Evaluation of Conditional Weight Approximations for Two-Level Models

Abstract

This article evaluates two methods of approximating cluster-level and conditional sampling weights when only unconditional sampling weights are available. For estimation of a multilevel analysis that does not include all facets of a sampling design, conditional sampling weights at each stage of the model should be used, but typically only the unconditional sampling weight of the ultimate sampling unit is provided on federal publicly-released datasets. Methods of approximating these conditional weights have been suggested but there has been no study of their adequacy. This demonstration and simulation study examines the feasibility of using these weight approximations.

Keywords:

• Multilevel models
• Multilevel pseudomaximum likelihood
• Sampling weights

Mathematics Subject Classification:

• 62D66

Acknowledgments

The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D110046 to the University of Maryland, Baltimore County. The opinions expressed are those of the author and do not represent views of the Institute or the U.S. Department of Education.

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