Developments in the factor analysis of individual time series

Factor Analysis at 100: Historical Developments and Future Directions

Volumn , Issue , 2012, Pages 265-292

  Browne, Michael W ^a   Zhang, Guangjian ^a,b  

^a OHIO STATE UNIVERSITY   (United States)

^b Notre Dame ^*  (France)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84925778534     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9780203936764     Document Type: Chapter

References (25)

1. Anderson, T. W. (1963). The use of factor analysis in the statistical analysis of time series. Psychometrika, 28, 1-25.
2. Borkenau, P., & Ostendorf, F. (1998). The Big Five as states: How useful is the five factor model to describe intraindividual variations over time? Journal of Research in Personality, 32, 202-221.
3. Briggs, N. E., & MacCallum, R. C. (2003). Recovery of weak common factors by maximum likelihood and ordinary least squares estimation. Multivariate Behavioral Research, 38, 25-56.
4. Brockwell, P. J., & Davis, R. A. (1991). Time series: Theory and methods (2nd ed.). New York: Springer.
5. Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111-150.
6. Browne, M. W., & Nesselroade, J. R. (2005) Representing psychological processes with dynamic factor models: Some promising uses and extensions of ARMA time series models. In A. Maydeu-Olivares & J. J. McArdle (Eds.), Advances in psychometrics: A festschrift to Roderick P. McDonald (pp. 415-451). Mahwah, NJ: Lawrence Erlbaum Associates.
7. Browne, M. W., & Zhang, G. (2005). DyFA: Dynamic factor analysis of lagged correlation matrices, version 2.03. [WWW document and computer program]. Available from http://faculty.psy.ohio-state.edu/browne/
8. Cattell, R. B., Cattell, A. K. S., & Rhymer, R. M. (1947). P-technique demonstrated in determining psycho-physiological source traits in a normal individual. Psychometrika, 12, 267-288.
9. du Toit, S. H. C., & Browne, M. W. (2001). The covariance structure of a vector ARMA time series. In R. Cudeck, S. H. C. du Toit, & D. Sorbom (Eds.), Structural equation modeling: Present and future (pp. 279-314). Chicago: Scientific Software International, Inc.
10. Engle, R., & Watson, M. (1981). A one-factor multivariate time series model of metropolitan wage rates. Journal of the American Statistical Association, 76, 774-781.
11. Geweke, J., & Singleton, K. (1981). Maximum likelihood confirmatory factor analysis of economic time series, International Economic Review, 22, 37-54.
12. Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. M. (2005). Statistical modeling of the individual: Rationale and application of multivariate stationary time series analysis. Multivariate Behavioral Research, 40, 207-234.
13. Hamilton, J. D. (1994). Time series analysis. Princeton, NJ: Princeton University Press.
14. Harville, D. A. (1997). Matrix algebra from a statistician's perspective. New York: Springer-Verlag.
15. Holtzman, W. (1963). Statistical models for the study of change in the single case. In C. W. Harris (Ed.), Problems in measuring change (pp. 199-211). Madison: The University of Wisconsin.
16. Immink, W. (1986). Parameter estimation in Markov models and dynamic factor analysis. Unpublished doctoral dissertation, University of Utrecht.
17. Jones, C. J., & Nesselroade, J. R. (1990). Multivariate, replicated, single-subject, repeated measures designs and P-technique factor analysis: A review of intraindividual change studies. Experimental Aging Research, 16, 171-183.
18. Joreskog, K. G. (1977). Factor analysis by least squares and maximum likelihood methods. In K. Enslein, R. Ralston, & S. W. Wilf (Eds.), Statistical methods for digital computers (Vol. 3, pp. 125-153). New York: Wiley.
19. Luborsky, L., & Mintz, J. (1972). The contribution of P-technique to personality, psychotherapy, and psychosomatic research. In R. M. Dreger (Ed.), Multivariate personality research: Contributions to the understanding of personality in honor of Raymond B. Cattell (p. 387-410). Baton Rouge, LA: Claitor's Publishing Division.
20. Lutkepohl, H. (1993). Introduction to multiple time series analysis (2nd. ed.). Berlin: Springer-Verlag.
21. MacCallum, R. C., Tucker, L. R., & Briggs, N. E. (2001). An alternative perspective on parameter estimation in factor analysis and related methods. In R. Cudeck, S. du Toit, & D. Sorbom (Eds.), Structural equation modeling: Present and future (pp. 39-57). Lincolnwood, IL: SSI.
22. Molenaar, P. C. M. (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50, 181-202.
23. Nesselroade, J. R., McArdle, J. J., Aggen, S. H., & Meyers, J. M. (2002). Alternating dynamic factor models of multivariate time-series analysis. In D. M. Moscowitz & S. L. Hershberger (Eds.), Modeling intraindividual variabilility with repeated measures data (pp. 235-265). Mahwah, NJ: Lawrence Erlbaum Associates.
24. Wei, W. S. (1990). Time series analysis: Univariate and multivariate methods. New York: Addison-Wesley.
25. Whittle, P. (1963). On the fitting of multivariate autoregressions and the approximate canonical factorization of a special density matrix. Biometrika, 40, 129-134.