ÇARPIK DAĞILIMLARDA FAKTÖR SAYISI BELİRLEME YÖNTEMLERİNİN PERFORMANSLARININ İNCELENMESİ

Abstract

This research aims to evaluate the performance of dimensionality determination methods under various simulation conditions. Therefore, dimensionality determination methods were compared, including optimal parallel analysis, MAP, HULL, EGA (TMFG) estimation, EGA (glasso) estimation, and comparison data forest method. The type of distribution, sample size, number of items per factor, number of categories, and measurement model were specified as simulation conditions in the study. For each condition, 100 replications were conducted. A fully crossed simulation design was employed in the study. The results of this study, which examined the performance of factor determination methods under skewed distributions, indicated that the HULL method had the highest average considering the average accuracy values of all conditions. Meanwhile, the HULL method had the lowest RB average. However, no method demonstrated adequate performance under all conditions. This study examined one-factor and two-factor structures with interfactor correlations of 0.00 and 0.30. Considering structures with more than two factors in education and psychology, future research could focus on working with data exhibiting skewed distributions involving more factors and items to compare the performance of methods.