We are conducting analyses using National Health Interview Survey and Medical Expenditures Panel Survey. Both are nationally representative surveys. We use the survey commands in STATA to adjust for complex survey design and have to decide how to deal with the problem with single psu. We understand that it is important to adjust for all three levels of survey design (weights, PSUs, and strata) in order to obtain correct variance and standard errors. However, we are wondering what the statistical implications are (magnitude and direction of standard errors, in particular) if we only adjust for weights and PSUs but not strata. Per the Stata manual, the variance estimates are based only on computations at the primary sampling-unit level and do not require information about the secondary sampling units. We thus had considered the loss of efficiency without adjusting for strata might have little impact on the variance estimates or standard errors.
We ran bivariates analyses with and without adjusting for strata. For some analyses, results of the Pearson chi-square test were similar. However, for other analyses, results were very different, e.g., analysis without adjusting for strata is not significant (p=0.16) while analysis adjusting for strata is highly significant (p<0.001).
We are wondering whether it has been examined or determined how the results may or may not vary without or without adjusting for strata. Does it depend on the type of analyses (bivaraite or regression), sample size, or sub-group analyses?
We would greatly appreciate your thoughts on this issue.
Su-Ying
Su-Ying Liang, Ph.D.
University of California San Francisco
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San Francisco, CA 94143 (fedex 94118)
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