A Brief Biography: Robert M. Pruzek

Robert M. Pruzek received a B.S. from Wisconsin State University (River Falls) and an M.S. and Ph.D. from the University of Wisconsin (Madison).

He currently holds joint appointments at the State University of New York at Albany in Educational and Counseling Psychology, Division of Educational Psychology and Methodology, and the Department of Biometry and Statistics in the School of Public Health.

Dr. Pruzek’s interests include measurement, psychometric methods, research design and especially multivariate analysis and regression. He has taught at the University of Toronto, Ontario Institute for Studies in Education, the Free University of West Berlin and the University of Pittsburgh. He has been active in the American Educational Research Association, the Psychometric Society, and most recently in the Society for Multivariate Experimental Psychology. His recent work has concentrated on studying and developing modern approaches to regression and prediction, especially methods that employ comprehensive forms of data smoothing. Some of his work has entailed Bayesian approaches; a key concern has been to effectively ‘borrow strength’ to aid better predictions and stronger inference. Special interests in recent years concern graphical methods, computer-intensive methods, including the bootstrap, and better ways to facilitate causal inferences from observational data. He has consulted for several years with the New York State Health Department, Division of Nutrition, evaluating effects of WIC programs, especially effects of mothers’ nutrition on birth outcomes. His publications have appeared in the Psychological Bulletin, Cortex, The Journal of the American Educational Research Association, Encyclopedia of Computer Science and Technology, and Multivariate Behavioral Research.

Representative Publications include:

  • Pruzek, R.M. (1997). An introduction to Bayesian Inference and its applications. In Harlow, L., Mulaik, S.A., and Steiger, J. (Eds.), What if there were no significance tests? (pp. 287-318). Hillsdale, NJ: Erlbaum & Associates.
  • Pruzek, R.M., & Lepak, G. (1992). Weighted structural regression: A broad class of adaptive methods to improving linear prediction. Multivariate Behavioral Research, 27, 95-129.
  • Rabinowitz, S.N., Rule, D., & Pruzek, R.M. (1998). Some new regression methods for predictive and construct validation. Social Indicators Research, 45, 201-231