Accounting for self-protective responses in randomized response data from a social security survey using the zero-inflated Poisson model
Cruyff, Maarten J. L. F., Böckenholt, Ulf, van den Hout, Ardo and van der Heijden, Peter G.M. (2008) Accounting for self-protective responses in randomized response data from a social security survey using the zero-inflated Poisson model. Annals of Applied Statistics, 2, (1), 316-331. (doi:10.1214/07-AOAS135).
Full text not available from this repository.
In 2004 the Dutch Department of Social Affairs conducted a survey to assess the extent of noncompliance with social security regulations. The survey was conducted among 870 recipients of social security benefits and included a series of sensitive questions about regulatory noncompliance. Due to the sensitive nature of the questions the randomized response design was used. Although randomized response protects the privacy of the respondent, it is unlikely that all respondents followed the design. In this paper we introduce a model that allows for respondents displaying self-protective response behavior by consistently giving the nonincriminating response, irrespective of the outcome of the randomizing device. The dependent variable denoting the total number of incriminating responses is assumed to be generated by the application of randomized response to a latent Poisson variable denoting the true number of rule violations. Since self-protective responses result in an excess of observed zeros in relation to the Poisson randomized response distribution, these are modeled as observed zero-inflation. The model includes predictors of the Poisson parameters, as well as predictors of the probability of self-protective response behavior.
|Digital Object Identifier (DOI):||doi:10.1214/07-AOAS135|
|Keywords:||randomized response, poisson regression, zero-inflation, self-protective responses, regulatory noncompliance|
|Subjects:||H Social Sciences > HA Statistics
H Social Sciences > HV Social pathology. Social and public welfare
|Divisions :||Faculty of Social and Human Sciences > Southampton Statistical Sciences Research Institute
|Accepted Date and Publication Date:||
|Date Deposited:||26 Oct 2012 14:02|
|Last Modified:||31 Mar 2016 14:36|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)