Analysis of large-scale secondary data in higher education research: Potential perils associated with complex sampling designs

Most large-scale secondary data sets used in higher education research (e.g., NPSAS or BPS) are constructed using complex survey sample designs where the population of interest is stratified on a number of dimensions and oversampled within certain of these strata. Moreover, these complex sample designs often cluster lower level units (e.g., students) within higher level units (e.g., colleges) to achieve efficiencies in the sampling process. Ignoring oversampling (unequal probability of selection) in complex survey designs presents problems when trying to make inferences-data from these designs are, in their raw form, admittedly nonrepresentative of the population to which they are designed to generalize. Ignoring the clustering of observations in these sampling designs presents a second set of problems when making inferences about variability in the population and testing hypotheses and usually leads to an increased likelihood of committing Type I errors (declaring something as an effect when in fact it is not). This article presents an extended example using complex sample survey data to demonstrate how researchers can address problems associated with oversampling and clustering of observations in these designs.