Main Article Content
Randomized response technique introduces anonymity into subjects' responses hence encouraging more honest responses. In quantitative randomized response model, additive and multiplicative models have been developed to reduce bias. However, additive and multiplicative models may not be sufficient to reduce this bias so the generalized optional scrambling randomized response model proposed is able to reduce these problems. We also improved mean estimation utilizing information from a non-sensitive auxiliary variable by way of ratio and regression estimators in the proposed model.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).