Home About Login Register Search Current Archive Announcement

Tutorial: Factor analysis revisited – An overview with the help of SPSS, SAS and R packages

Editor IJSMI


Numerous research articles and books published on Factor Analysis as it is widely applied in many of the disciplines such as Psychology & Behaviour sciences and marketing where more number of observed variables is used. Factor analysis is used to reduce the number of variables (which are correlated among them) by defining them into few factors which are linear combinations of the original variables. Factor Analysis also studies the underlying structure in the data set. Factor analysis [1,2] is introduced by Spearman a century ago[3, 2]. This paper provides an overview of Factor Analysis and how to conduct a Factor Analysis using SAS, SPSS and R statistical packages through a hypothetical data set.


Factor Analysis; Principal Component Analysis; Exploratory Factor Analysis; Confirmatory Factor Analysis; Method of Principal Component; Cattel’s method; Velicer’s method; Horn’s Parallel Analysis; Principal factor analysis; Canonical factor analysis; Alp

Full Text:



Holzinger, K. J., & Harman, H. H. (1941). Factor analysis; a synthesis of factorial methods.

Cartel, R. B. (1965). Factor analysis: An introduction to essential I. The purpose of underlying models. Biometrics, 21(1), 190-235.

Pearson, Karl. "Principal components analysis." The London, Edinburgh and Dublin Philosophical Magazine and Journal 6.2 (1901): 566.

Kane, H., & Brand, C. (1905). The importance of Spearman’s g. The occidental quarterly, 3(1), 7-30.

Joliffe, I. T., & Morgan, B. J. T. (1992). Principal component analysis and exploratory factor analysis. Statistical methods in medical research, 1(1), 69-95.

Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association.

Fabrigar, L. R., & Wegener, D. T. (2011). Exploratory factor analysis. Oxford University Press.


Lawley, D. N., & Maxwell, A. E. (1971). Factor analysis as a statistical method (Vol. 18). London: Butterworths.

Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and psychological measurement, 20(1), 141-151.

Cattell, R. B. (1966). The scree test for the number of factors. Multivariate behavioral research, 1(2), 245-276.

Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321-327.

Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179-185.

McDonald, R. P. (1970). The theoretical foundations of principal factor analysis, canonical factor analysis, and alpha factor analysis. British Journal of Mathematical and Statistical Psychology, 23(1), 1-21.

Jöreskog, K. G. (1967). A general approach to confirmatory maximum likelihood factor analysis. ETS Research Report Series, 1967(2), 183-202.

Jöreskog, K. G., & Sörbom, D. (1986). LISREL VI: Analysis of linear structural relationships by maximum likelihood, instrumental variables, and least squares methods. Scientific Software.

Jöreskog, K. G., & Goldberger, A. S. (1972). Factor analysis by generalized least squares. Psychometrika, 37(3), 243-260.

Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23(3), 187-200.

Neuhaus, J. O., & Wrigley, C. (1954). The quartimax method. British Journal of Statistical Psychology, 7(2), 81-91.

Kaiser, H. F. (1974). A note on the equamax criterion. Multivariate behavioral research, 9(4), 501-503.

Jennrich, R. I. (1979). Admissible values of γ in direct oblimin rotation. Psychometrika, 44(2), 173-177.

Jennrich, R. I., & Sampson, P. F. (1966). Rotation for simple loadings. Psychometrika, 31(3), 313-323.

Hendrickson, A. E., & White, P. O. (1964). Promax: A quick method for rotation to oblique simple structure. British journal of statistical psychology, 17(1), 65-70.

Costello, A. B. (2009). Getting the most from your analysis. Pan, 12(2), 131-146.

Mundfrom, D. J., Shaw, D. G., & Ke, T. L. (2005). Minimum sample size recommendations for conducting factor analyses. International Journal of Testing, 5(2), 159-168.

SPSS Inc. Released 2008. SPSS Statistics for Windows, Version 17.0. Chicago: SPSS Inc.

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.

Team, R. C. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013.

DOI: http://dx.doi.org/10.3000/ijsmi.v3i1.6

DOI (PDF1): http://dx.doi.org/10.3000/ijsmi.v3i1.6.g20

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.