Technical Note: Using Latent Class Analysis versus K-means or Hierarchical Clustering to Understand Museum Visitors

This paper discusses the benefits of using Latent Class Analysis (LCA) versus K‐means Cluster Analysis or Hierarchical Clustering as a way to understand differences among visitors in museums, and is part of a larger research program directed toward improving the museum‐visit experience. For our comp...

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Bibliographic Details
Published in:Curator (New York, N.Y.) N.Y.), 2014-01, Vol.57 (1), p.45-59
Main Authors: Schreiber, James B., Pekarik, Andrew J.
Format: Article
Language:English
Subjects:
Cluster analysis
Museums
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Summary:This paper discusses the benefits of using Latent Class Analysis (LCA) versus K‐means Cluster Analysis or Hierarchical Clustering as a way to understand differences among visitors in museums, and is part of a larger research program directed toward improving the museum‐visit experience. For our comparison of LCA and K‐means Clustering, we use data collected from 190 visitors leaving the exhibition Against All Odds; Rescue at the Chilean Mine in the National Museum of Natural History in January 2012. For the comparison of LCA and Hierarchical Clustering, we use data from 312 visitors leaving the exhibition Elvis at 21 in the National Portrait Gallery in January 2011.
ISSN:0011-3069
2151-6952
DOI:10.1111/cura.12050