Randomized response model
Randomized response model is a statistical technique used in survey research to reduce bias and increase respondent privacy. It was first introduced by Warner in 1965 as a way to collect sensitive information without compromising the anonymity of respondents.
The basic idea behind the randomized response model is to introduce random noise into the survey responses, making it difficult to determine the true answer for any individual respondent. This allows respondents to answer honestly without fear of being identified as the source of sensitive information.
For example, let’s say a survey asks respondents if they have ever used illegal drugs. Instead of asking for a direct ”yes” or ”no” response, the randomized response model might ask respondents to flip a coin and answer based on the outcome (e.g. ”heads = yes, tails = no”). This way, even if someone admits to drug use, it is impossible to know if they are telling the truth or simply following the randomization process.
Overall, the randomized response model is a valuable tool for researchers looking to collect accurate data on sensitive topics while protecting the privacy of their respondents.
For more information on the randomized response model, you can visit Wikipedia.