Replication data for: An Effective Approach to the Repeated Cross-Sectional Design

Repeated cross-sectional (RCS) designs are distinguishable from true panels and pooled cross-sectional-time-series (PCSTS) since cross-sectional units – e.g. individual survey respondents – appear but once in the data. This poses two serious challenges. First, as with PCSTS, autocorrelation threatens inferences. However, common solutions like differencing and using a lagged dependent variable are not possible with RCS since lags for i cannot be used. Second, although RCS designs contain information that allows both aggregate- and individual-level analyses, available methods – from pooled OLS to PCSTS to time series – force researchers to choose one level of analysis. The PCSTS toolkit does not provide an appropriate solution and we offer one here: double filtering with ARFIMA methods to account for autocorrelation in longer RCS followed by the use of multilevel modeling (MLM) to estimate both aggregate- and individual-level parameters simultaneously. We use Monte-Carlo experiments and three applied examples to explore the advantages of our framework.