<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Department of Operations, Business Analytics, and Information Systems Cincinnati

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

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

Pubblicato online: 10 dic 2023

Pagine: 459 - 487

Ricevuto: 01 set 2021

Accettato: 01 mar 2023

DOI: https://doi.org/10.2478/jos-2023-0022

Parole chiave
Block weighted least squares estimation, block-wise missing data, nonlinear cost function, split questionnaire design, large-scale survey

© 2023 Yang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Pubblicato online: 10 dic 2023

Pagine: 459 - 487

Ricevuto: 01 set 2021

Accettato: 01 mar 2023

DOI: https://doi.org/10.2478/jos-2023-0022

Parole chiaveBlock weighted least squares estimation, block-wise missing data, nonlinear cost function, split questionnaire design, large-scale survey

© 2023 Yang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Block weighted least squares estimation, block-wise missing data, nonlinear cost function, split questionnaire design, large-scale survey