Franklin's randomized response model with correlated scrambled variables

We propose two types of estimators that are analogous to Franklin's model. One estimator is derived by concentrating on the row averages of the responses, and another is obtained by concentrating on the column averages of the observed responses. In the latter case we have two responses per respondent from a bi‐variate normal distribution. The proposed estimator based on row averages, by making use of negatively correlated random numbers from a multivariate density, is always more efficient than the corresponding Franklin's estimator. In the case of the proposed estimator based on column averages, we found that the use of positively correlated random numbers from a bivariate density can lead to the most efficient estimator. We also discuss results which are observed by making use of three responses per respondent. When the three responses are recorded, three independent normal densities are derived from three correlated variables. The findings are supported based on analytical, numerical, and simulation studies. A simulation study was done to determine the minimum sample size required to produce nonnegative estimates of the population proportion of a sensitive characteristic, and to investigate the 95% nominal coverage by the interval estimates. Ultimately at the end, one best estimator is suggested. A very neat and clean derivations of theoretical results and discussion of numerical and simulation studies are documented in Data S1.