Block Weighted Least Squares Estimation for Nonlinear Cost-based Split Questionnaire Design

Li Yang (), Qi Le (), Qin Yichen (), Lin Cunjie () and Yang Yuhong ()
Additional contact information
Li Yang: Renmin University of China, Center for Applied Statistics and School of Statistics, att: Cunjie Lin, 59 Zhongguancun St, Beijing 100872, China.
Qi Le: Renmin University of China, Center for Applied Statistics and School of Statistics, att: Cunjie Lin, 59 Zhongguancun St, Beijing 100872, China.
Qin Yichen: University of Cincinnati, Department of Operations, Business Analytics, and Information Systems Cincinnati, Ohio, U.S.A.
Lin Cunjie: Renmin University of China, Center for Applied Statistics and School of Statistics, att: Cunjie Lin, 59 Zhongguancun St, Beijing 100872, China.
Yang Yuhong: University of Minnesota, School of Statistics Minneapolis, U.S.A.

Journal of Official Statistics, 2023, vol. 39, issue 4, 459-487

Abstract: In this study, we advocate a two-stage framework to deal with the issues encountered in surveys with long questionnaires. In Stage I, we propose a split questionnaire design (SQD) developed by minimizing a quadratic cost function while achieving reliability constraints on estimates of means, which effectively reduces the survey cost, alleviates the burden on the respondents, and potentially improves data quality. In Stage II, we develop a block weighted least squares (BWLS) estimator of linear regression coefficients that can be used with data obtained from the SQD obtained in Stage I. Numerical studies comparing existing methods strongly favor the proposed estimator in terms of prediction and estimation accuracy. Using the European Social Survey (ESS) data, we demonstrate that the proposed SQD can substantially reduce the survey cost and the number of questions answered by each respondent, and the proposed estimator is much more interpretable and efficient than present alternatives for the SQD data.

Keywords: Block weighted least squares estimation; block-wise missing data; nonlinear cost function; split questionnaire design; large-scale survey (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/jos-2023-0022 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:39:y:2023:i:4:p:459-487:n:2

DOI: 10.2478/jos-2023-0022

Access Statistics for this article

Journal of Official Statistics is currently edited by Annica Isaksson and Ingegerd Jansson

More articles in Journal of Official Statistics from Sciendo
Bibliographic data for series maintained by Peter Golla ().