Methods of data collection, perceptions of risks and losses, and motivation to give truthful answers to sensitive survey questions

Rasinski, Kenneth A.; Willis, Gordon; Baldwin, Alison K.; Yeh, Wen-Lin; Lee, Lisa

doi:10.1002/(sici)1099-0720(199910)13:5<465::aid-acp609>3.0.co;2-y

Cited by 57 publications

(49 citation statements)

References 13 publications

(7 reference statements)

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“…Respondents frequently choose to align their answers to sensitive questions with social norms in order to make or uphold a socially desirable impression (Krumpal, 2013;Marquis, Marquis, & Polich, 1986;Paulhus, 1991;Paulhus & Reid, 1991;Phillips & Clancy, 1972;Rasinski, Willis, Baldwin, Yeh, & Lee, 1999;Stocké, 2007;Sudman & Bradburn, 1974;Tourangeau & Yan, 2007). Consequently, prevalence estimates of sensitive attributes may be distorted by the underreporting of socially undesirable and the over-reporting of socially desirable attitudes and behaviors.…”

Section: Abstract Randomized Response Technique Stochastic Lie Detementioning

confidence: 99%

Assessing the validity of two indirect questioning techniques: A Stochastic Lie Detector versus the Crosswise Model

Hoffmann

Musch

2015

Behav Res

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Estimates of the prevalence of sensitive attributes obtained through direct questions are prone to being distorted by untruthful responding. Indirect questioning procedures such as the Randomized Response Technique (RRT) aim to control for the influence of social desirability bias. However, even on RRT surveys, some participants may disobey the instructions in an attempt to conceal their true status. In the present study, we experimentally compared the validity of two competing indirect questioning techniques that presumably offer a solution to the problem of nonadherent respondents: the Stochastic Lie Detector and the Crosswise Model. For two sensitive attributes, both techniques met the Bmore is better^criterion. Their application resulted in higher, and thus presumably more valid, prevalence estimates than a direct question. Only the Crosswise Model, however, adequately estimated the known prevalence of a nonsensitive control attribute.

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Section: Abstract Randomized Response Technique Stochastic Lie Detementioning

confidence: 99%

Assessing the validity of two indirect questioning techniques: A Stochastic Lie Detector versus the Crosswise Model

Hoffmann

Musch

2015

Behav Res

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“…It is due to these threats that researchers studying sensitive topics are more often confronted with higher nonresponse rates and obtain more socially desirable answers than those studying neutral topics. The disturbances can lead to underreporting sensitive topics, thus making the data less valid (Lee 1993;Rasinski et al 1999).…”

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confidence: 99%

Meta-Analysis of Randomized Response Research

Lensvelt-Mulders¹,

Hox²,

Heijden³

et al. 2005

Sociological Methods & Research

354

347

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This article discusses two meta-analyses on randomized response technique (RRT) studies, the first on 6 individual validation studies and the second on 32 comparative studies. The meta-analyses focus on the performance of RRTs compared to conventional question-and-answer methods. The authors use the percentage of incorrect answers as effect size for the individual validation studies and the standardized difference score (d-probit) as effect size for the comparative studies. Results indicate that compared to other methods, randomized response designs result in more valid data. For the individual validation studies, the mean percentage of incorrect answers for the RRT condition is .38; for the other conditions, it is .49. The more sensitive the topic under investigation, the higher the validity of RRT results. However, both meta-analyses have unexplained residual variances across studies, which indicates that RRTs are not completely under the control of the researcher.

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“…When sensitive topics are studied, respondents often react in ways that negatively affect the validity of the data. Such a threat to the validity of the results is the respondents' tendency to give socially desirable answers to avoid social embarrassment and to project a positive self-image (Sudman and Bradburn, 1982;Rasinski, 1999). Warner (1965Warner ( , 1971 reasoned that the reluctance of the respondents to reveal sensitive or probably harmful information would diminish when respondents could be convinced that their *Author for correspondence: Utrecht University, Faculty of Social Sciences, Department of Methodology and Statistics, Utrecht, The Netherlands.…”

Section: Introductionmentioning

confidence: 99%

How to Improve the Efficiency of Randomised Response Designs

2005

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Abstract. This paper describes ways to maximise the efficiency of randomised response designs. When randomised response designs become more efficient their value as a tool to study sensitive topics will increase. An overview of the literature shows that when sensitive or incriminating topics are studied, the overall results of randomised response studies are more valid than the results of direct question designs. This positive effect is small, however, and randomised response designs are known to be less efficient than direct question designs, making it necessary to recruit larger samples. In this paper the efficiency of six randomised response methods (Warner's design, both forms of the unrelated question technique, the forced response technique, Moors's design and Mangat's improved model) will be compared relative to direct question designs. Using the right design parameters we can make randomised response studies up to twice as efficient. The forced response method and a special form of the unrelated question design are the most efficient designs until now.

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Methods of data collection, perceptions of risks and losses, and motivation to give truthful answers to sensitive survey questions

Cited by 57 publications

References 13 publications

Assessing the validity of two indirect questioning techniques: A Stochastic Lie Detector versus the Crosswise Model

Assessing the validity of two indirect questioning techniques: A Stochastic Lie Detector versus the Crosswise Model

Meta-Analysis of Randomized Response Research

How to Improve the Efficiency of Randomised Response Designs

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