Regency EF
A Computationally Efficient Method for Selecting a Split Questionnaire Design (304072)
*Matthew Stuart, Iowa State University
Cindy Yu, Iowa State University
Keywords: Split Questionnaire Design, Survey Methodology, Survey Sampling
Split questionnaire design (SQD) is a relatively new survey tool to reduce response burden and increase quality of responses. Among a set of possible SQD choices, a design is considered the best if it leads to the least amount of information loss quantified by the Kullback-Leibler divergence (KLD) distance, which requires computation of the distribution function for the observed data after integrating out all the missing variables in a particular SQD. For a typical survey questionnaire with a large number of categorical variables, this computation can become practically infeasible. Motivated by the Horvitz-Thompson estimator, we propose to approximate the distribution function of the observed in much reduced computation time and lose little information when comparing different SQDs. We contrive a thorough simulation study to test if the proposed approximation method can correctly identify the best SQD under several simulation scenarios created to cover different distribution shapes and correlation structures. Finally, the proposed approach is applied to the 2012 Pet Demographic Survey data. We demonstrate that the proposed method is computationally efficient and accurate.