Sampling weights are calibrated according to the theory of calibration when the sum of population total for auxiliary variables is known. Under known population, totals for auxiliary variables and some conditions Devile and Sarndal showed that generalized regression estimators could approximate calibration estimators and their variances. In this paper, under unknown population totals for auxiliary variables, an estimator for the population total is proposed and its variance is obtained. It is shown that our estimator for the population total is more efficient than the Horvitz-Thompson estimators by theoretically and simulation results.

In survey sampling, the respondents often do not state the actual response to the sensitive questions. Randomized response techniques have been designed to protect the privacy of responses. This paper focused on the randomized response technique for qualitative variables based on Simmons method. Using idea of repeating answer, the new repeated randomized response technique is introduced. Its efficiency is compared with the Simmons technique. Proportion of student cheating in Shahid Chamran University is estimated using the proposed technique.

Judgment post-stratification is a method of using additional information of ranking in the simple random sampling, to increase the efficiency of the estimators of population parameters. In this paper, we use judgment post-stratification instead of simple random sampling in stratums of stratified sampling, and present new estimators for population mean. Then, we compare the proposed estimators with random stratified mean estimator by using a simulation study. The simulation results show that the proposed estimators perform better than the random stratified mean estimator in most of the cases.