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Evangelos Ioannidis, Takis Merkouris, Li-Chun Zhang, Martin Karlberg, Michalis Petrakos, Fernando Reis and Photis Stavropoulos

Open access

Martin Karlberg, Silvia Biffignandi, Piet J.H. Daas, Anders Holmberg, Beat Hulliger, Pascal Jacques, Risto Lehtonen, Ralf T. Münnich, Natalie Shlomo, Roxane Silberman and Ineke Stoop

Open access

Martin Karlberg, Silvia Biffignandi, Piet J.H. Daas, Loredana Di Consiglio, Anders Holmberg, Risto Lehtonen, Ralf T. Münnich, Boro Nikic, Marianne Paasi, Natalie Shlomo, Roxane Silberman and Ineke Stoop

Open access

Evangelos Ioannidis, Takis Merkouris, Li-Chun Zhang, Martin Karlberg, Michalis Petrakos, Fernando Reis and Photis Stavropoulos

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.