Determining the Number of Factors to Retain in EFA: An easy-to-use computer program for carrying out Parallel Analysis

Parallel Analysis is a Monte Carlo simulation technique that aids researchers in determining the number of factors to retain in Principal Component and Exploratory Factor Analysis. This method provides a superior alternative to other techniques that are commonly used for the same purpose, such as the Scree test or the Kaiser’s eigenvalue-greater-than-one rule. Nevertheless, Parallel Analysis is not well known among researchers, in part because it is not included as an analysis option in the most popular statistical packages. This paper describes and illustrates how to apply Parallel Analysis with an easy-to-use computer program called ViSta-PARAN. ViSta-PARAN is a user-friendly application that can compute and interpret Parallel Analysis. Its user interface is fully graphic and includes a dialog box to specify parameters, and specialized graphics to visualize the analysis output.

[1]  Michael Friendly,et al.  Visual Statistics: Seeing Data with Dynamic Interactive Graphics , 2006 .

[2]  Chung-Ping Cheng,et al.  Parallel Analysis with Unidimensional Binary Data , 2005 .

[3]  J. Osborne,et al.  Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. , 2005 .

[4]  Donald A. Jackson,et al.  How many principal components? stopping rules for determining the number of non-trivial axes revisited , 2005, Comput. Stat. Data Anal..

[5]  James C. Hayton,et al.  Factor Retention Decisions in Exploratory Factor Analysis: a Tutorial on Parallel Analysis , 2004 .

[6]  K B Keeling,et al.  A Regression Equation for Determining the Dimensionality of Data , 2000, Multivariate behavioral research.

[7]  B P O'Connor,et al.  SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[8]  J. Tanguma.  Determining the Number of Factors To Retain. , 2000 .

[9]  Duane T. Wegener,et al.  Evaluating the use of exploratory factor analysis in psychological research. , 1999 .

[10]  R. Gorsuch,et al.  Effects of under- and overextraction on principal axis factor analysis with varimax rotation. , 1996 .

[11]  B. Thompson,et al.  Factor Analytic Evidence for the Construct Validity of Scores: A Historical Overview and Some Guidelines , 1996 .

[12]  Louis W. Glorfeld.  An Improvement on Horn's Parallel Analysis Methodology for Selecting the Correct Number of Factors to Retain , 1995 .

[13]  R. Stewart Longman,et al.  Interpolating 95Th Percentile Eigenvalues from Random Data: An Empirical Example , 1993 .

[14]  A. Buja,et al.  Remarks on Parallel Analysis. , 1992, Multivariate behavioral research.

[15]  Rick L. Edgeman,et al.  LISP-STAT: An Object-Oriented Environment for Statistical Computing and Dynamic Graphics , 1992 .

[16]  R. Stewart Longman,et al.  PAM: A double-precision FORTRAN routine for the parallel analysis method in principal components analysis , 1989 .

[17]  G J Lautenschlager,et al.  A Comparison of Alternatives to Conducting Monte Carlo Analyses for Determining Parallel Analysis Criteria. , 1989, Multivariate behavioral research.

[18]  Charles E. Lance,et al.  Parallel Analysis Criteria: Revised Equations for Estimating the Latent Roots of Random Data Correlation Matrices , 1989 .

[19]  Raymond Hubbard,et al.  An empirical comparison of alternative methods for principal component extraction , 1987 .

[20]  W. Velicer,et al.  Comparison of five rules for determining the number of components to retain. , 1986 .

[21]  J. Horn,et al.  Cattell's Scree Test In Relation To Bartlett's Chi-Square Test And Other Observations On The Number Of Factors Problem. , 1979, Multivariate behavioral research.

[22]  Lloyd G. Humphreys,et al.  Latent roots of random data correlation matrices with squared multiple correlations on the diagonal: A monte carlo study , 1976 .

[23]  W. Velicer.  Determining the number of components from the matrix of partial correlations , 1976 .

[24]  Richard G. Montanelli,et al.  An Investigation of the Parallel Analysis Criterion for Determining the Number of Common Factors , 1975 .

[25]  R. Cattell.  The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.

[26]  J. Horn.  A rationale and test for the number of factors in factor analysis , 1965, Psychometrika.

[27]  H. Kaiser.  The Application of Electronic Computers to Factor Analysis , 1960 .

[28]  R S Longman,et al.  A Regression Equation for the Parallel Analysis Criterion in Principal Components Analysis: Mean and 95th Percentile Eigenvalues. , 1989, Multivariate behavioral research.

[29]  J D Kaufman,et al.  Determining the number of factors to retain: Q windows-based FORTRAN-IMSL program for parallel analysis , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.