<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">I. A. Carrillo</style></author><author><style face="normal" font="default" size="100%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining cohorts in longitudinal surveys</style></title><secondary-title><style face="normal" font="default" size="100%">Survey Methodology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Joint-randomization inference</style></keyword><keyword><style  face="normal" font="default" size="100%">Multi-cohort longitudinal surveys</style></keyword><keyword><style  face="normal" font="default" size="100%">Replication variance estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Rotating panel surveys</style></keyword><keyword><style  face="normal" font="default" size="100%">Superpopulation parameters</style></keyword><keyword><style  face="normal" font="default" size="100%">Weighted Generalized Estimating Equations</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">149-182</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A question that commonly arises in longitudinal surveys is the issue of how to combine differing cohorts of the survey. In this paper we present a novel method for combining different cohorts, and using all available data, in a longitudinal survey to estimate parameters of a semiparametric model, which relates the response variable to a set of covariates. The procedure builds upon the Weighted Generalized Estimation Equation method for handling missing waves in longitudinal studies. Our method is set up under a joint-randomization frame work for estimation of model parameters, which takes into account the superpopulation model as well as the survey design randomization. We also propose a design-based, and a joint-randomization, variance estimation method. To illustrate the methodology we apply it to the Survey of Doctorate Recipients, conducted by the U.S. National Science Foundation&lt;/p&gt;
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