Covariate Randomized Response Models

Scheers, N. J.; Dayton, C. Mitchell

doi:10.1080/01621459.1988.10478686

Cited by 60 publications

(60 citation statements)

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“…Our contribution here is to point out that all four designs can be analyzed under the single likelihood function given in Equation (7). In the literature, van den Hout, van der Heijden, and Gilchrist (2007) showed that the same likelihood function applies to the forced response and mirrored response designs (see also Scheers and Dayton 1988). van der Heijden and van Gils (1996) developed a similar likelihood framework for the forced response and disguised response designs.…”

Section: Multivariate Regression Modelmentioning

confidence: 99%

“…The only relevant work we find is Lakshmi and Raghavarao (1992) who derived a power function to test the probability of respondent noncompliance under a mirrored question design. While others compare efficiency across various designs (Moors 1971;Pollock and Bek 1976;Scheers and Dayton 1988;Umesh and Peterson 1991;Lensvelt-Mulders et al 2005b), they fall short of providing a unified framework for conducting power analysis to help applied researchers design randomized response surveys. Our analysis fills this important gap in the literature.…”

Section: Power Analysismentioning

confidence: 99%

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E-research – selected issues concerning the Internet attitudes and opinions research

Kulikowski¹

2015

e-mentor

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About a half century ago, in 1965, Warner proposed the randomized response method as a survey technique to reduce potential bias due to nonresponse and social desirability when asking questions about sensitive behaviors and beliefs. This method asks respondents to use a randomization device, such as a coin flip, whose outcome is unobserved by the interviewer. By introducing random noise, the method conceals individual responses and protects respondent privacy. While numerous methodological advances have been made, we find surprisingly few applications of this promising survey technique. In this article, we address this gap by (1) reviewing standard designs available to applied researchers, (2) developing various multivariate regression techniques for substantive analyses, (3) proposing power analyses to help improve research designs, (4) presenting new robust designs that are based on less stringent assumptions than those of the standard designs, and (5) making all described methods available through open-source software. We illustrate some of these methods with an original survey about militant groups in Nigeria.

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Section: Multivariate Regression Modelmentioning

confidence: 99%

Section: Power Analysismentioning

confidence: 99%

E-research – selected issues concerning the Internet attitudes and opinions research

Kulikowski¹

2015

e-mentor

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“…The closer (c, d) to (0, 1) the better in terms of standard errors, but the closer to (0, 1), the greater the lack of privacy protection and, hence, the higher the probability that respondents do not follow the instructions of the RR design, see Böckenholt and van der Heijden (in press). A discussion of the efficiency with respect to the Warner model and the unrelated-question model can be found in Scheers and Dayton (1988).…”

Section: Full Modelmentioning

confidence: 99%

“…Maddala (1983) was the first to present the likelihood of the model with respect to the RR design by Warner (1965). Scheers and Dayton (1988) discuss the model with respect to both the Warner model and the unrelated-question model (Greenberg et al, 1969). Van der Heijden and Van Gils (1996) present the model where the response variable is subject to either the RR design by Boruch (1971) or the RR design by Kuk (1990).…”

Section: Introductionmentioning

confidence: 99%

The logistic regression model with response variables subject to randomized response

Hout

Heijden

Gilchrist

2007

Computational Statistics & Data Analysis

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The univariate and multivariate logistic regression model is discussed where response variables are subject to randomized response (RR). RR is an interview technique that can be used when sensitive questions have to be asked and respondents are reluctant to answer directly. RR variables may be described as misclassified categorical variables where conditional misclassification probabilities are known. The univariate model is revisited and is presented as a generalized linear model. Standard software can be easily adjusted to take into account the RR design. The multivariate model does not appear to have been considered elsewhere in an RR setting; it is shown how a Fisher scoring algorithm can be used to take the RR aspect into account. The approach is illustrated by analyzing RR data taken from a study in regulatory non-compliance regarding unemployment benefit.

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“…Fox and Wyrick (2008) measured the prevalence of alcohol use, and alcohol-related problems among college students. Rates of academic cheating and rates of criminal behavior were investigated by Tracy and Fox (1981) and Scheers and Dayton (1988), respectively. The prevalence of academic cheating, tax evasion, and software piracy, among others, have been discussed by Lensvelt-Mulders, Hox, van der Heijden, and Maas (2005); Lensvelt-Mulders, Hox, and van der Heijden (2005) and van der Heijden et al (2000).…”

Section: Introductionmentioning

confidence: 99%

Bayesian randomized item response modeling for sensitive measurements

Avetisyan¹

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Covariate Randomized Response Models

Cited by 60 publications

References 3 publications

E-research – selected issues concerning the Internet attitudes and opinions research

E-research – selected issues concerning the Internet attitudes and opinions research

The logistic regression model with response variables subject to randomized response

Bayesian randomized item response modeling for sensitive measurements

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