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John J. McArdle

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Contact Information

University of Southern California
Department of Psychology
SGM 501
Los Angeles, CA 90089-1061
Phone: 213.740.2203
Email: JMCARDLE@USC.EDU

Education

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

Co-Investigator
Health and Retirement Study
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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.& 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.& Hamagami, F. Structural equation models for evaluating dynamic concepts within longitudinal twin analyses. Behavior Genetics, 33, 137-159 [2004]

McArdle, John J.& 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. & 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., & 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]

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