A Generalized Regression Estimation of the Item Sum Technique in Sensitive Surveys
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Date
2019
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Abstract
Survey researchers often nd it di cult to collect reliable data of human populations, yet the
validity of any research depends mainly on the accuracy of self-reported behavior especially
when the respondents are to re ect about sensitive issues or highly personal matter. It is
therefore important to develop methods of improving interviewees responses in any survey.
The Item Sum Technique (IST) is the most recent indirect questioning method and it is a
variant of the Item Count Technique (ICT) which can be used only for qualitative responses.
The aim of this study is to estimate the sensitive characteristic when using the IST especially
if two or more sensitive questions are investigated. It also focuses on the theoretical framework
which includes the introduction of a classical method called the Generalized Regression model
(GREG) using the IST. The e ciency of the GREG method was ascertained in comparison
to the Calibration estimator by an extensive simulation study. Results from the statistical
analysis indicates that the GREG estimator competes well with the calibration method and
can further be used for a small sample size or data that is not normally distributed.
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Keywords
Calibration estimator, Indirect questioning method, Item count technique, Sensitive questions, Small sample