Validation of a theoretically motivated approach to measuring childhood socioeconomic circumstances in the Health and Retirement Study

Abstract

Childhood socioeconomic status (cSES) is a powerful predictor of adult health, but its operationalization and measurement varies across studies. Using Health and Retirement Study data (HRS, which is nationally representative of community-residing United States adults aged 50+ years), we specified theoretically-motivated cSES measures, evaluated their reliability and validity, and compared their performance to other cSES indices. HRS respondent data (N = 31,169, interviewed 1992–2010) were used to construct a cSES index reflecting childhood social capital (cSC), childhood financial capital (cFC), and childhood human capital (cHC), using retrospective reports from when the respondent was <16 years (at least 34 years prior). We assessed internal consistency reliability (Cronbach’s alpha) for the scales (cSC and cFC), and construct validity, and predictive validity for all measures. Validity was assessed with hypothesized correlates of cSES (educational attainment, measured adult height, self-reported childhood health, childhood learning problems, childhood drug and alcohol problems). We then compared the performance of our validated measures with other indices used in HRS in predicting self-rated health and number of depressive symptoms, measured in 2010. Internal consistency reliability was acceptable (cSC = 0.63, cFC = 0.61). Most measures were associated with hypothesized correlates (for example, the association between educational attainment and cSC was 0.01, p < 0.0001), with the exception that measured height was not associated with cFC (p = 0.19) and childhood drug and alcohol problems (p = 0.41), and childhood learning problems (p = 0.12) were not associated with cHC. Our measures explained slightly more variability in self-rated health (adjusted R2 = 0.07 vs. <0.06) and number of depressive symptoms (adjusted R2 > 0.05 vs. < 0.04) than alternative indices. Our cSES measures use latent variable models to handle item-missingness, thereby increasing the sample size available for analysis compared to complete case approaches (N = 15,345 vs. 8,248). Adopting this type of theoretically motivated operationalization of cSES may strengthen the quality of research on the effects of cSES on health outcomes.