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…

Determining the number of factors in exploratory factor analysis (EFA) is crucial because it affects the rest of the analysis and the conclusions of the study. Researchers have developed various methods for deciding the number of factors to retain in EFA, but this remains one of the most difficult decisions in the EFA. The purpose of this study is…

Factor analysis is a statistical method to explore the relationships among observed variables and identify latent structures. It is crucial in scale development and validity analysis. Key factors affecting the accuracy of factor analysis results include the type of data, sample size, and the number of response categories. While some studies…

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…

The instrument that assesses teachers' competence on AR methodology is limited. Thus, it is one of the issues concerning evaluating the effectiveness of a professional development program on designing AR projects. It is difficult to determine how much and what teachers have learned in a course or training. Thus, this cross-sectional study aimed to…

Investigation of measurement invariance (MI) commonly assumes correct specification of dimensionality across multiple groups. Although research shows that violation of the dimensionality assumption can cause bias in model parameter estimation for single-group analyses, little research on this issue has been conducted for multiple-group analyses.…

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…

Conventional differential item functioning (DIF) detection methods (e.g., the Mantel-Haenszel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable. True sources of DIF may include unobserved, latent variables, such as…

Temperament has a long history of scholarship dating back as early as 350 BC when Hippocrates (1984) associated body fluids or temperament with behavior. Temperament is broadly described as stylistic and relatively stable traits that subsume intrinsic tendencies to act and react in somewhat predictable ways to people, events, and other stimuli.…

This study examined the construct validity of the TOEFL iBT Speaking subsection for the purposes of international teaching assistant (ITA) certification, a purpose for which it was not specifically designed. The factor structure of the new TOEFL was compared with that of another language performance test in use at a major American research…

The Biggs' Study Process Questionnaire (SPQ) was used to test competing models of students' approaches to learning in a sample of undergraduate students (n = 125) from an inquiry based Bachelor of Health Sciences program. In addition to an internal consistency and test-retest reliability analysis of the SPQ, confirmatory factor analysis was used…

The nonnormed fit index's dependence on sample size in covariance structure analysis is discussed. Contrary to K. A. Bollen (1986) (whose alternative index depends on sample size), it is shown that the mean of the nonnormed fit index is independent of sample size for true and almost true models. (Author/TJH)

This study of maximum likelihood confirmatory factor analysis found effects of practical significance due to sample size, the number of indicators per factor, and the number of factors for Joreskog and Sorbom's (1981) goodness-of-fit index (GFI), GFI adjusted for degrees of freedom, and the root mean square residual. (Author/BW)

This study addressed which, if any, contemporary fit indices are least susceptible to the bias associated with confirmatory factor analysis (CFA) involving a large number of measured variables. Data were obtained from student responses from 1980 to 1990 on the Student Evaluations of Educational Quality (SEEQ) instrument of H. Marsh (1987). Factor…