This is a competing renewal application to support years 03 and 04 of a project focusing on the relationship of socioeconomic status and health, elaborated on by the consideration of a range of personal psychological resources (viz. cognitive functioning, personality characteristics, and social participation/social capital) and family socioeconomic background factors. We seek support for the secondary analysis of two major longitudinal data sets that measure socioeconomic experiences, individual differences in cognitive functioning, personality, social participation, and health at various points in the life-span. The main objectives of the proposed research are to address four specific aims: (1) to investigate the relationships of various measures of current socioeconomic status (e.g. education, occupation, income and assets, employment status) on multiple health and function domains (e.g., physical, psychological, cognitive, social), net of other health risk and demographic factors, in middle-aged adults; (2) to examine the mechanisms underlying the links between current socioeconomic status and health (e.g., the role of childhood socioeconomic status, pre-adult cognitive functioning, social integration, personality characteristics, health behaviors); (3) to examine the ways in which these same mechanisms elaborate and moderate the linkages between SES and health; (4) to test whether the patterns found in these relationships and mechanisms differ by gender, race/ethnicity, or across specific health domains, where possible. To accomplish these tasks, we seek support to analyze two existing longitudinal datasets: (1) the Wisconsin Longitudinal Study (WLS), a sample of 1957 high school graduates from the state of Wisconsin (n=8,493), followed from youth to approximately age 52 in 1992-93; and (2) the Health and Retirement Study (HRS), a national panel study of preretirement men and women aged 51-61 assessed in 1992 (n=9,824), and reinterviewed in 1994 and 1996. We propose to employ a range of statistical procedures, including structural equation modeling, latent growth curve analysis, and logistic regression techniques in the analysis of these data.
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