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Unfolding Incomplete Data: Guidelines for Unfolding Row-Conditional Rank Order Data with Random Missings

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Abstract

Unfolding creates configurations from preference information. In this paper, it is argued that not all preference information needs to be collected and that good solutions are still obtained, even when more than half of the data is missing. Simulation studies are conducted to compare missing data treatments, sources of missing data, and designs for the specification of missing data. Guidelines are provided and used in actual practice.

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Authors and Affiliations

  1. Department of Psychology, Leiden University, P.O. Box 9555, 2300 RB, Leiden, the Netherlands

    Frank M. T. A. Busing & Mark de Rooij

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  1. Frank M. T. A. Busing
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  2. Mark de Rooij
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Correspondence to Frank M. T. A. Busing.

Additional information

This research was conducted while the second author was sponsored by the Netherlands Organization for Scientific Research (NWO), Innovational Grant, no. 452-06-002. The authors would like to thank the DIOS for their invaluable computer support and Willem Heiser and Graham Cleaver and the three anonymous referees for their helpful comments and suggestions to improve the quality of this work.

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Busing, F.M.T.A., de Rooij, M. Unfolding Incomplete Data: Guidelines for Unfolding Row-Conditional Rank Order Data with Random Missings. J Classif 26, 329–360 (2009). https://doi.org/10.1007/s00357-009-9039-7

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