The University of Southampton
University of Southampton Institutional Repository

Do randomized-response designs eliminate response biases? An empirical study of non-compliance behavior

Do randomized-response designs eliminate response biases? An empirical study of non-compliance behavior
Do randomized-response designs eliminate response biases? An empirical study of non-compliance behavior
Out of the toolbox of survey methods for obtaining honest answers to sensitive issues, the method of randomized responses (RR) has proven to be the most effective one. So far, in applications of RR methods it has been assumed that they eliminate response biases. We investigate the validity of this assumption by applying multivariate RR models that allow for different types of response biases. Our data analyses show that RR methods do not eliminate response biases but that they can be modeled in informative ways: accounting for response biases leads to estimates that are at least twice the size of the estimates obtained when response biases are ignored
0883-7252
377-392
Böckenholt, Ulf
580c976b-4040-4cb2-877c-35e042a224df
Barlas, Sema
70946aff-6ec5-4574-bfe1-3e85b35c547b
van der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
Böckenholt, Ulf
580c976b-4040-4cb2-877c-35e042a224df
Barlas, Sema
70946aff-6ec5-4574-bfe1-3e85b35c547b
van der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612

Böckenholt, Ulf, Barlas, Sema and van der Heijden, Peter G.M. (2009) Do randomized-response designs eliminate response biases? An empirical study of non-compliance behavior. Journal of Applied Econometrics, 24 (3), 377-392. (doi:10.1002/jae.1052).

Record type: Article

Abstract

Out of the toolbox of survey methods for obtaining honest answers to sensitive issues, the method of randomized responses (RR) has proven to be the most effective one. So far, in applications of RR methods it has been assumed that they eliminate response biases. We investigate the validity of this assumption by applying multivariate RR models that allow for different types of response biases. Our data analyses show that RR methods do not eliminate response biases but that they can be modeled in informative ways: accounting for response biases leads to estimates that are at least twice the size of the estimates obtained when response biases are ignored

Full text not available from this repository.

More information

Published date: April 2009
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 344669
URI: https://eprints.soton.ac.uk/id/eprint/344669
ISSN: 0883-7252
PURE UUID: a6282c0d-4ad8-4d68-ada8-e1e2a8a005b3

Catalogue record

Date deposited: 08 Nov 2012 08:34
Last modified: 18 Jul 2017 05:14

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×