Abstract
Abstract. Research, policy, and practice often target specific subpopulations. Large-scale survey studies are particularly useful for performing subpopulation analyses due to the large and representative nature of the samples. However, these studies utilize complex probability sampling designs, which complicate subpopulation analyses. This Monte-Carlo simulation study evaluated the interactive effect of subpopulation analysis method (multiple-group; zero-weight; subset) and cluster estimation method (multilevel modeling; robust single-level modeling) on the performance of fixed effect parameter, standard error, and interval estimators. Results should be used to inform statistical practice with the ultimate goal of achieving more valid inferences in subpopulation research.
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