Randomized Response and New Thoughts on Politz–Simmons Technique

2016 ◽  
pp. 233-251 ◽  
Author(s):  
T.J. Rao ◽  
J. Sarkar ◽  
B.K. Sinha
Keyword(s):  
Randomized Response

Applicants’ Self-Presentational Behavior

2012 ◽  
Vol 11 (2) ◽  
pp. 77-85 ◽  
Author(s):  
Anne Jansen ◽  
Cornelius J. König ◽  
Eveline H. Stadelmann ◽  
Martin Kleinmann
Keyword(s):  
Personnel Selection ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Self Presentation ◽  
Actual Use

This study contributes to the literature on self-presentation by comparing recruiters’ expectations about applicants’ self-presentational behaviors in personnel selection settings to applicants’ actual use of these behaviors. Recruiters (N = 51) rated the perceived appropriateness of 24 self-presentational behaviors. In addition, the prevalence of these behaviors was separately assessed in two subsamples of applicants (N1 = 416 and N2 = 88) with the randomized response technique. In line with the script concept, the results revealed that recruiters similarly evaluated the appropriateness of specific self-presentational behaviors and that applicants’ general use of these behaviors corresponded to recruiters’ shared expectations. The findings indicate that applicants who use strategic self-presentational behaviors may just be trying to fulfill situational requirements.

Optionally Randomized Response Techniques

1985 ◽  
Vol 34 (3-4) ◽  
pp. 225-230 ◽  
Author(s):  
Arijit Chaudhuri ◽  
Rahul Mukerjee
Keyword(s):  
Randomized Response ◽  
Randomized Response Techniques

scholarly journals Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 609
Author(s):  
María del Mar Rueda ◽  
Beatriz Cobo ◽  
Antonio Arcos
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Regression Coefficients ◽  
Randomized Response ◽  
Statistical Tool ◽  
Complex Survey ◽  
Complex Sample ◽  
Qualitative Variables ◽  
World Survey ◽  
Sensitive Variable

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.

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