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…

In this study, the purpose is to compare factor retention methods under simulation conditions. For this purpose, simulations conditions with a number of factors (1, 2 [simple]), sample sizes (250, 1.000, and 3.000), number of items (20, 30), average factor loading (0.50, 0.70), and correlation matrix (Pearson Product Moment [PPM] and Tetrachoric)…

This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure. Binary data were generated to compare the performance of maximum likelihood (ML), mean and variance…

Factor structure of the Reactions to Tests (RTT) scale measuring test anxiety was studied by testing a series of confirmatory factor models including a second-order structure with 636 college students. Results support a shorter 20-item RTT but also raise questions about the cross-validation of covariance models. (SLD)