Emeritus Professor of Psychology and Statistics
Michael Browne obtained his BA (1958) and M.Sc. (1965) degrees from the University of the Witwatersrand and his Ph. D. (1969) from the University of South Africa. He was employed at the South African National Institute for Personnel Research from 1959 to 1973, ending as Chief Research Officer, and was Professor of Statistics at the University of South Africa from 1973 to 1990. During this time he spent several years on sabbatical in the USA, being a Visiting Research Fellow in Frederic Lord's group at ETS in 1966, 1967 and 1972, Visiting Scholar at UCLA in 1980 and Hill Visiting Professor at the University of Minnesota in 1988.
President: South African Statistical Association (1978)
President: Psychometric Society (1992)
President: Society for Multivariate Experimental Psychology (1999)
Associate Editor Psychometrika (1988-1993)
South African Statistical Association: Fellow (1979)
Society for Multivariate Experimental Psychology: Sells Award for distinguished lifetime achievement in multivariate experimental psychology. (2003)
Psychometric Society: Lifetime Career Achievement Award
Michael Browne's research has been primarily concerned with statistical modeling of multivariate psychological data. He has made contributions on asymptotically distribution free estimation in the analysis of moment structures, asymptotic robustness of multivariate normal theory against violation of assumptions, multiplicative models for multitrait-multimethod data, circumplex models for the investigating the personality circle, nonlinear latent curve models, and rotational methodology for factor analysis.
His main current interests are in multivariate time series and dynamic factor analysis (Du Toit & Browne, 2001; Browne & Nesselroade, 2005; Browne & Zhang, 2007a; Browne & Zhang, 2007b).
Software for methodology developed in the course of research is available. Click here for more information..
Browne, M. W. (1974). Generalized least squares estimators in the analysis of covariance structures. South African Statistical Journal, 8, 1–24
Browne, M. W. (1984). The decomposition of multitrait-multimethod matrices. British Journal of Mathematical and Statistical Psychology, 37, 1– 21
Browne, M. W. (1984) Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62–83
Browne, M. W. & Shapiro, A. (1988). Robustness of normal theory methods in the analysis of linear latent variate models. British Journal of Mathematical and Statistical Psychology, 41, 193–208
Browne, M. W. (1992). Circumplex models for correlation matrices, Psychometrika, 57, 469–497
Browne, M.W. (1993). Structured latent curve models. In C. M. Cuadras & C. R. Rao (Eds.), Multivariate analysis: Future directions 2 (pp. 171-198). Amsterdam: North-Holland.
Browne, M. W. & Cudeck, R. (1993). Alternative ways of assessing model fit. In: Bollen, K. A. & Long, J. S. (Eds.) Testing Structural Equation Models. pp. 136–162. Beverly Hills, CA: Sage
Browne, M. W. (2000) Cross-validation methods. Journal of Mathematical Psychology, 44,108–132.
Browne, M. W., (2001) An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111–150
Chicago: Scientific Software International Inc. Browne, M.W., MacCallum, R.C., Kim, C-T., Andersen, B.L. & Glaser, R.(2002) When fit indices and residuals are incompatible. Psychological Methods, 7, 403–421.
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: Erlbaum.
Browne, M. W. & Zhang, G. (2007a). Developments in the Factor Analysis of Individual Time Series. In R. Cudeck & R. C. MacCallum (Eds.), Factor Analysis at 100: Historical Developments and Future Directions. Mahwah, NJ: Erlbaum.
Browne, M. W. & Zhang, G. (2007b). Repeated time series models for learning data. In S. M. Boker & M. J. Wenger (Eds.), Data Analytic Techniques for Dynamical Systems in the Social and Behavioral Sciences, Mahwah, NJ: Erlbaum.
Du Toit, S.H.C. & Browne, M.W (2007). Structural 3equation of modeling of multivariate time series. Multivariate Behavioral Research, 42, 67-101