Two new models for survey sampling with sensitive characteristic: design and analysis

Yu, Jun; Guo, Tian; Tang, Man‐Lai

doi:10.1007/s00184-007-0131-x

Cited by 196 publications

(271 citation statements)

References 18 publications

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“…In this section, four NRR methods are described: The crosswise method and the triangular method (both Yu et al, 2008), the multi-category design by Tang et al (2009), and the diagonal technique by Groenitz (2014). Let the sensitive characteristic be denoted by X.…”

Section: Nrr Methodsmentioning

confidence: 99%

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Improving Estimation Accuracy in Nonrandomized Response Questioning Methods by Multiple Answers

Groenitz

2017

IJSP

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When private or stigmatizing characteristics are included in sample surveys, direct questions result in low cooperation of the respondents. To increase cooperation, indirect questioning procedures have been established in the literature. Nonrandomized response methods are one group of such procedures and have attracted much attention in recent years. In this article, we consider four popular nonrandomized response schemes and present a possibility to improve the estimation precision of these schemes. The basic idea is to require multiple indirect answers from each respondent. We develop a Fisher scoring algorithm for the maximum likelihood estimation in the presented new schemes and show the better efficiency of the new schemes compared with the original designs.

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Section: Nrr Methodsmentioning

confidence: 99%

“…This motivated diverse authors to introduce nonrandomized response (NRR) methods, for example, Yu, Tian, and Tang (2008), Tan, Tian, andLiu (2009) or Groenitz (2014). In NRR schemes, an indirect answer that depends on the respondent's outcome of an auxiliary characteristic must be given.…”

Section: Introductionmentioning

confidence: 99%

Improving Estimation Accuracy in Nonrandomized Response Questioning Methods by Multiple Answers

Groenitz

2017

IJSP

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“…To overcome this problem, this study employs a recent survey method, the Crosswise Model (CM) (Yu et al, 2008). Comparable to RRT, the interviewer asks two questions under CM, a sensitive one with an unknown distribution, and a non-sensitive one with a known distribution.…”

Section: Introductionmentioning

confidence: 99%

Applying 'Benford's Law' to the Crosswise Model: Findings from an Online Survey on Tax Evasion

Kundt

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Applying "Benford's law" to the Crosswise Model: Findings from an online survey on tax evasion Terms of use: Documents in THORBEN KUNDT Zusammenfassung/ AbstractMany surveys on sensitive topics such as tax evasion suffer from the reluctance of respondents to provide truthful answers which can cause downward-biased estimates. This paper addresses this problem by making use of a recent survey method (Crosswise Model) designed to provide positive incentives for respondents to answer sensitive questions more truthful. We extend the Crosswise Model by applying the so-called "Benford Illusion" which allows us to increase the precision of the Crosswise Model that is less statistically efficient than other methods. To test the effectiveness of the model in providing privacy protection, we carried out an online survey in which the respondents were randomly allocated into two splits differing only by the questioning technique applied. Our results suggest that the Crosswise Model can help to increase privacy protection compared to a simple direct questioning approach. As a consequence, survey estimates of tax evasion using the Crosswise Model are likely to become more valid. At the same time, we show that were able to obtain an efficient estimator without substantially decreasing privacy protection, even for a relatively small sample size.

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“…Two other randomized response models that also do not require a (physical) randomizing device, are the triangular model (TM) and the crosswise method (CM) proposed by Yu et al (2008). Two questions are asked, a sensitive question and an innocuous question with a known population prevalence, and a joint answer to both questions is required.…”

Section: The Triangular and Crosswise Rr Methodsmentioning

confidence: 99%

“…The methods were developed in the late 70's (Takahasi & Sakasegawa, 1977) and have received increased attention in the past five years. Related developments are the hidden sensitivity method (Tang, Tian, Tang, & Liu, 2009;Tian et al, 2007), the triangular method (Tan, Tian, & Tang, 2009;Tian, Tang, Liu, Tan, & Tang, 2011;Yu, Tian, & Tang, 2008), the crosswise method (Jann, Jerke, & Krumpal, 2012;Yu et al, 2008), the method of single sample count (Petróczi et al, 2011), and the unmatched count technique (Coutts & Jann, 2011). The techniques are usually claimed to be free from the limitations of the randomized approach and to increase the relative efficiency and the degree of privacy protection.…”

Section: Nonrandomized Response Techniquesmentioning

confidence: 99%

Bayesian randomized item response modeling for sensitive measurements

Avetisyan¹

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Two new models for survey sampling with sensitive characteristic: design and analysis

Abstract: Maximum likelihood estimate, Randomizing device, Randomized response technique, Sensitive questions, Warner’s model,

Cited by 196 publications

References 18 publications

Improving Estimation Accuracy in Nonrandomized Response Questioning Methods by Multiple Answers

Improving Estimation Accuracy in Nonrandomized Response Questioning Methods by Multiple Answers

Applying 'Benford's Law' to the Crosswise Model: Findings from an Online Survey on Tax Evasion

Bayesian randomized item response modeling for sensitive measurements

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