Parallel analysis
Parallel analysis, also known as Horn's parallel analysis, is a statistical method used to determine the number of components to keep in a principal component analysis or factors to keep in an exploratory factor analysis. It is named after psychologist John L. Horn, who created the method in 1965.[1] The method compares the eigenvalues generated from the data matrix to the eigenvalues generated from a Monte-Carlo simulated matrix created from random data of the same size.[2]
Other methods of determining the number of factors or components to retain in an analysis include the scree plot, Kaiser rule, or Velicer's (1976) MAP test.[3]
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 discrimination, and type of correlation coefficient.[4]
See also
References
- ^ Horn, John L. (June 1965). "A rationale and test for the number of factors in factor analysis". Psychometrika. 30 (2): 179–185. doi:10.1007/bf02289447. PMID 14306381.
- ^ Mike Allen (11 April 2017). The SAGE Encyclopedia of Communication Research Methods. SAGE Publications. p. 518. ISBN 978-1-4833-8142-8.
- ^ Velicer, W.F. (1976). "Determining the number of components from the matrix of partial correlations". Psychometrika. 41 (3): 321–327. doi:10.1007/bf02293557.
- ^ Tran, U. S.; Formann, A. K. (2009). "Performance of parallel analysis in retrieving unidimensionality in the presence of binary data". Educational and Psychological Measurement. 69: 50–61. doi:10.1177/0013164408318761.
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