The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Factor Analysis

  • Irma Adelman
Reference work entry


Factor analysis is a branch of analysis of variance used to investigate the structure of a data set. Consider a data set xij resulting from the observation of several variables j on several objects i. If the data set arises from a complex multidimensional process about which little is known a priori statistical analysis of the data itself might profitably be used to gain insights into various characteristics of the processes which generated the data set. In particular, statistical techniques can be used to: (1) search for a simpler representation of the underlying processes which generated the data by reducing the dimension of the variable space in which the objects are represented; (2) look for the interactions among the variables by forming linear clusters of variables; and (3) seek characterizations of the clusters of variables which relate them to the underlying processes which generated the data set being analysed. Factor analysis performs all three functions.

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  1. Adelman, I., and C.T. Morris. 1967. Society, politics, and economic development: A quantitative approach. Baltimore: Johns Hopkins Press.Google Scholar
  2. Aigner, D.J., and A.S. Goldberger (eds.). 1977. Latent variables in socioeconomic models. Amsterdam: North-Holland.Google Scholar
  3. Anderson, T.W. 1958. An introduction to multivariate statistical analysis. New York: Wiley.Google Scholar
  4. Banks, C. 1954. The factorial analysis of crop productivity: A reexamination of professor Kendall’s data. Journal of the Royal Statistical Society, Series B 16: 100–111.Google Scholar
  5. Bartlett, M.S. 1938. Methods of estimating mental factors. Nature 141: 609–610.Google Scholar
  6. Bolton, B., Hinman, S., and Tuft, S. 1973. Annotated bibliography: Factor analytic studies 1941–1970, 4 vols. Fayetteville: University of Arkansas, Arkansas Rehabilitation Research and Training Center. (Tuft did not collaborate on vols 3 and 4.)Google Scholar
  7. Fletcher, R., and M.J.D. Powell. 1963. A rapidly convergent descent method for minimization. Computer Journal 6: 163–168.CrossRefGoogle Scholar
  8. Geary, R.C. 1948. Studies in relation between economic time series. Journal of the Royal Statistical Society, Series B 10: 140–158.Google Scholar
  9. Geweke, J. 1977. The dynamic factor analysis of economic time-series models. In Latent variables in socioeconomic models, ed. D.J. Aigner and A.S. Goldberger. Amsterdam: North-Holland.Google Scholar
  10. Harman, H.H. 1960. Modern factor analysis, 3rd ed, revised. Chicago: University of Chicago Press, 1976.Google Scholar
  11. Hotelling, H. 1933. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24: 417–441, 498–520.CrossRefGoogle Scholar
  12. Huang, C.-L., Raunika, R., and Fletcher, S.M. 1980. Estimation of demand parameters based on factor analysis. Paper presented at the American Agricultural Economics Association Meetings in Urbana, Illinois.Google Scholar
  13. Jennrich, R.I., and D.T. Thayer. 1973. A note on Lawley’s formulas for standard errors in maximum likelihood factor analysis. Psychometrika 38: 571–580.CrossRefGoogle Scholar
  14. Jøreskog, K.G. 1963. Statistical estimation in factor analysis: A New technique and its foundation. Stockholm: Almqvist & Wiksell.Google Scholar
  15. Jøreskog, K.G. 1967. Some contributions to maximum likelihood factor analysis. Psychometrika 32: 443–482.CrossRefGoogle Scholar
  16. Jøreskog, K.G. 1984. Advances in factor analysis and structural equation models. Lanham: University Press of America.Google Scholar
  17. Jøreskog, K.G., and A.S. Goldberger. 1972. Factor analysis by generalized least squares. Psychometrika 37: 243–260.CrossRefGoogle Scholar
  18. Kaiser, H.F. 1958. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23: 187–200.CrossRefGoogle Scholar
  19. King, B. 1966. Market and industry factors in stock price behavior. Journal of Business 39(Supplement): 139–190.CrossRefGoogle Scholar
  20. Kruskal, J.B. 1978. Factor analysis: Bilinear methods. In International encyclopedia of statistics, 307–330. New York: Macmillan.Google Scholar
  21. Lawley, D.N. 1940. The estimation of factor loadings by the method of maximum likelihood. Royal Society of Edinburgh, Section A, Proceedings 60: 64–82.CrossRefGoogle Scholar
  22. Lawley, D.N., and A.E. Maxwell. 1963. Factor analysis as a statistical method, 2nd ed. London: Butterworth, 1971.Google Scholar
  23. McDonald, R.P. 1967. Factor interaction in nonlinear factor analysis. British Journal of Mathematical and Statistical Psychology 20: 205–215.CrossRefGoogle Scholar
  24. Rayner, A.C. 1970. The use of multivariate analysis in development theory: A critique of the approach used by Adelman and Morris. Quarterly Journal of Economics 84: 639–647.CrossRefGoogle Scholar
  25. Schilderinck, J.H.F. 1969. Factor analysis applied to developed and developing countries. Rotterdam: Rotterdam University Press.Google Scholar
  26. Spearman, C.E. 1904. ‘General intelligence’ objectively determined and measured. American Journal of Psychology 15: 201–293.CrossRefGoogle Scholar
  27. Stone, R. 1945. The analysis of market demand. Journal of the Royal Statistical Society, Series A 108: 286–382.CrossRefGoogle Scholar
  28. Stone, R. 1947. On the interdependence of blocks of transactions. Journal of Royal Statistical Society, Series B 9: 1–45.Google Scholar
  29. Thurstone, L.L. 1935. The vectors of mind: Multiple-factor analysis for the isolation of primary traits. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  30. Wold, H. 1982. Soft modeling and some extensions. In Systems under indirect observation, vol. II, ed. K.G. Jøreskog and H. Wold, 1–54. Amsterdam: North-Holland.Google Scholar

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© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Irma Adelman
    • 1
  1. 1.