A Bayesian Generalized Regression Approach for estimating parameters in the Item Sum Technique of sensitive surveys

Abstract

Most respondents find it difficult to respond to sensitive questions in surveys especially providing information that require one to disclose behaviors that are possibly against social norms. This study involves solving the problem of non-response or falsification of information in a sample survey as a result of sensitive questions using the Item Sum Technique (IST). The IST is a recent indirect method for collecting data for continuous sensitive characteristics and it is a quantitative variant of the Item Count Technique (ICT). The calibration method has previously been used to estimate the IST, but the method is design-based and only useful for sufficiently large domains. There are situations where the domain sample size is not adequate to meet the needs of the precision of the estimates, hence the smallarea estimation which is model-based may be needed. The Bayesian method is a model-based estimation method that tackles the problem of small area sample size. Results from simulation indicate the efficiency of the Bayesian Generalized Regression (BGREG) method for both small and large domain samples as compared with the Generalized Regression (GREG) and calibration method using three different criteria, AIC, BIC and MSE. A small sample survey on the use of substance abuse and internet gambling among university students from Nigeria was further used to show that the BGREG outperformed the GREG and Calibration methods using the AIC, BIC and MSE criterion.