Article
Version 1
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Level Weights for Modeling with Complex Survey Data
Version 1
: Received: 15 March 2023 / Approved: 20 March 2023 / Online: 20 March 2023 (03:41:30 CET)
How to cite: Natuhamya, C. Level Weights for Modeling with Complex Survey Data. Preprints 2023, 2023030339. https://doi.org/10.20944/preprints202303.0339.v1 Natuhamya, C. Level Weights for Modeling with Complex Survey Data. Preprints 2023, 2023030339. https://doi.org/10.20944/preprints202303.0339.v1
Abstract
Introduction: Weighting is widely used in applied statistics especially while dealing with survey data. In recent years, multilevel modeling under complex survey designs has increased, resulting into demand for level weights. However, survey data that are accessible to the public for use usually do not contain level weights that are useful in multilevel modeling, but final survey weights that are only appropriate for single level analyses. In this paper, we demonstrate how the final survey weights can be used to estimate level weights for multilevel data analysis, and compare a model that applied level weights with one that applied the final survey weights. Methods: A framework for approximating level weights proposed by the Demographic Health Survey (DHS) program was used to estimate the level weights. Models were fitted using a multilevel mixed effects logistic regression method. Estimates from a model that applied survey weights was compared to those from a model that applied level weights. Results: Application of final survey weights instead of level weights underestimated standard errors and led to loss of precision of model estimates. Conclusions: Use of level weights produces estimates with high precision and yields correct values of standard errors hence appropriately informing inference.
Keywords
complex survey design; multilevel models; nonresponse; sampling probability; survey data; Uganda; weighting
Subject
Medicine and Pharmacology, Veterinary Medicine
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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