Split Questionnaire Design for Massive Surveys

Abstract

Companies are conducting more and longer surveys than ever before. Massive questionnaires are pervasive in marketing practice. As an alternative to the heuristic methods that are currently used to split questionnaires, this study develops a methodology to design the split questionnaire in a way that minimizes information loss. Using estimates from a first wave or pilot study, the authors apply the modified Fedorov algorithm using the Kullback–Leibler distance as a design criterion to find the optimal splits. Their design criterion is based on a general mixed data model that accommodates continuous, rank-ordered, and discrete measurement scales. The optimal construction of the split questionnaire design is easy and fast. The authors use Markov chain Monte Carlo procedures to impute missing values that result from the design. They generate split questionnaire designs by selecting either entire blocks of questions (between-block design) or sets of questions in each block (within-block design). They compare the efficiency of split questionnaires generated with the proposed method with multiple matrix sampling designs, incomplete block designs, and a heuristic procedure, using synthetic and empirical Web survey data. The authors illustrate in a field study that as a result of reduced respondent burden, the quality of data using split questionnaire designs improves.