Stability and Similarity of Clusters under Reduced Response Data

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Stability and Similarity of Clusters under Reduced Response Data

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dc.contributor.author Litong-Palima, Marisciel
dc.contributor.author Albers, Kristoffer Jon
dc.contributor.author Glückstad, Fumiko Kano
dc.date.accessioned 2018-08-08T09:13:24Z
dc.date.available 2018-08-08T09:13:24Z
dc.date.issued 2018-08-08
dc.identifier.uri http://hdl.handle.net/10398/9652
dc.description.abstract This study presents a validated recommendation on how to shorten the surveys while still obtaining segmentation-based insights that are consistent with the analysis of the full length version of the same survey. We use latent class analysis to cluster respondents based on their responses to a survey on human values. We first define the clustering performance based on stability and similarity measures for ten random subsamples relative to the complete set. We find foremost that the use of true binary scale can potentially reduce survey completion time while still providing sufficient response information to derive clusters with characteristics that resemble those obtained with the full Likert scale version. The main motivation for this study is to provide a baseline performance of a standard clustering tool for cases when it is preferable or necessary to limit survey scope, in consideration of issues like respondent fatigue or resource constraints. en_US
dc.format.extent 4 en_US
dc.language eng en_US
dc.publisher Japanese Society for Artificial Intelligence en_US
dc.title Stability and Similarity of Clusters under Reduced Response Data en_US
dc.type cp en_US
dc.accessionstatus modt18aug08 soma en_US
dc.contributor.corporation Copenhagen Business School. CBS en_US
dc.contributor.department Department of Management, Society and Communication en_US
dc.contributor.departmentshort MSC en_US
dc.contributor.departmentuk Department of Management, Society and Communication en_US
dc.contributor.departmentukshort MSC en_US
dc.description.notes Paper presented at the 32nd Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2018). June 5-8 2018, Kagoshima, Japan en_US
dc.publisher.city Tokyo en_US
dc.publisher.year 2018 en_US


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