Combining cohorts in longitudinal surveys

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Iván A. Carrillo and Alan F. Karr¹

Abstract

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 framework 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.

Key Words

Superpopulation parameters; Joint-randomization inference; Replication variance estimation; Rotating panel surveys; Multi-cohort longitudinal surveys; Weighted Generalized Estimating Equations.

Table of content

1 Introduction

2 The SDR design

3 Methodology

4 Application to the SDR

5 Conclusions and future research

 

 

 

 

 

¹Iván A. Carrillo and Alan F. Karr, National Institute of Statistical Sciences, 19 T.W. Alexander Drive, Research Triangle Park, NC 27709, U.S.A. E-mail: ivan@niss.org and karr@niss.org.