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About 10,000 papers for “Randomized response”

Randomized response

Bouts, J., Van der Heijden, P.G.M., Landsheer, J.A. · Data Archiving and Networked Services (DANS) · 1998

Cited by 0Open access

Randomized response techniques

Gregor Kočan · Digital Repository (National Repository of Grey Literature) · 2025

This paper focuses on randomized response methods, which are used for collecting data on sensitive questions. These methods are mainly applied when respondents face questions they might not want to answer truthfully, for example due to shame or fear. In this paper, we analyze three basic types of these methods and how estimates are calculated from them. We also study their properties, such as unbiasedness and variance, and discuss how to choose the right settings for these methods in practice.

Cited by 0

Randomized response methods

van der Heijden, P.G.M., Cruyff, Maarten, Bockenholt, U. · Utrecht University Repository (Utrecht University) · 2014

In survey research it is often problematic to ask people sensitive questions because they may refuse to answer or they may provide a socially desirable answer that does not reveal their true status on the sensitive question. To solve this problem Warner (1965) proposed randomized response (RR). Here a chance mechanism hides why respondents say “yes” or “no” to the question being asked. Thus far RR has been mainly used in research to estimate the prevalence of sensitive characteristics. RR can be used in, for example, self-report studies on criminal behavior (compare, e.g., the two meta-analyses in Lensvelt-Mulders et al.2005). In the Netherlands, much research has been funded by the Expert Centre for Law Enforcement of the Ministry of Justice in the area of regulatory noncompliance in areas as the taxi-driving law, under regulations for food retailers, the law of individual rent subsidy, and the law on agricultural chemicals.

Cited by 0

RE: RANDOMIZED RESPONSE

J. E. Connect · American Journal of Epidemiology · 1986

Journal Article RE: RANDOMIZED RESPONSE Get access J. E. Connect J. E. Connect Division of Biometry 1226 Mayo University of MinnesotaMinneapolis MN 55455 Search for other works by this author on: Oxford Academic PubMed Google Scholar American Journal of Epidemiology, Volume 123, Issue 4, April 1986, Pages 755–756, https://doi.org/10.1093/oxfordjournals.aje.a114301 Published: 01 April 1986

Cited by 0

Randomized Response Techniques

Arijit Chaudhuri, Sanghamitra Pal, Dipika Patra · 2024

This book discusses appropriate procedures of randomized response techniques for analyzing data from queries dealing with sensitive issues.

Cited by 3Open access

Randomized Response Instruments

Michael A. Bailey · Cambridge University Press eBooks · 2024

This chapter highlights the critical importance of having the right kind of data for selection models that address nonignorable nonresponse. In general, we need a variable that is included in our response model and excluded from our outcome model. The best approach is creating a randomized response instrument that affects whether someone responds, but does not affect the content of their response. In many polling contexts, it is easy to create randomized response instruments. The pollster simply needs to figure out some protocol that affects response rate and then randomize it. Section 10.1 makes it clear that knowing the correct functional form is not enough to save a selection model. Section 10.2 highlights the difficulty of using observational response instruments. Section 10.3 discusses how and why to create randomized response instruments. Section 10.4 shows how to use randomized response instruments in a simple test for diagnosing nonignorable nonresponse. Section 10.5 shows how randomized response instruments enable us to use the full suite of selection models even when we do not observe data for nonrespondents.

Cited by 0

Randomized response techniques

Sarjinder Singh · Model Assisted Statistics and Applications · 2014

Collection of data on sensitive characteristics from human populations is not an easy task. For example sensitive questions: (a) Are you an Alawite? (b) Are you gay? (c) Have you ever molested a child? (d) Have you underreported your income on your tax return? (e) Do you smoke marijuana?, and (f) Have you ever cheated on an exam?, etc. These questions are not likely to be responded honestly by the respondents if they are asked using direct question survey methods. The randomized response technique was first introduced by Warner (1965) to deal with a problem of estimation of such sensitive characteristics in a finite population. This technique enables respondents to provide truthful information in anonymity on sensitive or highly personal questions without endangering their privacy. To promote the pioneer idea of Warner (1965), we have devoted the present issue of the Journal Model Assisted Statistics and Applications on a special topic, “Randomized Response Techniques.” A number of prominent authors who work in the area of randomized response sampling have contributed towards to the special issue, and their details and contributions are explained in brief as follows:

Cited by 1Open access

Optimal Randomized Response Models

Stanley L. Warner · International Statistical Review · 1976

Summary Randomized response methods allow different kinds and degrees of privacy depending upon models and parameters. A theoretical approach to efficiency is suggested based on minimizing variance for given privacy requirements. Privacy requirements are indexed by measures from information theory, variances are expressed in terms of these measures, and variances of competing models are compared when the models satisfy given privacy requirements. The approach is illustrated through developing minimum variances under given information restrictions for a general dichotomous-population dichotomous-response model and then applying these results to compare two familiar models.

Cited by 11

Multinomial randomized response models

Morten Moshagen · Univ. Duesseldorf: Duesseldorfer Dokumenten- und Publikationsserver · 2008

Although interviews and questionnaires are widely used in behavioral research, the validity of self-reports of sensitive attitudes and behaviors suffers from the tendency of individuals to distort their responses towards their perception of what is socially acceptable. As a consequence, studies self-report measures consistently underestimate the prevalence of undesirable attitudes or behaviors and overestimate the prevalence of desirable attitudes or behaviors. The randomized response technique (RRT) was developed as a means to overcome this problem by adding random noise to the responses such that there is no direct link between the response an individual provides and his or her true status. Owing to the randomization, the RRT guarantees the confidentiality of responses and encourages more honest responding. Although the superiority of the RRT over more traditional data collection techniques has been repeatedly demonstrated, the RRT is rarely used in substantive research on sensitive issues. Reasons for this dearth of RRT applications include the susceptibility of the RRT to respondents that fail to comply with the instructions, practical problems when assessing multiple attributes in a single study, and the lack of a freely available and easy to use software program implementing randomized response models. The present thesis addressed these problems by extending and validating a cheating detection modification (CDM) of the RRT, showing how to assess multiple attributes with just a single randomization process, and developing a software program tailored for the needs of a wider audience wishing to use RRT models in practice. In Study I, a multinomial reformulation of the CDM was utilized to obtain information about dental hygiene habits among male and female Chinese college students. The results showed that the RRT can substantially improve the validity of prevalence estimates of sensitive behaviors as compared to a traditional direct questioning format. In Study II, computer simulations were performed in order to examine the statistical efficiency, the statistical power, and the robustness to violations of assumptions of the CDM. It was demonstrated that violations of the assumptions underlying the model lead to biased parameter estimates. Given that the CDM is just-identified and will fit the data perfectly irrespective of violations of assumptions, an extension of the CDM that has the capability to detect violations of assumptions was proposed and examined. The simulation studies further called attention to the importance of choosing diverging randomization probabilities to improve statistical efficiency and power. Study III addressed the problem of assessing multiple attributes in a single study. Using the RRT with multiple attributes seems to either require multiple initializations of the randomization device rendering the administration of the RRT tedious and complicated, or effectively cancels the privacy protection feature of the RRT. To overcome this problem, a particular distribution scheme of the outcomes of the randomization device was developed, which simultaneously avoids the need for multiple randomization processes and maintains privacy protection. Study IV was concerned with the shortcoming of the CDM that it is not able to distinguish whether cheating participants have or do not have a critical attribute. To overcome this problem, an extension of Mangat's (1994) variant of the RRT was proposed. This extension is able to estimate the extent of untruthful responding of those participants who unequivocally carry the sensitive attribute. In Study V, thirteen variants of the RRT are reviewed and it is shown how a common multinomial modeling framework can be adopted for these models. Based on the multinomial reformulation of the models, a software program was developed that allows for the analysis of all of the RRT models in single and multiple groups. Additionally, the program includes an option to perform a-priori and post hoc power analyses. Taken together, the present thesis tackled a series of concerns precluding a wider use of models suitable to gain more valid estimates of the prevalence of sensitive attitudes and behaviors.

Cited by 1

Randomized response survey procedure.

Muhammad Azeem (211501), Javid Shabbir (3183468), Najma Salahuddin (17296140) et al. · Figshare · 2023

<div><p>In social surveys, the randomized response technique can be considered a popular method for collecting reliable information on sensitive variables. Over the past few decades, it has been a common practice that survey researchers develop new randomized response techniques and show their improvement over previous models. In majority of the available research studies, the authors tend to report only those findings which are favorable to their proposed models. They often tend to hide the situations where their proposed randomized response models perform worse than the already available models. This approach results in biased comparisons between models which may influence the decision of practitioners about the choice of a randomized response technique for real-life problems. We conduct a neutral comparative study of four available quantitative randomized response techniques using separate and combined metrics of respondents’ privacy level and model’s efficiency. Our findings show that, depending on the particular situation at hand, some models may be better than the other models for a particular choice of values of parameters and constants. However, they become less efficient when a different set of parameter values are considered. The mathematical conditions for efficiency of different models have also been obtained.</p></div>

Cited by 0Open access

Synthesized randomized response techniques

Isaac O. Adeniyi, Olusegun Sunday Ewemooje · Scientific African · 2023

Reducing response bias in survey research is important in ensuring that the data collected accurately represents the population of interest. This study proposes the synthesized random response technique (SRRT) estimator as an efficient method to reduce response bias in this regard. We compared the proposed SRRT estimator with other conventional techniques, such as the Hybrid Tripartite Randomized Response Technique (HTRRT), the Alternative Tripartite Randomized Response Technique (ATRRT), and the Two-Stage Unrelated Question Randomized Response Model for Estimating Prevalence of Stigmatized Attributes (TSUQRRMEPSA). Our results show that the proposed SRRT estimator consistently produces more efficient estimates than other conventional techniques as sample sizes increase. As a result, the proposed SRRT estimator have the potential to enhance the accuracy of survey data, which is sensitive for informed decision-making. Hence, the proposed method is more efficient in reducing response bias than other conventional methods.

Cited by 2Open access

Randomized Item Response Model

Jean-paul Fox · 2012

The collection of data trough surveys on highly personal and sensitive issues may lead to answering refusals and false responses, making inferences difficult. Obtaining valid and reliable information depends on the cooperation of the respondents, and the willingness of the respondents depends on the confidentiality of their responses. Warner (1965) developed a data collection procedure, the randomized response technique (RR), that allows researchers to obtain sensitive information while guaranteeing privacy to respondents. For example, a randomizing device is used to select a question from a group of questions and the respondent answers the selected question. The respondent is protected since the interviewer will not know which question is being answered. In Warner’s and related approaches, there is only sufficient information from a sample of respondents to estimate proportions and related confidence intervals in the population. When using item response theory, individual attitudes can be estimated and other statistical analyses are possible. An answer of a respondent concerns a real answer to the sensitive question with

Cited by 0

Multi-Dimensional Randomized Response

Josep Domingo‐Ferrer, Jordi Soria-Comas · arXiv (Cornell University) · 2020

In our data world, a host of not necessarily trusted controllers gather data on individual subjects. To preserve her privacy and, more generally, her informational self-determination, the individual has to be empowered by giving her agency on her own data. Maximum agency is afforded by local anonymization, that allows each individual to anonymize her own data before handing them to the data controller. Randomized response (RR) is a local anonymization approach able to yield multi-dimensional full sets of anonymized microdata that are valid for exploratory analysis and machine learning. This is so because an unbiased estimate of the distribution of the true data of individuals can be obtained from their pooled randomized data. Furthermore, RR offers rigorous privacy guarantees. The main weakness of RR is the curse of dimensionality when applied to several attributes: as the number of attributes grows, the accuracy of the estimated true data distribution quickly degrades. We propose several complementary approaches to mitigate the dimensionality problem. First, we present two basic protocols, separate RR on each attribute and joint RR for all attributes, and discuss their limitations. Then we introduce an algorithm to form clusters of attributes so that attributes in different clusters can be viewed as independent and joint RR can be performed within each cluster. After that, we introduce an adjustment algorithm for the randomized data set that repairs some of the accuracy loss due to assuming independence between attributes when using RR separately on each attribute or due to assuming independence between clusters in cluster-wise RR. We also present empirical work to illustrate the proposed methods.

Cited by 0Open access