<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This article considers a modular approach to the design of integrated social surveys. The approach consists of grouping variables into ‘modules’, each of which is then allocated to one or more ‘instruments’. Each instrument is then administered to a random sample of population units, and each sample unit responds to all modules of the instrument. This approach offers a way of designing a system of integrated social surveys that balances the need to limit the cost and the need to obtain sufficient information. The allocation of the modules to instruments draws on the methodology of split questionnaire designs. The composition of the instruments, that is, how the modules are allocated to instruments, and the corresponding sample sizes are obtained as a solution to an optimisation problem. This optimisation involves minimisation of respondent burden and data collection cost, while respecting certain design constraints usually encountered in practice. These constraints may include, for example, the level of precision required and dependencies between the variables. We propose using a random search algorithm to find approximate optimal solutions to this problem. The algorithm is proved to fulfil conditions that ensure convergence to the global optimum and can also produce an efficient design for a split questionnaire.</p></abstract>

This article considers a modular approach to the design of integrated social surveys. The approach consists of grouping variables into ‘modules’, each of which is then allocated to one or more ‘instruments’. Each instrument is then administered to a random sample of population units, and each sample unit responds to all modules of the instrument. This approach offers a way of designing a system of integrated social surveys that balances the need to limit the cost and the need to obtain sufficient information. The allocation of the modules to instruments draws on the methodology of split questionnaire designs. The composition of the instruments, that is, how the modules are allocated to instruments, and the corresponding sample sizes are obtained as a solution to an optimisation problem. This optimisation involves minimisation of respondent burden and data collection cost, while respecting certain design constraints usually encountered in practice. These constraints may include, for example, the level of precision required and dependencies between the variables. We propose using a random search algorithm to find approximate optimal solutions to this problem. The algorithm is proved to fulfil conditions that ensure convergence to the global optimum and can also produce an efficient design for a split questionnaire.

On a Modular Approach to the Design of Integrated Social Surveys

Athens University of Economics and Business, Patission str. 76, 10434 Athens, Greece.

University of Southampton, Southampton, SO17 1BJ, United Kingdom and Statistics Norway.

European Commission (Eurostat), L-2920 Luxembourg.

Agilis SA, Acadimias 96-100, 10677 Athens, Greece

Agilis SA, Acadimias 96-100, 10677 Athens, Greece.

Journal of Official Statistics

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

{"article-title":"On a Modular Approach to the Design of Integrated Social Surveys"}

This article considers a modular approach to the design of integrated social surveys. The approach consists of grouping variables into ‘modules’, each of which...

On a Modular Approach to the Design of Integrated Social Surveys

Published Online: May 28, 2016

Page range: 259 - 286

Received: Feb 01, 2015

Accepted: Oct 01, 2015

DOI: https://doi.org/10.1515/jos-2016-0013

Keywords
Efficient design, respondent burden, sample allocation, simulated annealing, split questionnaire

© 2016 Evangelos Ioannidis et al., published by De Gruyter Open

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

On a Modular Approach to the Design of Integrated Social Surveys

Published Online: May 28, 2016

Page range: 259 - 286

Received: Feb 01, 2015

Accepted: Oct 01, 2015

DOI: https://doi.org/10.1515/jos-2016-0013

KeywordsEfficient design, respondent burden, sample allocation, simulated annealing, split questionnaire

© 2016 Evangelos Ioannidis et al., published by De Gruyter Open

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

Keywords
Efficient design, respondent burden, sample allocation, simulated annealing, split questionnaire