Panel Studies

Panel studies pose fairly formidable methodological challenges. The lag between waves needs to be consistent with the change patterns of interest. For example, whereas the impact of a medication's side effect on cognitive functioning may last only weeks, the effect of obesity and lack of exercise on onset of diabetes may not take place for ten or more years. Also, measurement must be consistent over waves in order not to confound change in the underlying characteristic with measurement change, and it needs to be inclusive in order to capture all possible causes and outcomes and controls.

Most of the big panel studies utilize population probability samples that permit generalization to the target population and provide for external validity. Such generalizations can be biased by missing data, including nonresponse to initial recruitment, missing information to specific measures, and—particularly critical for panel studies—dropping out of later waves, because those with missing data may be systematically different from those without. Potential biases can be reduced by minimizing nonresponse and attrition during conduct of the panel study, by imputing missing responses after data are collected, and by modeling missing information during data analysis.

All of these design considerations imply that a fair amount of prior knowledge about the phenomenon of interest and its explanations is required to optimally design a panel study. Moreover, relevant knowledge continues to accumulate over the often lengthy life of a panel study and can make an ongoing study obsolete. Finally, panel studies tend to be very expensive to conduct and therefore tend to be designed with several objectives in mind; the multipurpose nature can interfere with a design tightly focused on a specific hypothesis.