A practical bootstrap method for testing hypotheses from survey data - ARCHIVED

The bootstrap technique is becoming more and more popular in sample surveys conducted by national statistical agencies. In most of its implementations, several sets of bootstrap weights accompany the survey microdata file given to analysts. So far, the use of the technique in practice seems to have been mostly limited to variance estimation problems. In this paper, we propose a bootstrap methodology for testing hypotheses about a vector of unknown model parameters when the sample has been drawn from a finite population. The probability sampling design used to select the sample may be informative or not. Our method uses model-based test statistics that incorporate the survey weights. Such statistics are usually easily obtained using classical software packages. We approximate the distribution under the null hypothesis of these weighted model-based statistics by using bootstrap weights. An advantage of our bootstrap method over existing methods of hypothesis testing with survey data is that, once sets of bootstrap weights are provided to analysts, it is very easy to apply even when no specialized software dealing with complex surveys is available. Also, our simulation results suggest that, overall, it performs similarly to the Rao-Scott procedure and better than the Wald and Bonferroni procedures when testing hypotheses about a vector of linear regression model parameters.