Estimation of Hidden Logits Using Several Randomized Response Techniques

Abstract

In survey sampling, we aspire to obtain sound and consistent responses, which are not achieved while dealing with sensitive issues. Frequently, respondents give elusive responses to sensitive questions, so we employ randomized response techniques that facilitate finding an appropriate proportion of socially sensitive characteristics. In the present study, we proposed a hidden logit estimation method using Huang, Warner, and Mangat’s randomized response techniques. This study depicts that the estimates become closer to the standard logits as the values of p increase. We found that the hidden logit estimates obtained by the Huang randomized response technique were nearer to the parametric values, in contrast to the other existing techniques, and we demonstrate an increase in accuracy as well. The simulation-based AIC and SIC values are used to assess model performance. We found that the Huang model is the best model for the proposed hidden logit method. This paper contributes towards the application of logistic models in the case of sensitive or socially stigmatized issues.

Dual-type quantile regression approach for mean estimation incorporating sampled and non-sampled data

“…It is assumed that this sensitive variable is observed in sampled units through nonstandard survey techniques, implemented to improve respondent response rate. These methods are linked to the randomized response theory pioneered by Warner [26] and thoroughly demonstrated by Asma et al [27] . Moreover, a numerical study is conducted to assess the merits of the proposed dual class for the variable of sensitivity.…”