Panel Studies

Panel studies are the optimal design for addressing some of the core questions in aging research: first by measuring in each wave the same characteristics in the same persons, panel studies are able to provide descriptions of changes experienced by individual persons over time and differences between people in their individual change patterns. For example, measures of cognitive functioning and diagnosed diseases are collected at each wave in the Health and Retirement Study, a panel study of physical and mental health and their labor force and economic consequences. Change can then be defined either as transition from one state to another on a categorical variable (e.g., the transition from "no diabetes" to "diabetes") or as difference in level on a continuous variable (e.g., the extent of decline or improvement in cognitive functioning). The absence of change, or stability, may also be of interest.

A single panel study of persons of a limited age range can describe change or stability associated with aging for only one cohort. It is generally recognized that aging is conditioned by the historical, political, economic, and societal contexts in which it takes place. For example, the onset of diabetes depends on lifestyle factors such as obesity or lack of exercise, and lifestyle factors have been changing. Therefore panel studies of different cohorts who age through different historical times are required for generalizeable descriptions of change. But even a multiple-cohort design cannot provide a definitive distinction between aging and historical phenomena; direct investigations of specific causal factors such as lifestyle on diabetes onset represent a more fruitful approach.

Second, panel studies with several waves are the best quasi-experimental design for investigating the causes and consequences of change with high internal validity. While a true experimental design generally is considered the strongest design for investigating causal patterns, many potential causes of interest to aging researchers are not amenable to experimental manipulation. In the above example lifestyle as a potential cause of the onset of diabetes is not easily manipulated, nor is it feasible to manipulate the onset of diabetes in order to study its hypothesized effects on quality of life outcomes. Quasi-experimental designs that depend on the naturally occurring variation of potential causes are the next best alternative. Because naturally occurring variation depends on other causal factors (e.g., the fact that those with healthy lifestyles differ in many other ways from those with less healthy lifestyles), the data collection must be planned to ensure that those other factors are measured and careful statistical control of those factors must be applied during the analysis of the data from quasi-experimental designs. Panel studies have an important advantage over those that use the simpler quasi-experimental design of a single, cross-sectional data collection in that they allow for better specification of the time-ordering between presumed cause and effect.