Accounting for Non‐compliance in the Analysis of Randomized Response Data

Hout, Ardo van den; Klugkist, Irene

doi:10.1111/j.1467-842x.2009.00552.x

Cited by 13 publications

(11 citation statements)

References 34 publications

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“…In other Bayesian settings, O'Hagan (1987) derived Bayes linear estimators using a nonparametric approach, and Oh (1994) and Unnikrishnan andKunte (1999) used MCMC methods. Recently, van den Hout andKlugkist (2009) proposed Bayesian inference to the RR technique which takes into account assumptions with respect to non-compliance under simple random sampling. Also, Tian et al (2009) proposed Bayesian method to non-randomized response models which are not using randomized device.…”

Section: Song and Kimmentioning

confidence: 99%

Bayesian Analysis of Randomized Response Sum Score Variables

Song

Kim

2012

Communications in Statistics - Theory and Methods

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The randomized-response (RR) technique is an effective survey method when collecting sensitive information. In this technique, a probability mechanism using randomization devices is commonly involved in answering to sensitive questions. In order to evaluate the survey at the most accurate extend, self-protection (SP) is introduced to describe the responses by participants who give the evasive answer without taking the result of the randomization device into account. In this study, we propose a Bayesian approach to modeling RR sum score variables under SP assumption. RR data from a Dutch survey on non-compliance with social security regulation in 2004 is used to demonstrate the proposed models.

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Section: Song and Kimmentioning

confidence: 99%

Bayesian Analysis of Randomized Response Sum Score Variables

Song

Kim

2012

Communications in Statistics - Theory and Methods

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“…As a consequence, they might fail to adhere to the instructions and intentionally alter their true status by providing self‐protecting responses. Respondents who do not follow the instructions of the questioning procedure and/or do not answer as directed by the procedure are identified by Clark & Desharnais () as cheaters , while van den Hout & Klugkist () used the less accusatory notion of noncompliance . Heck et al () chose the term nonadherence to include the deliberate concealing of the true status (cheating/self‐protective responding) or response patterns resulting from misunderstanding or carelessness.…”

Section: The Optional Negative Surveymentioning

confidence: 99%

“…Heck et al (2018) chose the term nonadherence to include the deliberate concealing of the true status (cheating/self-protective responding) or response patterns resulting from misunderstanding or carelessness. In general, apart from different definitions, nonadherence to instructions leads to questionable results unless models that account for this behaviour are considered (see, among others, Clark & Desharnais, 1998;van den Hout & Klugkist, 2009;Moshagen et al, 2012;Heck et al, 2018;Hoffmann et al, 2017).…”

Section: The Optional Negative Surveymentioning

confidence: 99%

Mixture of Truthful–Untruthful Responses in Public Surveys

Christofides

Perri

2019

Int Statistical Rev

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Summary When sensitive issues are surveyed, collecting truthful data and obtaining reliable estimates of population parameters is a persistent problem in many fields of applied research mostly in sociological, economic, demographic, ecological and medical studies. In this context, and moving from the so‐called negative survey, we consider the problem of estimating the proportion of population units belonging to the categories of a sensitive variable when collected data are affected by measurement errors produced by untruthful responses. An extension of the negative survey approach is proposed herein in order to allow respondents to release a true response. The proposal rests on modelling the released data with a mixture of truthful and untruthful responses that allows researchers to obtain an estimate of the proportions as well as the probability of receiving the true response by implementing the EM‐algorithm. We describe the estimation procedure and carry out a simulation study to assess the performance of the EM estimates vis‐à‐vis certain benchmark values and the estimates obtained under the traditional data‐collection approach based on direct questioning that ignores the presence of misreporting due to untruthful responding. Simulation findings provide evidence on the accuracy of the estimates and permit us to appreciate the improvements that our approach can produce in public surveys, particularly in election opinion polls, when the hidden vote problem is present.

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“…For example, Winkler and Franklin (1979) gave an approximate Bayesian analysis of Warner's mirrored design, O'Hagan (1987) derived Bayesian linear estimators for the unrelated question design, and Oh (1994) used data augmentation to introduce latent variables to Gibbs sampling of the mirrored design, the unrelated question design and the two-stage design with binary and polychotomous responses. van den Hout and Klugkist (2009) proposed Bayesian inference that takes into account assumptions with respect to non-compliance under simple random sampling. Also, Tian et al (2009) proposed Bayesian approaches to nonrandomised response models without using random mechanisms.…”

Section: Review Of Randomised Response Designsmentioning

confidence: 99%

Bayesian Analysis of a Sensitive Proportion for a Small Area

Nandram

2018

Int Statistical Rev

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Summary Without accounting for sensitive items in sample surveys, sampled units may not respond (nonignorable nonresponse) or they respond untruthfully. There are several survey designs that address this problem and we will review some of them. In our study, we have binary data from clusters within small areas, obtained from a version of the unrelated‐question design, and the sensitive proportion is of interest for each area. A hierarchical Bayesian model is used to capture the variation in the observed binomial counts from the clusters within the small areas and to estimate the sensitive proportions for all areas. Both our example on college cheating and a simulation study show significant reductions in the posterior standard deviations of the sensitive proportions under the small‐area model as compared with an analogous individual‐area model. The simulation study also demonstrates that the estimates under the small‐area model are closer to the truth than for the corresponding estimates under the individual‐area model. Finally, for small areas, we discuss many extensions to accommodate covariates, finite population sampling, multiple sensitive items and optional designs.

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Accounting for Non‐compliance in the Analysis of Randomized Response Data

Cited by 13 publications

References 34 publications

Bayesian Analysis of Randomized Response Sum Score Variables

Bayesian Analysis of Randomized Response Sum Score Variables

Mixture of Truthful–Untruthful Responses in Public Surveys

Bayesian Analysis of a Sensitive Proportion for a Small Area

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