Undoing complex survey data structures : Some theory and applications of inverse sampling

Application of classical statistical methods to data from complex sample surveys without making allowance for the survey design features can lead to erroneous inferences. Methods have been developed that account for the survey design, but these methods require additional information such as survey weights, design effects or cluster identification for microdata. Inverse sampling (Hinkins, Oh and Scheuren 1997) provides an alternative approach by undoing the complex survey data structures so that standard methods can be applied. Repeated subsamples with unconditional simple random sampling structure are drawn and each subsample analysed by standard methods and then combined to increase the efficiency. This method has the potential to preserve confidentiality of microdata, although computer-intensive. We present some theory of inverse sampling and explore its limitations. A combined estimating equations approach is proposed for handling complex parameters such as ratios and "census" linear regression and logistic regression parameters. The method is applied to a cluster correlated data set reported in Battese, Harter and Fuller (1988).