Using the Canadian Workplace and Employee Survey (WES), three variance estimation methods for weighting large datasets with complex sampling designs are compared: simple final weighting, standard bootstrapping and mean bootstrapping. Using a logit analysis, it is shown - depending on which weighting method is used - different predictor variables are significant. The potential lack of independence inherent in a multi-stage cluster sample design, as in the WES, results in a downward bias in the variance when conducting statistical inference (using the simple final weight), which in turn results in increased Type I errors. Bootstrap methods can account for the survey’s design and adjust the variance so that it is inference appropriate and corrected for downward bias. The WES provides mean, as opposed to standard, bootstrap weights with the data; thus, a further adjustment to account for the reduced variation inherent when information is grouped is required. Failure to use mean bootstrap weights appropriately leads to biased standard errors and inappropriate inference.
Mann, Sara and Chowhan, James
"Weighting Large Datasets with Complex Sampling Designs: Choosing the Appropriate Variance Estimation Method,"
Journal of Modern Applied Statistical Methods:
1, Article 11.
Available at: http://digitalcommons.wayne.edu/jmasm/vol10/iss1/11