Who participates? Accounting for longitudinal retention in the MIDUS national study of health and well-being.

Who is likely to participate in the second wave of a large, national health study? Despite variation in samples, research design, and mode of assessment, prior studies have shown evidence of differentiated probabilities of response (or nonresponse) due to selective sociodemographic and health characteristics (Fitzgerald, Gottschalk, & Moffitt, 1998; Kapteyn, Michaud, Smith, & van Soest, 2006; Lindenberger et al., 1999). We draw on this work to examine predictors of longitudinal retention in the second wave of the MIDUS (Midlife in the United States) national survey. We highlight longitudinal retention rather than attrition because studies focused on attrition and/or nonresponse tend to be concerned with bias and related methodological adjustments (e.g., sample weights based on various assumptions about nonresponders compared to responders). The emphasis on retention, in contrast, addresses characteristics of the Time-2 respondents on whom subsequent longitudinal findings will be generated. Such information underscores the importance of which sociodemographic and health characteristics predict who stays in a longitudinal study over time. The literature on survey response has examined many common sociodemographic and health variables to explain participation, but the direction and significance of these influences have been inconsistent. For example, men are sometimes found to be less likely to respond to a survey than women (Kalton, Lepkowski, Montanari, & Maligalig, 1990), whereas in other studies gender plays no significant role (Adams et al., 1990). The literature is also ambiguous about the effect of marital status and race on survey response; not being married is sometimes associated with nonresponse, just as response rates among minorities are sometimes lower than among others. Both marital status and race have been linked to health, suggesting that marital status and race may be confounded with health in studies of survey participation. Whether any of these variables are key predictors is likely influenced by whether other important variables are controlled for. In fact, Mihelic and Crimmins (1997) suggested that such inconsistent results may be due to the lack of multivariate analyses or the omission of important variables (see also Groves, Singer, & Corning, 2000). They propose greater emphasis on control variables, that is, examine the effects of single variables, net of the influence of other factors as the solution. Groves et al. (2000) suggested a further alternative, which is to look at the interplay among key predictor variables—that is, how they modulate the effects of each other. So doing calls for enriched conceptualization of survey participation via a priori formulation of reasons for how one predictor variable might mitigate, or exacerbate, the influence of another on participation. Thus, rather than focus on single predictors of survey response, net of other factors, we emphasize the need to assess participation as a product of multiple interacting influences. Guided by this formulation, we first examine sociodemographic and health characteristics (variable by variable) that likely predict retention, and we then examine select interactions between sociodemographic and health predictor variables to probe the conditional relationships between them.