scholarly journals Randomized response techniques for complex survey designs

2007 ◽  
Vol 48 (1) ◽  
pp. 131-141 ◽  
Author(s):  
Raghunath Arnab ◽  
Georg Dorffner
Keyword(s):  
Randomized Response ◽  
Complex Survey ◽  
Randomized Response Techniques ◽  
Survey Designs

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.

Randomized response techniques for a multi-level attribute using a single sensitive question

2016 ◽  
Vol 59 (1) ◽  
pp. 291-306 ◽  
Author(s):  
Shu-Hui Hsieh ◽  
Shen-Ming Lee ◽  
Su-Hao Tu
Keyword(s):  
Randomized Response ◽  
Sensitive Question ◽  
Multi Level ◽  
Randomized Response Techniques

scholarly journals Correction to: Trend estimation for complex survey designs of water chemistry indicators from Sierra Nevada Lakes

2018 ◽  
Vol 190 (11) ◽  
Author(s):  
L. A. H. Starcevich ◽  
T. McDonald ◽  
A. Chung-MacCoubrey ◽  
A. Heard ◽  
J. Nesmith ◽  
...  
Keyword(s):  
Water Chemistry ◽  
Sierra Nevada ◽  
Complex Survey ◽  
Trend Estimation ◽  
Survey Designs

Improved estimation methods for unrelated question randomized response techniques

2016 ◽  
Vol 46 (16) ◽  
pp. 8101-8112 ◽  
Author(s):  
Shen-Ming Lee ◽  
Ter-Chao Peng ◽  
Jean de Dieu Tapsoba ◽  
Shu-Hui Hsieh
Keyword(s):  
Estimation Methods ◽  
Randomized Response ◽  
Randomized Response Techniques ◽  
Unrelated Question
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