Type I and Type II Error Rates and Overall Accuracy of the Revised Parallel Analysis Method for Determining the Number of Factors

Samuel B. Green, Marilyn Thompson, Roy Levy, Wen Juo Lo

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Traditional parallel analysis (T-PA) estimates the number of factors by sequentially comparing sample eigenvalues with eigenvalues for randomly generated data. Revised parallel analysis (R-PA) sequentially compares the kth eigenvalue for sample data to the kth eigenvalue for generated data sets, conditioned on k− 1 underlying factors. T-PA and R-PA are conceptualized as stepwise hypothesis-testing procedures and, thus, are alternatives to sequential likelihood ratio test (LRT) methods. We assessed the accuracy of T-PA, R-PA, and LRT methods using a Monte Carlo approach. Although no method was uniformly more accurate across all 180 conditions, the PA approaches outperformed LRT methods overall. Relative to T-PA, R-PA tended to perform better within the framework of hypothesis testing and to evidence greater accuracy in conditions with higher factor loadings.

Original languageEnglish (US)
Pages (from-to)428-457
Number of pages30
JournalEducational and Psychological Measurement
Volume75
Issue number3
DOIs
StatePublished - Jun 6 2015

Keywords

  • factor analysis
  • parallel analysis
  • revised parallel analysis

ASJC Scopus subject areas

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

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