An Efficient Method for Estimating Population Parameters Using Split Questionnaire Design

Abstract

Abstract. The effect of survey questionnaire length on precision of survey statistics has been discussed in several studies. It is generally concluded that the lengthy questionnaire leads to increase non-sampling errors, especially nonresponse rate. Split questionnaire method has been introduced as a solution to decrease the response burden and nonresponse rate, involves splitting the questionnaire into subquestionnaires and then administering these subquestionnaires to different subsets of the original sample. In this paper, we suggest a method for splitting long questionnaire and analyzing resulting data, using small area estimation. The general idea behind this approach is to construct some socio-demographic or geographic small areas to apply small area estimation to improve the efficiency of survey statistics. Our new approach is supported by a simulation study based on a real dataset of the 2011 Iran Income and Expenditure survey, in which we show our method provides more reliable statistics than existing methods.

Split Questionnaire Design: An Application in Household Expenditure and Income Survey of Iran

“…For matrix survey sampling with overlapping subsets of questions, Merkouris (2015) proposed an efficient estimation method based on the principle of best linear unbiased estimation, generates composite optimal regression estimators of population totals. Kamgar and Navvabpour (2017) proposed a strategy to split long questionnaires and estimate the population parameters based on data collected from subquestionnaires. They examined the four methods of small-area estimation, Horvitz-Thompson estimation, regression method, and multiple imputation, and concluded that of these four methods if the appropriate auxiliary variables are used from the registers or previous census, the small-area estimation method gives us a more accurate estimate.…”