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This paper considers the problem of estimation for binomial proportions of sensitive attributes in the population of interest. Randomized response techniques are suggested for protecting the privacy of respondents and reducing the response bias while eliciting information on sensitive attributes. By applying the Wilson (J Am Stat Assoc 22:209–212, 1927) score approach for constructing confidence intervals, various probable point estimators and confidence interval estimators are suggested for the common structures of randomized response procedures. In addition, efficiency comparisons are carried out to study the performances of the proposed estimators for both the cases of direct response surveys and randomized response surveys. Circumstances under which each proposed estimators is better are also identified.

Warner’s (J Am Stat Assoc 60:63–69, 1965) pioneering ‘randomized response’ (RR) technique (RRT), useful in unbiasedly estimating the proportion of people bearing a sensitive characteristic, is based exclusively on simple random sampling (SRS) with replacement (WR). Numerous developments that follow from it also use SRSWR and employ in estimation the mean of the RR’s yielded by each person everytime he/she is drawn in the sample. We examine how the accuracy in estimation alters when instead we use the mean of the RR’s from each distinct person sampled and also alternatively if the Horvitz and Thompson’s (HT, J Am Stat Assoc 47:663–685, 1952) method is employed for an SRSWR eliciting only, a single RR from each. Arnab’s (1999) approach of using repeated RR’s from each distinct person sampled is not taken up here to avoid algebraic complexity. Pathak’s (Sankhya 24:287–302, 1962) results for direct response (DR) from SRSWR are modified with the use of such RR’s. Through simulations we present relative levels in accuracy in estimation by the above three alternative methods.

In the present investigation, a new forced quantitative randomized response (FQRR) model has been proposed. Both situations when the values of the forced quantitative response are known and unknown are studied. The forced qualitative randomized response models due to Liu and Chow (J Am Stat Assoc 71:72–73, 1976a, Biometrics 32:607–618, 1976b) and Stem and Steinhorst (J Am Stat Assoc 79:555–564, 1984) are shown as a special case of the situation when the value of the forced quantitative randomized response is simply replaced by a forced “yes” response. The proposed FQRR model remains more efficient than the recent Bar-Lev et al. (Metrika, 60:255–260, 2004), say BBB model. The relative efficiency of the proposed FQRR model with respect to the existing competitors, like the BBB model, has been investigated under different situations. No doubt the present model will lead to several new developments in the field of randomized response sampling. The proposed FQRR model will encourage researchers/scientists to think more on these lines.

In this paper we present a randomized response technique which can be used to estimate the proportion of individuals having two sensitive characteristics at the same time. The information obtained from this procedure can be used for weighting (post-stratification) purposes. © Springer-Verlag 2005. 62 1 53 63 Cited By :13

This paper applies Kim and Warde’s (2004) stratified Warner’s randomized response model to Mangat and Singh’s (1990) two-stage randomized response model. The proposed stratified randomized response model has an optimal allocation and a large gain in precision. Hence, the estimator based on the proposed method is more efficient than Kim and Warde’s (2004) and Mangat and Singh’s (1990) estimators under the conditions presented in both the case of completely truthful reporting and that of not completely truthful reporting by the respondents.

Standard randomized response (RR) models deal primarily with surveys which usually require a ‘yes’ or a ‘no’ response to a sensitive question, or a choice for responses from a set of nominal categories. As opposed to that, Eichhorn and Hayre (1983) have considered survey models involving a quantitative response variable and proposed an RR technique for it. Such models are very useful in studies involving a measured response variable which is highly ‘sensitive’ in its nature. Eichhorn and Hayre obtained an unbiased estimate for the expectation of the quantitative response variable of interest. In this note we propose a procedure which uses a design parameter (controlled by the experimenter) that generalizes Eichhorn and Hayre’s results. Such a procedure yields an estimate for the desired expectation which has a uniformly smaller variance.

Christofides (2003) has given an improved modification of Warner’s (1965) pioneering randomized response (RR) technique in estimating an unknown proportion of people bearing a sensitive characteristic in a given community. As both these RR devices are shown to yield unbiased estimators based only on simple random sampling (SRS) with replacement (WR) but in practice samples are mostly taken with unequal selection probabilities without replacement (WOR), here we present methods of estimation when Christofides’ RR data are available from unequal probability samples. Warner’s (1965) RR device was earlier shown by Chaudhuri (2001) to be applicable in complex surveys. For completeness we present estimators for the variance of our estimator and also describe what to do if some people opt to divulge truths.

The collection of data through personal interview surveys on sensitive issues such as induced abortions, drug abuse and family income is a serious issue in social sciences. Warner (1965) was the first to propose an ingenious method to collect information on such questions without disclosing the privacy of the respondents, which is called randomized response technique. In the present investigation, a new stochastic randomized response technique is proposed that elicits greater cooperation from the respondents, and can be made more efficient than Warner's (1965) and Mangat and Singh (1990) method by selecting certain parameters of the proposed randomization device.

Comparisons between different randomized response strategies have already been performed by several workers but all have concentrated solely on comparing the variances of the appropriate estimators. A very little attention has been paid by these workers to the degree of privacy protection offered to the interviewees. In the present paper, an attempt has been made in this direction and some important randomized response strategies have been compared with the Warner's model, taking into account the aspect of privacy protection.

In this paper, a simple and obvious procedure is presented that allows to estimate π, the population proportion of a sensitive group, in addition to T, the probability that the respondents belonging to the sensitive group tell the truth whenever questioning directly. Properties of the estimators of π and T as well as sample size allocation are studied. And, efficiency comparisons are carried out to investigate the performance of the proposed method. It is found that the proposed strategy is more efficient than Warner's (1965) strategy, and has an additional advantage of deciding the optimal survey technique for practical situations.

This article considers the asymptotic estimation theory for the proportion in randomized response survey usinguncertain prior information (UPI) about the true proportion parameter which is assumed to be available on the basis of some sort of realistic conjecture. Three estimators, namely, the unrestricted estimator, the shrinkage restricted estimator and an estimator based on a preliminary test, are proposed. Their asymptotic mean squared errors are derived and compared. The relative dominance picture of the estimators is presented.

General procedures are described to generate quantitative randomized response (RR) required to estimate the finite population total of a sensitive variable. Permitting sample selection with arbitrary probabilities a formula for the mean square error (MSE) of a linear estimator of total based on RR is noted indicating the simple modification over one that might be based on direct response (DR) if the latter were available. A general formula for an unbiased estimator of the MSE is presented. A simple approximation is proposed in case the RR ratio estimator is employed based on a simple random sample (SRS) taken without replacement (WOR). Among sampling strategies employing unbiased but not necessarily linear estimators based on RR, certain optimal ones are identified under two alternative models analogously to well-known counterparts based on DR, if available. Unlike Warner’s (1965) treatment of categorical RR we consider quantitative RR here.