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.
|Number of pages||4|
|Publication status||Published - 2018|
|Event||32nd Annual Conference of the Japanese Society for Artificial Intelligence - Kagoshima, Japan|
Duration: 5 Jun 2018 → 8 Jun 2018
|Conference||32nd Annual Conference of the Japanese Society for Artificial Intelligence|
|Period||05/06/2018 → 08/06/2018|
Litong-Palima, M., Albers, K. J., & Kano Glückstad, F. (2018). Stability and Similarity of Clusters under Reduced Response Data. Paper presented at 32nd Annual Conference of the Japanese Society for Artificial Intelligence, Kagoshima, Japan.