#9088. Determining the Number of Factors When Population Models Can Be Closely Approximated by Parsimonious Models

September 2026publication date
Proposal available till 21-05-2025
4 total number of authors per manuscript3510 $

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Journal’s subject area:
Education;
Applied Mathematics;
Developmental and Educational Psychology;
Applied Psychology;
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Abstract:
Despite the existence of many methods for determining the number of factors, none outperforms the others under every condition. This study compares traditional parallel analysis (TPA), revised parallel analysis (RPA), Kaiser’s rule, minimum average partial, sequential ?2, and sequential root mean square error of approximation, comparative fit index, and Tucker–Lewis index under a realistic scenario in behavioral studies, where researchers employ a closing–fitting parsimonious model with K factors to approximate a population model, leading to a trivial model-data misfit.
Keywords:
dimensionality assessment; factor analysis; model–data fit; parallel analysis

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