The aim of this study was to assess the reliability and validity of ERQ in a group of Egyptian adolescents. 648 adolescents from middle schools in Nasr city, Egypt were recruited. These adolescents aged 14-15 years old (M=14.4, SD=2.22). Of which,400 were females (61.72%) while 248 were males (38.27%). Exploratory Factor Analysis, with CFA and…

In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present…

One of the aims of the current study is to specify the model providing the best fit to the data among the exploratory, the bifactor exploratory and the confirmatory structural equation models. The study compares the three models based on the model data fit statistics and item parameter estimations (factor loadings, cross-loadings, factor…

In Bayesian structural equation modeling (BSEM), prior settings may affect model fit, parameter estimation, and model comparison. This simulation study was to investigate how the priors impact evaluation of relative fit across competing models. The design factors for data generation included sample sizes, factor structures, data distributions, and…

Bayesian structural equation modeling (BSEM) was used to investigate the latent structure of the Differential Ability Scales-Second Edition core battery using the standardization sample normative data for ages 7-17. Results revealed plausibility of a three-factor model, consistent with publisher theory, expressed as either a higher-order (HO) or a…

The aim of the present study was to determine the factor structure of the Positive Life Assets Scale (PLAS), a new measure to identify both internal and external life assets among high school students in Thailand, and to further examine the usefulness of the PLAS for a comprehensive, developmental, and strengths-based school and community…