Design and Analysis of the Randomized Response Technique

Blair, Graeme; Imai, Kosuke; Zhou, Yang-Yang

doi:10.1080/01621459.2015.1050028

Cited by 141 publications

(126 citation statements)

References 77 publications

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“…The first extension includes covariates in a small‐area context. We assume that

y_{i j k} | π_{i j k} 1, π_{i j k 2}, ν_{i 1}, ν_{i 2} \overset{i n d}{\sim} Bernoulli {p_{i j} π_{i j k 1} + (1 - p_{i j}) π_{i j k 2}},

k = 1, ⋯, m_{i j}, j = 1, ⋯, n_{i}, i = 1, ⋯, ℓ,

where

π_{i j k s} = \frac{\exp ({bold-italicx}_{i j k}^{'} β_{s} + ν_{i s})}{1 + \exp ({bold-italicx}_{i j k}^{'} β_{s} + ν_{i s})}, ν_{i s} \sim \overset{i n d}{\sim} Normal (0, σ_{s}^{2}), s = 1, 2 .

The comments in Blair et al () for a single area are relevant.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Analysis of a Sensitive Proportion for a Small Area

Nandram

2018

Int Statistical Rev

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Summary Without accounting for sensitive items in sample surveys, sampled units may not respond (nonignorable nonresponse) or they respond untruthfully. There are several survey designs that address this problem and we will review some of them. In our study, we have binary data from clusters within small areas, obtained from a version of the unrelated‐question design, and the sensitive proportion is of interest for each area. A hierarchical Bayesian model is used to capture the variation in the observed binomial counts from the clusters within the small areas and to estimate the sensitive proportions for all areas. Both our example on college cheating and a simulation study show significant reductions in the posterior standard deviations of the sensitive proportions under the small‐area model as compared with an analogous individual‐area model. The simulation study also demonstrates that the estimates under the small‐area model are closer to the truth than for the corresponding estimates under the individual‐area model. Finally, for small areas, we discuss many extensions to accommodate covariates, finite population sampling, multiple sensitive items and optional designs.

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“…The first extension includes covariates in a small‐area context. We assume that

y_{i j k} | π_{i j k} 1, π_{i j k 2}, ν_{i 1}, ν_{i 2} \overset{i n d}{\sim} Bernoulli {p_{i j} π_{i j k 1} + (1 - p_{i j}) π_{i j k 2}},

k = 1, ⋯, m_{i j}, j = 1, ⋯, n_{i}, i = 1, ⋯, ℓ,

where

π_{i j k s} = \frac{\exp ({bold-italicx}_{i j k}^{'} β_{s} + ν_{i s})}{1 + \exp ({bold-italicx}_{i j k}^{'} β_{s} + ν_{i s})}, ν_{i s} \sim \overset{i n d}{\sim} Normal (0, σ_{s}^{2}), s = 1, 2 .

The comments in Blair et al () for a single area are relevant.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Analysis of a Sensitive Proportion for a Small Area

Nandram

2018

Int Statistical Rev

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“…For example, one can generalize this method to other designs of list experiments such as double list experiments (Droitcour et al 1991;Glynn 2013) or other indirect questioning methods such as endorsement experiments (Bullock, Imai, and Shapiro 2011) and randomized response methods (Warner 1965;Blair, Imai, and Zhou 2014). These and other related research projects are currently being conducted by the authors and other researchers.…”

Section: Discussionmentioning

confidence: 99%

Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models

Imai

Park

Greene³

2015

Polit. anal.

Self Cite

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Edited by R. Michael AlvarezThe list experiment, also known as the item count technique, is becoming increasingly popular as a survey methodology for eliciting truthful responses to sensitive questions. Recently, multivariate regression techniques have been developed to predict the unobserved response to sensitive questions using respondent characteristics. Nevertheless, no method exists for using this predicted response as an explanatory variable in another regression model. We address this gap by first improving the performance of a naive two-step estimator. Despite its simplicity, this improved two-step estimator can only be applied to linear models and is statistically inefficient. We therefore develop a maximum likelihood estimator that is fully efficient and applicable to a wide range of models. We use a simulation study to evaluate the empirical performance of the proposed methods. We also apply them to the Mexico 2012 Panel Study and examine whether vote-buying is associated with increased turnout and candidate approval. The proposed methods are implemented in open-source software.

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“…The method consists of two parts: the first part is the multivariate data disguising technique used for data collection; the second part is the modified ID3 decision tree building algorithm used for building a classifier from the disguised data. The framework [9] conducts a multivariate regression analysis to generate predicted probabilities for the sensitive item. They showed to use the sensitive attitude inferred from the multivariate regression analysis as a predictor for an outcome regression model.…”

Section: Data Modificationmentioning

confidence: 99%

A Comprehensive Survey on Privacy Preserving Big Data Mining

Srijayanthi¹,

Sethukkarasi²

2017

IJCATR

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Abstract:In recent years, privacy preservation of large scale datasets in big data applications such as physical, biological and biomedical sciences is becoming one of the major concerned issues for mining useful information from sensitive data. Preservation of privacy in data mining has ascended as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Privacy-Preserving Data Mining (PPDM) aids to mine information and reveals patterns from large dataset protecting private and sensitive data from being exposed. With the advent of varied technologies in data collection, storage and processing, numerous privacy preservation techniques have been developed. In this paper, we provide a review of the state-of-the-art methods for privacy preservation

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Design and Analysis of the Randomized Response Technique

Cited by 141 publications

References 77 publications

Bayesian Analysis of a Sensitive Proportion for a Small Area

Bayesian Analysis of a Sensitive Proportion for a Small Area

Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models

A Comprehensive Survey on Privacy Preserving Big Data Mining

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