The Wechsler Adult Intelligence Scale (WAIS) has been widely used in research as well as clinical applications. But the conclusions about adult intelligence that can be drawn from the existing WAIS literature are often clouded and sometimes contradictory. WAIS summary data, subscale scores, item data and other information on over 50,000 individual subjects has been obtained from over 200 different experimental studies. Statistical stratification and crossvalidation procedures have been used to select specialized subsamples for analyses.
This research aims i s to define and isolate the growth curves of adult intelligence as measured by the WAIS. Large scale Longitudinal data (N=2,500) and family data (N=3,500) have already been collected from many diverse studies, and ranging over the entire adult age span. The larger sample of cross- sectional WAIS data (N=16,000) will be used to match individuals based on some crucial demographic characteristics. Statistical comparisons of the longitudinal, family, and cross-sectional WAIS data will be made to quantify test-retest and within-family selection effects. A variety of latent variable structural equation models (e.g., LISREL, etc.) will be used to examine the basic questions of factorial invariance over age and time. Growth curve models will be developed which will account for components of age, date of testing, education, and gender, as well as subject selectively and biases due to attrition. One aspect of this work will revolve around extensions of the latent growth model (LGM), especially for use with the analysis of incomplete growth curves, and other analyses will use graphical techniques. In a broad sense, we will adapt contemporary methods from human physical growth and epidemiology for use in psychological aging research.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Project (R01)
Project #
Application #
Study Section
Human Development and Aging Subcommittee 3 (HUD)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Virginia
Schools of Arts and Sciences
United States
Zip Code
McArdle, John J (2014) Loehlin's original models and model contributions. Behav Genet 44:614-9
Blankson, A Nayena; McArdle, John J (2014) A Brief Report on the Factor Structure of the Cognitive Measures in the HRS/AHEAD Studies. J Aging Res 2014:798514
González, Hector M; Tarraf, Wassim; Bowen, Mary E et al. (2013) What do parents have to do with my cognitive reserve? Life course perspectives on twelve-year cognitive decline. Neuroepidemiology 41:101-9
Ghisletta, Paolo; McArdle, John J (2012) Teacher's Corner: Latent Curve Models and Latent Change Score Models Estimated in R. Struct Equ Modeling 19:651-682
McArdle, John J (2011) Longitudinal Dynamic Analyses of Cognition in the Health and Retirement Study Panel. Adv Stat Anal 95:453-480
Jajodia, Archana; Borders, Ashley (2011) Memory predicts changes in depressive symptoms in older adults: a bidirectional longitudinal analysis. J Gerontol B Psychol Sci Soc Sci 66:571-81
McArdle, John J; Prescott, Carol A (2010) Contemporary Modeling of Gene × Environment Effects in Randomized Multivariate Longitudinal Studies. Perspect Psychol Sci 5:606-21
McArdle, John J; Plassman, Brenda L (2009) A biometric latent curve analysis of memory decline in older men of the NAS-NRC twin registry. Behav Genet 39:472-95
McArdle, John J (2009) Latent variable modeling of differences and changes with longitudinal data. Annu Rev Psychol 60:577-605
McArdle, John J; Grimm, Kevin J; Hamagami, Fumiaki et al. (2009) Modeling life-span growth curves of cognition using longitudinal data with multiple samples and changing scales of measurement. Psychol Methods 14:126-49

Showing the most recent 10 out of 24 publications