Evaluation of parallel analysis methods for determining the number of factors

Aaron V. Crawford, Samuel B. Green, Roy Levy, Wen Juo Lo, Lietta Scott, Dubravka Svetina, Marilyn Thompson

Research output: Contribution to journalArticle

98 Scopus citations

Abstract

Population and sample simulation approaches were used to compare the performance of parallel analysis using principal component analysis (PA-PCA) and parallel analysis using principal axis factoring (PA-PAF) to identify the number of underlying factors. Additionally, the accuracies of the mean eigenvalue and the 95th percentile eigenvalue criteria were examined. The 95th percentile criterion was preferable for assessing the first eigenvalue using either extraction method. In assessing subsequent eigenvalues, PA-PCA tended to perform as well as or better than PA-PAF for models with one factor or multiple minimally correlated factors; the relative performance of the mean eigenvalue and the 95th percentile eigenvalue criteria depended on the number of variables per factor. PA-PAF using the mean eigenvalue criterion generally performed best if factors were more than minimally correlated or if one or more strong general factors as well as group factors were present.

Original languageEnglish (US)
Pages (from-to)885-901
Number of pages17
JournalEducational and Psychological Measurement
Volume70
Issue number6
DOIs
StatePublished - Dec 2010

Keywords

  • factor analysis
  • number of factors
  • parallel analysis

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology
  • Applied Psychology
  • Applied Mathematics

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