John J. McArdle

Co-Investigator Picture
Health and Retirement Study

Contact Information

University of Southern California
Department of Psychology
SGM 501
Los Angeles, CA 90089-1061
Phone: 213.740.2203


Post-Doc, University of Denver, Denver, CO
M.A., Ph.D., Hofstra University, Hempstead, NY
B.A., Franklin & Marshall College, Lancaster, PA

Research and Projects

John J. (Jack) McArdle is a multivariate experimental psychologist. He is currently Professor of Psychology at University of Southern California. McArdle's research has been focused on age-sensitive methods for psychological and educational measurement and longitudinal data analysis including published work has been in the area of factor analysis, growth curve analysis, and dynamic modeling of adult cognitive abilities.

Selected Recent Publications

McArdle, John J. and Hamagami, F. Methods for dynamic change hypotheses. In K. van Montfort, J. Oud, & A. Satorra, pp. 295-335, Recent developments on structural equation models. Dordrecht: Kluwer Academic Publishers [2004]

McArdle, John J. and Hamagami, F. Structural equation models for evaluating dynamic concepts within longitudinal twin analyses. Behavior Genetics, 33, 137-159 [2004]

McArdle, John J. and Nesselroade, J.R. Growth curve analyses in contemporary psychological research. In J. Schinka & W. Velicer (Editors), Comprehensive Handbook of Psychology, Volume Two: Research Methods in Psychology. New York: Pergamon Press [2003]

McArdle, J.J., Ferrer-Caja, E., Hamagami, F. and Woodcock, R.W. Comparative longitudinal longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Developmental Psychology, 38(1), 115-142 [2002]

McArdle, J.J., Hamagami, F., Meredith, W., and Bradway, K.P. Modeling the dynamic hypotheses of Gf-Gc theory using longitudinal life-span data. Learning and Individual Differences, 12 (2000), 53-79 [2001]

McArdle, J.J. A latent difference score approach to longitudinal dynamic structural analyses. In R. Cudeck, S. du Toit, & D. Sorbom (Eds.). Structural Equation Modeling: Present and future. Lincolnwood, IL: Scientific Software International (pp. 342-380) [2001]