Unbiased estimator modeling in unrelated dichotomous randomized response

The unrelated design has been shown to improve the efficiency of a randomized response method and reduces respondents’ suspicion. In the light of this, the paper proposes a new Unrelated Randomized Response Model constructed by incorporating an unrelated question into the alternative unbiased estimator in the dichotomous randomized response model proposed by Ewemooje in 2019. An unbiased estimate and variance of the model are thus obtained. The variance of the proposed model decreases as the proportion of the sensitive attribute ?_A and the unrelated attribute ?_U increases, in contrast to the earlier Ewemooje model, whose variance increases as the proportion of the sensitive attribute increases. The relative efficiency of the proposed model over the earlier Ewemooje model decreases as ?_U increases when 0.1??_A? 0.3 and increases as ?_U increases when 0.35??_A? 0.45. Application of the proposed model also revealed its efficiency over the direct method in estimating the prevalence of examination malpractices among university students; the direct method gave an estimate of 19.0%, compared to the proposed method’s estimate of 23.0%. Hence, the proposed model is more efficient than the direct method and the earlier Ewemooje model as the proportion of people belonging to the sensitive attribute increases.