Split Questionnaire Design for Massive Surveys
Published 10/1/2008
Author: Feray Adigüzel and Michel Wedel
View this content
Executive Summary
Companies are conducting more and longer surveys than ever before. However, massive questionnaires take more time; induce the use of undesired response styles; increase respondent fatigue and boredom; and result in more nonresponse, item nonresponse, and early break-off. Therefore, in practice, market research agencies frequently resort to splitting questionnaires into parts (or splits) and administering each part to a randomly selected group of respondents. As an alternative to the heuristic methods that are currently used to split these 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 find the optimal splits. Their design criterion accommodates continuous, rank-ordered, and discrete measurement scales. The optimal construction of the split questionnaire design is easy and fast. The authors use Bayesian 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). The authors compare the efficiency of split questionnaires generated with the proposed method with several possible alternatives (multiple matrix sampling designs, incomplete block designs, and a heuristic procedure) on synthetic and empirical Web survey data. Using a field study, the authors illustrate that because of reduced respondent burden, the quality of data using split questionnaire designs improves.
Biography
Feray Adıgüzel is Assistant Professor of Marketing at VU University Amsterdam, the Netherlands. She has an undergraduate and a master degree in Statistics from Middle East Technical University (METU), Turkey, and a PhD in Marketing from the University of Groningen, the Netherlands. Her research interests are in the area of marketing research methodology and Bayesian methods.
Michel Wedel earned his MSc in Biomathematics from the University of Leiden (1981), his MSc in Statistics from the Netherlands Statistical Society (1986), and his PhD in Marketing from Wageningen University (1990). He is the Pepsico Professor of Consumer Science in the Robert H. Smith School of Business at the University of Maryland and is Honorary Professor of Marketing at the University of Groningen. His main research interests are in marketing research methodology and the application of statistical and econometric methods to marketing problems. His work has appeared in International Journal for Research in Marketing, Journal of Econometrics, Journal of Applied Econometrics, Journal of Classification, Journal of the American Statistical Association, Journal of Business and Economic Statistics, Journal of Consumer Research, Journal of Marketing Research, Journal of Marketing, Management Science, Marketing Science, and Psychometrika, among others. He serves on the editorial boards of Journal of Marketing and Journal of Classification, and he is area editor for Journal of Marketing Research and Marketing Science.
J Marketing Research, Volume 45, Number 5, October 2008
View Table of Contents.