Factor analysis is widely utilized to identify latent factors underlying the observed variables. This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis, the K1 rule, and parallel analysis, along with a more recently developed method, the bass-ackward method.…

Determining the number of factors is a critical first step in exploratory factor analysis. Although various criteria and methods for determining the number of factors have been evaluated in the usual between-subjects R-technique factor analysis, there is still question of how these methods perform in within-subjects P-technique factor analysis. A…

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…

This study compares the performance of two approaches in analysing four-point Likert rating scales with a factorial model: the classical factor analysis (FA) and the item factor analysis (IFA). For FA, maximum likelihood and weighted least squares estimations using Pearson correlation matrices among items are compared. For IFA, diagonally weighted…

In this descriptive exploratory study, the Posttraumatic Growth Inventory (PTGI; Tedeschi & Calhoun, 1996) was used to measure levels of personal growth attributed by college students (N = 117) to a semester of university life in retrospective self-reports. Results reflect attributions of substantial total growth in the range reported in the…

This study assessed the accuracy of parallel analysis, a technique in which observed eigenvalues are compared to eigenvalues from simulated data when no real factors are present. Three studies with manipulated sizes of real factors and sample sizes illustrate the importance of modeling the data more closely when parallel analysis is used. (SLD)