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The Number of Factors Problem

Timmerman, M. E., Lorenzo-Seva, U. & Ceulemans, E., 16-Feb-2018, The Wiley Handbook of Psychometric Testing: A Multidisciplinary Reference on Survey, Scale and Test Development. Wiley, p. 305-324 20 p.

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DOI

This chapter focuses on formal criteria to assess the dimensionality for exploratory factor modelling with the aim to facilitate the selection of a proper criterion in empirical practice. It introduces the different foundations that underlie the various criteria and provides an overview of currently available formal criteria, which we selected on the basis of their popularity in empirical practice and/or proven effectiveness. The chapter successively reviews principal component analysis (PCA)‐based methods and common factor analysis (CFA)‐based methods to assess the number of common factors. To assess the number of factors underlying an empirical data set, the chapter suggests some strategies. It explains the finding in many studies that the Kaiser criterion clearly yields inaccurate indications of the number of PCs and common factors, mostly indicating too many factors. Minimum average partial (MAP) performances in indicating the number of major factors deteriorated when the unique variances increased, with no clear tendency to over‐ or underindicate the number of factors.
Original languageEnglish
Title of host publicationThe Wiley Handbook of Psychometric Testing: A Multidisciplinary Reference on Survey, Scale and Test Development
PublisherWiley
Chapter11
Pages305-324
Number of pages20
ISBN (Electronic)9781118489772, Paul Irwing, Tom Booth, David J. Hughes
ISBN (Print)9781118489833
Publication statusPublished - 16-Feb-2018

ID: 61406502