Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/6/609 |
| _version_ | 1797541604348657664 |
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| author | María del Mar Rueda Beatriz Cobo Antonio Arcos |
| author_facet | María del Mar Rueda Beatriz Cobo Antonio Arcos |
| author_sort | María del Mar Rueda |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:18:19Z |
| format | Article |
| id | doaj.art-5d078c7ffe69402d9d2b580dda41a4d9 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T13:18:19Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-5d078c7ffe69402d9d2b580dda41a4d92023-11-21T10:13:58ZengMDPI AGMathematics2227-73902021-03-019660910.3390/math9060609Regression Models in Complex Survey Sampling for Sensitive Quantitative VariablesMaría del Mar Rueda0Beatriz Cobo1Antonio Arcos2Department of Statistics and Operations Research, University of Granada, 18071 Granada, SpainDepartment of Quantitative Methods for the Economy and Business, University of Granada, 18011 Granada, SpainDepartment of Statistics and Operations Research, University of Granada, 18071 Granada, SpainRandomized 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.https://www.mdpi.com/2227-7390/9/6/609regression modelsrandomized response techniquescomplex sampling designs |
| spellingShingle | María del Mar Rueda Beatriz Cobo Antonio Arcos Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables Mathematics regression models randomized response techniques complex sampling designs |
| title | Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables |
| title_full | Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables |
| title_fullStr | Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables |
| title_full_unstemmed | Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables |
| title_short | Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables |
| title_sort | regression models in complex survey sampling for sensitive quantitative variables |
| topic | regression models randomized response techniques complex sampling designs |
| url | https://www.mdpi.com/2227-7390/9/6/609 |
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