Abstract
Factor analysis is a branch of analysis of variance used to investigate the structure of a data set. Consider a data set x ij 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.
Keywords
- Maximum Likelihood Approach
- Sample Covariance Matrix
- Estimate Confidence Interval
- Stock Market Price
- Linear Cluster
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This chapter was originally published in The New Palgrave: A Dictionary of Economics, 1st edition, 1987. Edited by John Eatwell, Murray Milgate and Peter Newman
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References
Adelman, I., and C.T. Morris. 1967. Society, politics, and economic development: A quantitative approach. Baltimore: Johns Hopkins Press.
Aigner, D.J., and A.S. Goldberger (eds.). 1977. Latent variables in socioeconomic models. Amsterdam: North-Holland.
Anderson, T.W. 1958. An introduction to multivariate statistical analysis. New York: Wiley.
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.
Bartlett, M.S. 1938. Methods of estimating mental factors. Nature 141: 609–610.
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.)
Fletcher, R., and M.J.D. Powell. 1963. A rapidly convergent descent method for minimization. Computer Journal 6: 163–168.
Geary, R.C. 1948. Studies in relation between economic time series. Journal of the Royal Statistical Society, Series B 10: 140–158.
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.
Harman, H.H. 1960. Modern factor analysis, 3rd ed, revised. Chicago: University of Chicago Press, 1976.
Hotelling, H. 1933. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24: 417–441, 498–520.
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.
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.
Jøreskog, K.G. 1963. Statistical estimation in factor analysis: A New technique and its foundation. Stockholm: Almqvist & Wiksell.
Jøreskog, K.G. 1967. Some contributions to maximum likelihood factor analysis. Psychometrika 32: 443–482.
Jøreskog, K.G. 1984. Advances in factor analysis and structural equation models. Lanham: University Press of America.
Jøreskog, K.G., and A.S. Goldberger. 1972. Factor analysis by generalized least squares. Psychometrika 37: 243–260.
Kaiser, H.F. 1958. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23: 187–200.
King, B. 1966. Market and industry factors in stock price behavior. Journal of Business 39(Supplement): 139–190.
Kruskal, J.B. 1978. Factor analysis: Bilinear methods. In International encyclopedia of statistics, 307–330. New York: Macmillan.
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.
Lawley, D.N., and A.E. Maxwell. 1963. Factor analysis as a statistical method, 2nd ed. London: Butterworth, 1971.
McDonald, R.P. 1967. Factor interaction in nonlinear factor analysis. British Journal of Mathematical and Statistical Psychology 20: 205–215.
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.
Schilderinck, J.H.F. 1969. Factor analysis applied to developed and developing countries. Rotterdam: Rotterdam University Press.
Spearman, C.E. 1904. ‘General intelligence’ objectively determined and measured. American Journal of Psychology 15: 201–293.
Stone, R. 1945. The analysis of market demand. Journal of the Royal Statistical Society, Series A 108: 286–382.
Stone, R. 1947. On the interdependence of blocks of transactions. Journal of Royal Statistical Society, Series B 9: 1–45.
Thurstone, L.L. 1935. The vectors of mind: Multiple-factor analysis for the isolation of primary traits. Chicago: University of Chicago Press.
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.
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Adelman, I. (1987). Factor Analysis. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95121-5_72-1
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DOI: https://doi.org/10.1057/978-1-349-95121-5_72-1
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