Panel Studies

Many options for analysis of panel data are available. For outcomes in the form of simple transitions between two waves, logistic regression analysis may be used. For example, the transition from "no diabetes" at wave t to "diabetes" at wave t+1 may be so analyzed. The "competing risks" of dying and dropping out of the study may be modeled by using multinomial regression analysis that allows for multiple outcomes. In the case of transitions for which exact timing is available such as death or retirement, survival or hazard analysis techniques allow a refined assessment of the change patterns and the causal dynamics. For outcomes in the form of continuous variables such as a performance measure of cognitive functioning, ordinary least squares regression analysis can model the amount of change between two waves. Autoregressive structural equation modeling and growth curve analysis permit the specification of multiwave change and stability; in the former change is conceived as relative change, in the latter as absolute change. For both types of models statistical procedures exist that allow for the evaluation and control of measurement error. In all of these analytical techniques potential causes— conceptualized either as status or change—may be evaluated as statistical predictors. In multi-wave panel studies different lags between cause and consequence can also be evaluated.