The analysis of bridging constructs with hierarchical clustering methods: An application to identity

Colleen M. Farrelly, Seth J Schwartz, Anna Lisa Amodeo, Daniel J Feaster, Douglas L. Steinley, Alan Meca, Simona Picariello

Research output: Contribution to journalArticle

9 Scopus citations


When analyzing psychometric surveys, some design and sample size limitations challenge existing approaches. Hierarchical clustering, with its graphics (heat maps, dendrograms, means plots), provides a nonparametric method for analyzing factorially-designed survey data, and small samples data. In the present study, we demonstrated the advantages of using hierarchical clustering (HC) for the analysis of non-higher-order measures, comparing the results of HC against those of exploratory factor analysis. As a factorially-designed survey, we used the Identity Labels and Life Contexts Questionnaire (ILLCQ), a novel measure to assess identity as a bridging construct for the intersection of identity domains and life contexts. Results suggest that, when used to validate factorially-designed measures, HC and its graphics are more stable and consistent compared to EFA.

Original languageEnglish (US)
Pages (from-to)93-106
Number of pages14
JournalJournal of Research in Personality
StatePublished - Oct 1 2017



  • Bridging constructs
  • Cluster analysis
  • Identity
  • Measurement

ASJC Scopus subject areas

  • Social Psychology
  • Psychology(all)

Cite this