Panel Data Analysis

Panel data consist of a cross-section of “individuals” for which there are repeated observations over time. Individuals can be any cross-sectional unit of analysis, such as states, dyads, or survey respondents. Panel data sets are typically dichotomized between long panels, which have many measurement occasions relative to the size of the cross section, and short panels, which have many individuals in the cross section relative to the number of repeated measurement occasions, or “waves.” In general, the methods associated with the term panel data analysis or longitudinal analysis focus on short panels, while methods under the time-series cross section umbrella focus more on analyzing long panels. The key advantage of panel data is that such data offer the opportunity to better evaluate causal propositions than strictly cross-sectional data. Whereas cross-sectional data only allow the researcher to observe covariances, panel data further allow the researcher to observe whether a change in an input precedes a change in the outcome. In other words, since panel data consist of the same individuals over time, the analyst can observe a shift in responses as a reaction to an input. One example would be an evaluation of whether a state's present behavior responds to the prior behavior of its neighbors. Another example might be using a survey panel, such as those often incorporated into the American National Election Studies, to assess how partisan strength influences campaign interest over time. Panel data have a number of features that can pose challenges to analysts. These issues include unit effects, serial correlation, heteroskedasticity, and contemporaneous correlation. Panels also have special problems of missingness. The remainder of this entry focuses on these issues and some remedies for each.