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 or Kaiser rule.
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.
- ^ Mike Allen (11 April 2017). The SAGE Encyclopedia of Communication Research Methods. SAGE Publications. p. 518. ISBN 978-1-4833-8142-8.
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