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Adjusting for nonresponse in the analysis and estimation of sample survey data for cluster designs

Adjusting for nonresponse in the analysis and estimation of sample survey data for cluster designs
Adjusting for nonresponse in the analysis and estimation of sample survey data for cluster designs
Nonresponse in sample surveys has been increasing over the years. This thesis covers that issue in two main parts. The first part is concerned with how to use observed data to make inference about regression coefficients in a linear regression model of cluster-level variables when some of the response variable data is missing. A naive approach estimates the regression coeffcients without considering nonresponse. We propose new methods for estimating coeffcients which incorporate information on nonresponse at the cluster level. We also extend Heckman estimators to our clustered model. The Workplace Employment Relations Survey (WERS) 2004 data and data from a prepared simulation study are used to compare the new methods with the naive approach. In the second part the generalized regression estimator (GREG) for two-stage sampling will be considered. We propose new optimum GREG estimators for stratified two-stage sampling and a simulation study is used in order to assess the performance of the new estimators.
Nangsue, Nuanpan
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Nangsue, Nuanpan
eaea169b-48b1-4945-9e2e-bd5be3400b49
Berger, Yves
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Skinner, Christopher
dec5ef40-49ef-492a-8a1d-eb8c6315b8ce
SHLOMO, NATALIE
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Nangsue, Nuanpan (2014) Adjusting for nonresponse in the analysis and estimation of sample survey data for cluster designs University of Southampton, Social Sciences, Doctoral Thesis , 145pp.

Record type: Thesis (Doctoral)

Abstract

Nonresponse in sample surveys has been increasing over the years. This thesis covers that issue in two main parts. The first part is concerned with how to use observed data to make inference about regression coefficients in a linear regression model of cluster-level variables when some of the response variable data is missing. A naive approach estimates the regression coeffcients without considering nonresponse. We propose new methods for estimating coeffcients which incorporate information on nonresponse at the cluster level. We also extend Heckman estimators to our clustered model. The Workplace Employment Relations Survey (WERS) 2004 data and data from a prepared simulation study are used to compare the new methods with the naive approach. In the second part the generalized regression estimator (GREG) for two-stage sampling will be considered. We propose new optimum GREG estimators for stratified two-stage sampling and a simulation study is used in order to assess the performance of the new estimators.

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Published date: June 2014
Organisations: University of Southampton, Social Statistics & Demography

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Local EPrints ID: 366488
URI: http://eprints.soton.ac.uk/id/eprint/366488
PURE UUID: 516a848c-0ce4-4d1d-a125-95050f7afa45

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Date deposited: 10 Sep 2014 11:47
Last modified: 18 Jul 2017 02:12

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Contributors

Author: Nuanpan Nangsue
Thesis advisor: Yves Berger
Thesis advisor: Christopher Skinner
Thesis advisor: NATALIE SHLOMO

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