Partitioning Dichotomous Items Using Mokken Scale Analysis, Exploratory Graph Analysis and Parallel Analysis: A Monte Carlo Simulation

Authors

  • Gomaa Said Mohamed Abdelhamid Orcid
    Department of Educational Psychology, Faculty of Education, Fayoum University, Fayoum, Egypt; Department of Psychology, College of Education, Sultan Qaboos University, Muscat, Oman
  • María Dolores Hidalgo Orcid
    Department of Basic Psychology and Methodology, Faculty of Psychology, University of Murcia, Murcia, Spain
  • Brian F. French Orcid
    Department of Kinesiology and Educational Psychology, Washington State University, Pullman, WA, USA
  • Juana Gómez-Benito Orcid
    Department of Social Psychology and Quantitative Psychology, Faculty of Psychology, University of Barcelona, Barcelona, Spain; Group on Measurement Invariance and Analysis of Change (GEIMAC), Institute of Neuroscience, University of Barcelona, Barcelona, Spain

Abstract

Estimating the number of latent factors underlying a set of dichotomous items is a major challenge in social and behavioral research. Mokken scale analysis (MSA) and exploratory graph analysis (EGA) are approaches for partitioning measures consisting of dichotomous items. In this study we perform simulation-based comparisons of two EGA methods (EGA with graphical least absolute shrinkage and selector operator; EGAtmfg with triangulated maximally filtered graph algorithm), two MSA methods (AISP: automated item selection procedure; GA: genetic algorithm), and two widely used factor analytic techniques (parallel analysis with principal component analysis (PApc) and parallel analysis with principal axis factoring (PApaf)) for partitioning dichotomous items. Performance of the six methods differed significantly according to the data structure. AISP and PApc had highest accuracy and lowest bias for unidimensional structures. Moreover, AISP demonstrated the lowest rate of misclassification of items. Regarding multidimensional structures, EGA with GLASSO estimation and PApaf yielded highest accuracy and lowest bias, followed by EGAtmfg. In addition, both EGA techniques exhibited the lowest rate of misclassification of items to factors. In summary, EGA and EGAtmfg showed comparable performance to the highly accurate traditional method, parallel analysis. These findings offer guidance on selecting methods for dimensionality analysis with dichotomous indicators to optimize accuracy in factor identification.