Determining the Number of Factors to Retain in EFA: Using the SPSS R-Menu v2 0 to Make More Judicious Estimations

Exploratory factor analysis (EFA) is a common technique utilized in the development of assessment instruments. The key question when performing this procedure is how to best estimate the number of factors to retain. This is especially important as underor over-extraction may lead to erroneous conclusions. Although recent advancements have been made to answer the number of factors question, popular statistical packages do not come standard with these modern techniques. This paper details how to program IBM SPSS Statistics software (SPSS) to conveniently perform five modern techniques designed to estimate the number of factors to retain. By utilizing the five empirically-supported techniques illustrated in this article, researchers will be able to more judiciously model data.

[1]  F. Holgado-Tello,et al.  Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables , 2008 .

[2]  J. Ruscio,et al.  Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure. , 2012, Psychological assessment.

[3]  Francisco Pablo Holgado Tello,et al.  Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables , 2010 .

[4]  Vicente Ponsoda,et al.  Performance of Velicer’s Minimum Average Partial Factor Retention Method With Categorical Variables , 2011 .

[5]  Theodore A. Walls,et al.  Non-Graphical Solutions for Cattell’s Scree Test , 2013 .

[6]  D. Flora,et al.  An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. , 2004, Psychological methods.

[7]  Ulf Olsson,et al.  Maximum likelihood estimation of the polychoric correlation coefficient , 1979 .

[8]  Francisco José Abad,et al.  A new look at Horn's parallel analysis with ordinal variables. , 2013, Psychological methods.

[9]  W Revelle,et al.  Very Simple Structure: An Alternative Procedure For Estimating The Optimal Number Of Interpretable Factors. , 1979, Multivariate behavioral research.

[10]  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.

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

[12]  Wayne F. Velicer,et al.  Construct Explication through Factor or Component Analysis: A Review and Evaluation of Alternative Procedures for Determining the Number of Factors or Components , 2000 .

[13]  Peter M. Bentler,et al.  EQS : structural equations program manual , 1989 .

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

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

[16]  L. A. Goodman,et al.  Measures of association for cross classifications , 1979 .

[17]  Emin Babakus,et al.  The Sensitivity of Confirmatory Maximum Likelihood Factor Analysis to Violations of Measurement Scale and Distributional Assumptions , 1987 .

[18]  Yasuo Amemiya,et al.  Factor Analysis at 100:Historical Developments and Future Directions , 2003, Multivariate behavioral research.

[19]  R. Henson,et al.  Use of Exploratory Factor Analysis in Published Research , 2006 .

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

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

[22]  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..

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

[24]  Mário Basto,et al.  An SPSS R-Menu for Ordinal Factor Analysis , 2012 .

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

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

[27]  Pedro M. Valero-Mora,et al.  Determining the Number of Factors to Retain in EFA: An easy-to-use computer program for carrying out Parallel Analysis , 2007 .

[28]  K. Bollen,et al.  Pearson's R and Coarsely Categorized Measures , 1981 .