Reassessment of innovative methods to determine the number of factors: A simulation-based comparison of exploratory graph analysis and next eigenvalue sufficiency test. - PsycNET

 Reassessment of innovative methods to determine the number of factors: A simulation-based comparison of exploratory graph analysis and next eigenvalue sufficiency test. - PsycNET  https://psycnet.apa.org/doiLanding?doi=10.1037%2Fmet0000527Brandenburg, N.,& Papenberg, M. (2022). Reassessment of innovative methods to determine the number of factors: A simulation-based comparison of exploratory graph analysis and next eigenvalue sufficiency test. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000527Next Eigenvalue Sufficiency Test (NEST; Achim, 2017) is a recently proposed method to determine the number of factors in exploratory factor analysis (EFA). NEST sequentially tests the null-hypothesis that k factors are sufficient to model correlations among observed variables. Another recent approach to detect factors is exploratory graph analysis (EGA; Golino& Epskamp, 2017), which rules the number of factors equal to the number of nonoverlapping communities in a graphical network model of observed correlations. We applied NEST and EGA to data sets under simulated factor models with known numbers of factors and scored their accuracy in retrieving this number. Specifically, we aimed to investigate the effects of cross-loadings on the performance of NEST and EGA. In the first study, we show that NEST and EGA performed less accurately in the presence of cross-loadings on two factors compared with factor models without cro...
Source: Intelligent Insights on Intelligence Theories and Tests (aka IQ's Corner) - Category: Neuroscience Source Type: blogs