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All Journal ARMADA : Jurnal Penelitian Multidisiplin
Moh. Khairudin
Educational Research and Evaluation, Graduate School, Yogyakarta State University, Indonesia
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Which Factor Retention Method Performs Best? A Monte Carlo Simulation for Psychological and Educational Measurement Bhilan Rhiyu Antama; Edi Istiyono; Moh. Khairudin; Syukrul Hamdi; Heni Setiyaningsih
ARMADA : Jurnal Penelitian Multidisiplin Vol. 4 No. 9 (2026): ARMADA : Jurnal Penelitian Multidisplin, September 2026
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/armada.v4i9.3847

Abstract

Determining the appropriate number of factors is the most critical decision in exploratory factor analysis (EFA), but evidence-based guidance for the context of skewed ordinal data remains limited. This study compares the performance of three factor retention methods: optimal coordinates (OC), parallel analysis (PA), and exploratory graph analysis (EGA) through Monte Carlo simulations with 1000 replications using negatively skewed 5-category Likert scale data. Controlled conditions included the number of factors (2, 3, 5), sample size (300, 500, 900, 1500), factor loadings (0.4; 0.7), and inter-factor correlations (0.0; 0.3). The results show that PA achieved the highest accuracy (94.7%), followed by EGA (92.8%), while OC lagged far behind (36.8%). PA achieved perfect accuracy on large samples with high loadings, whereas OC exhibited a tendency toward under-extraction that increased drastically as the number of factors increased. EGA outperformed PA in minimizing over-extraction but was more prone to under-extraction in complex structures. These findings recommend PA as the primary method and EGA as a complementary method in Likert-scale-based research and confirm that algorithmic scree plots are not suitable for use as the sole criterion for factor retention.
Co-Authors Bhilan Rhiyu Antama Edi Istiyono Heni Setiyaningsih Syukrul Hamdi