Parallel analysis: Difference between revisions

Revision as of 03:30, 12 March 2025

Revision as of 04:56, 12 March 2025

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[[Anton Formann]] provided both theoretical and empirical evidence that parallel analysis's application might not be appropriate in many cases since its performance is influenced by [[sample size]], [[Item response theory#The item response function|item discrimination]], and type of [[correlation coefficient]].<ref>{{cite journal | last1 = Tran | first1 = U. S. | last2 = Formann | first2 = A. K. | year = 2009 | title = Performance of parallel analysis in retrieving unidimensionality in the presence of binary data | journal = Educational and Psychological Measurement | volume = 69 | pages = 50–61 | doi = 10.1177/0013164408318761 | s2cid = 143051337 }}</ref>

An extensive 2022 simulation study by Haslbeck and van Bork<ref>{{Cite journal |last=Haslbeck |first=Jonas M. B. |last2=van Bork |first2=Riet |date=February 2024-02 |title=Estimating the number of factors in exploratory factor analysis via out-of-sample prediction errors. |url=https://doi.apa.org/doi/10.1037/met0000528 |journal=Psychological Methods |language=en |volume=29 |issue=1 |pages=48–64 |doi=10.1037/met0000528 |issn=1939-1463}}</ref> found that parallel analysis was among the best-performing existing methods, but was slightly outperformed by their proposed prediction error-based approach.

==Implementation==

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[[Category:Multivariate statistics]]

[[Category:Factor analysis]]

{{statistics-stub}}