Incorporating the sampling design in weighting adjustments for panel attrition |
| |
| Authors: | Qixuan Chen Andrew Gelman Melissa Tracy Fran H Norris Sandro Galea |
| |
| Institution: | 1. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, U.S.A.;2. Department of Statistics, Columbia University, New York, NY, U.S.A.;3. Department of Political Science, Columbia University, New York, NY, U.S.A.;4. Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, Rensselaer, NY, U.S.A.;5. Department of Psychiatry, Dartmouth Medical School, Hanover, NH, U.S.A.;6. School of Public Health, Boston University, Boston, MA, U.S.A. |
| |
| Abstract: | We review weighting adjustment methods for panel attrition and suggest approaches for incorporating design variables, such as strata, clusters, and baseline sample weights. Design information can typically be included in attrition analysis using multilevel models or decision tree methods such as the chi‐square automatic interaction detection algorithm. We use simulation to show that these weighting approaches can effectively reduce bias in the survey estimates that would occur from omitting the effect of design factors on attrition while keeping the resulted weights stable. We provide a step‐by‐step illustration on creating weighting adjustments for panel attrition in the Galveston Bay Recovery Study, a survey of residents in a community following a disaster, and provide suggestions to analysts in decision‐making about weighting approaches. Copyright © 2015 John Wiley & Sons, Ltd. |
| |
| Keywords: | adjustment cell method CHAID algorithm design variables multilevel models response propensity weighting |
|
|
