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Incorporating the sampling design in weighting adjustments for panel attrition
Author(s) -
Chen Qixuan,
Gelman Andrew,
Tracy Melissa,
Norris Fran H.,
Galea Sandro
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6618
Subject(s) - weighting , attrition , computer science , sampling design , decision tree , inverse probability weighting , statistics , sampling (signal processing) , sample (material) , econometrics , data mining , propensity score matching , mathematics , medicine , population , chemistry , demography , dentistry , filter (signal processing) , chromatography , sociology , computer vision , radiology
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.