Parallel analysis: Difference between revisions

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'''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.<ref>{{cite journal |last1=Horn |first1=John L. |title=A rationale and test for the number of factors in factor analysis |journal=Psychometrika |date=June 1965 |volume=30 |issue=2 |pages=179–185 |doi=10.1007/bf02289447 |pmid=14306381}}</ref> The method compares the [[eigenvalues]] generated from the data matrix to the eigenvalues generated from a [[Monte-Carlo simulation|Monte-Carlo simulated]] matrix created from random data of the same size.<ref name="Allen2017">{{cite book|author=Mike Allen|title=The SAGE Encyclopedia of Communication Research Methods|url=https://books.google.com/books?id=4GFCDgAAQBAJ&pg=PA518|date=11 April 2017|publisher=SAGE Publications|isbn=978-1-4833-8142-8|pages=518}}</ref>

Other methods of determining the number of factors or components to retain in an analysis include the [[scree plot]] or Kaiser rule.

[[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. ''| url = | journal = Educational and Psychological Measurement, | volume = 69,'' 50-61.| issue = | pages = 50–61 }}</ref>

==See also==