An effective approach to the repeated cross-sectional design

Matthew J. Lebo, Christopher Weber

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


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 ordinary least squares to PCSTS to time series-force researchers to choose one level of analysis. The PCSTS tool kit 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 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.

Original languageEnglish (US)
Pages (from-to)242-258
Number of pages17
JournalAmerican Journal of Political Science
Issue number1
StatePublished - Jan 1 2015

ASJC Scopus subject areas

  • Sociology and Political Science
  • Political Science and International Relations


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