Applications of Paradata in Survey Design and Analysis
type: CreativeWork
description: This dissertation discusses three separate but related applications of paradata for survey design and analysis. Chapter 1 expands on the use of call record data for nonresponse investigation in face-to-face surveys. By focusing on the relatively underexplored analysis of longitudinal call record data in household surveys, it considers association between Wave 1 call record sequences and response outcomes in proceeding Waves (2, 3 and 4) of the UK Household Longitudinal Study (or Understanding Society). This chapter addresses the predictive power of predefined call record sequences observed in the baseline wave of this survey by comparing model estimates that employ not just this type of paradata but also more conventional predictors of nonresponse (like sociodemographic characteristics of issued households as well as auxiliary geographic information). Beyond finding associations and comparing model specifications, this analysis is primarily interested in informing response retention strategies for panel surveys based on the calling patterns of earlier waves. Chapter 2 is similarly concerned with field effort optimization. However, while Chapter 1 uses call record data as predictors of a given outcome of interest (namely future wave contact and cooperation), Chapter 2 proposes models to predict the calling effort inherent in the processes of contact and cooperation conditional on household and aggregate individual-level data (as well as lagged contact record data). Given the onerous fieldwork demands of household longitudinal surveys, the analysis of this chapter aims to inform data collection optimization by identifying predictors (especially those derived from paradata) of differential contactability, cooperation and overall field effort requirements in longitudinal context. The third and final chapter analyses different types of CAWI generated paradata to assess progress indicator (PI) effects on survey response quality. The data used for this analysis comes from …
abstract: This dissertation discusses three separate but related applications of paradata for survey design and analysis. Chapter 1 expands on the use of call record data for nonresponse investigation in face-to-face surveys. By focusing on the relatively underexplored analysis of longitudinal call record data in household surveys, it considers association between Wave 1 call record sequences and response outcomes in proceeding Waves (2, 3 and 4) of the UK Household Longitudinal Study (or Understanding Society). This chapter addresses the predictive power of predefined call record sequences observed in the baseline wave of this survey by comparing model estimates that employ not just this type of paradata but also more conventional predictors of nonresponse (like sociodemographic characteristics of issued households as well as auxiliary geographic information). Beyond finding associations and comparing model specifications, this analysis is primarily interested in informing response retention strategies for panel surveys based on the calling patterns of earlier waves. Chapter 2 is similarly concerned with field effort optimization. However, while Chapter 1 uses call record data as predictors of a given outcome of interest (namely future wave contact and cooperation), Chapter 2 proposes models to predict the calling effort inherent in the processes of contact and cooperation conditional on household and aggregate individual-level data (as well as lagged contact record data). Given the onerous fieldwork demands of household longitudinal surveys, the analysis of this chapter aims to inform data collection optimization by identifying predictors (especially those derived from paradata) of differential contactability, cooperation and overall field effort requirements in longitudinal context. The third and final chapter analyses different types of CAWI generated paradata to assess progress indicator (PI) effects on survey response quality. The data used for this analysis comes from …
author: Lagorio, Carlos
language: en
keywords: H Social Sciences (General), HA Statistics
date published: 2019-04
date modified: 2026-07-24T02:18:26Z
provider: University of Essex
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Essex