Comparing Proportions of Sensitive Items in Two Populations When Using Poisson and Negative Binomial Item Count Techniques

Kowalczyk, Barbara; Wieczorkowski, Robert

doi:10.22630/mibe.2017.18.1.07

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Many indirect methods of questioning have been developed to help in eliciting honest answers to sensitive questions and to eliminate the social desirability bias. Among them two methods are predominant: randomised response techniques (Warner 1965, Chaudhuri 2011, Imai 2015, Dihidar and Bhattacharya, 2017 and item count techniques (Miller, 1984, Blair and Imai, 2012, Chaudhuri and Christofides, 2007, Imai, 2011, Holbrook and Krosnick, 2010, Comsa and Postelnicu, 2013, Wolter and Laier, 2014, Kuha and Jackson, 2014, Trappman et al, 2014, Kowalczyk and Wieczorkowski, 2017, Krumpal et al, 2018. Item count techniques have many practical advantages (Tourangeau and Yan, 2007).…”

Section: Methodology and Questionnaire Designmentioning

confidence: 99%

New improved Poisson and negative binomial item count techniques for eliciting truthful answers to sensitive questions

Kowalczyk

Wieczorkowski²

2022

Statistics in Transition New Series

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Item count techniques (ICTs) are indirect survey questioning methods designed to deal with sensitive features. These techniques have gained the support of many applied researchers and undergone further theoretical development. Latterly in the literature, two new item count methods, called Poisson and negative binomial ICTs, have been proposed. However, if the population parameters of the control variable are not provided by the outside source, the methods are not very efficient. Efficiency is an important issue in indirect methods of questioning due to the fact that the protection of respondents’ privacy is usually achieved at the expense of the efficiency of the estimation. In the present paper we propose new improved Poisson and negative binomial ICTs, in which two control variables are used in both groups, although in a different manner. In the paper we analyse best linear unbiased and maximum likelihood estimators of the proportion of the sensitive attribute in the population in the introduced new models. The theoretical findings presented in the paper are supported by a comprehensive simulation study. The improved procedure allowed the increase of the efficiency of the estimation compared to the original Poisson and negative binomial ICTs.

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Section: Methodology and Questionnaire Designmentioning

confidence: 99%

New improved Poisson and negative binomial item count techniques for eliciting truthful answers to sensitive questions

Kowalczyk

Wieczorkowski²

2022

Statistics in Transition New Series

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“…As the problem deals with sensitive features only, π is commonly assumed to be less than 0.5 in the literature (see Imai, 2011;Tian et al, 2014;Kowalczyk, Wieczorkowski, 2017). To protect respondents' privacy properly and at the same time to ensure the not too high estimation error, Tian et al (2014) state that a good choice of λ is 2 (see Tian et al, 2014).…”

Section: Problem Of Optimal Allocation Based On ML Estimationmentioning

confidence: 99%

Optimal Allocation of the Sample in the Poisson Item Count Technique

Bernardelli

Kowalczyk

2018

Folia Oeconomica

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Indirect methods of questioning are of utmost importance when dealing with sensitive questions. This paper refers to the new indirect method introduced by Tian et al. (2014) and examines the optimal allocation of the sample to control and treatment groups. If determining the optimal allocation is based on the variance formula for the method of moments (difference in means) estimator of the sensitive proportion, the solution is quite straightforward and was given in Tian et al. (2014). However, maximum likelihood (ML) estimation is known from much better properties, therefore determining the optimal allocation based on ML estimators has more practical importance. This problem is nontrivial because in the Poisson item count technique the study sensitive variable is a latent one and is not directly observable. Thus ML estimation is carried out by using the expectation-maximisation (EM) algorithm and therefore an explicit analytical formula for the variance of the ML estimator of the sensitive proportion is not obtained. To determine the optimal allocation of the sample based on ML estimation, comprehensive Monte Carlo simulations and the EM algorithm have been employed.

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Comparing Proportions of Sensitive Items in Two Populations When Using Poisson and Negative Binomial Item Count Techniques

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Cited by 2 publications

References 6 publications

New improved Poisson and negative binomial item count techniques for eliciting truthful answers to sensitive questions

New improved Poisson and negative binomial item count techniques for eliciting truthful answers to sensitive questions

Optimal Allocation of the Sample in the Poisson Item Count Technique

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