Determining Parallel Analysis Criteria
By Marley W. Watkins. 2005
223 citations recorded by the source corpus.
Exploratory factor analysis is an important analytic tool for investigating test validity. Of all the decisions made in exploratory factor analysis, determining the number of factors to extract is perhaps the most critical because incorrect specification will obscure the factor structure (Cattell, 1978; Glorfeld, 1995; Goodwin & Goodwin, 1999). Although overextraction might be somewhat less serious than under-extraction (Wood, Tataryn, & Gorsuch, 1996), it has been empirically demonstrated that both have deleterious effects (Fava & Velicer, 1992, 1996). Many criteria for determining the number of factors to extract have been proposed (Benson & Nasser, 1998). Unfortunately, most are inaccurate guides to practice (Kanyongo, 2005; Zwick & Velicer, 1986). Based upon current simulation research (Velicer, Eaton, & Fava, 2000; Zwick & Velicer, 1986), only two methods have consistently emerged as accurate: the Parallel Analysis (PA) method of Horn (1965) and the Minimum Average Partial (MAP) method of Velicer (1976).
Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/5bf0b60f676a4c795263105b15288d118be7bfa4
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In Computer Networks and Communications
In Management Science and Operations Research
In Statistics and Probability
Citations in the corpus
- Ten Steps in Scale Development and Reporting: A Guide for Researchers (2018, methodology): “html for a free download of Monte Carlo PCA for Parallel Analysis (Watkins, 2006).”