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Abstract Researchers often have problems getting truthful responses when asking questions related to sensitive personal medical, financial, or societal topics. Questions related to income, tax evasions, pregnancies or abortion, sexual tendencies, personal hygiene, or drug usage, causes respondents to either provide incorrect information or avoid getting questioned altogether. The premise of the randomized response technique is that the respondent uses a randomization device that would mask his true answer to the sensitive question so that the interviewer cannot know the true value to the sensitive question of the respondent. The randomized response technique was developed by Warner (1965) to guarantee the respondents privacy, thus encouraging more truthful cooperation.The randomized response techniques allow the researcher to have a better estimate of the study question in terms of respondent’s cooperation and truthfulness.The randomized response technique was first designed for dichotomous variables; however, it was later extended to multinomial, and quantitative variables. Further improvements on the randomized response models were seeking to minimize the variance of their estimator, as it is by design greater than that of the direct questioning method. The present study is focused on randomized response models that obtain information on sensitive quantitative variables.The study offers a review of some of the most prominent randomized response models for quantitative data. |