The analysis of bridging constructs with hierarchical clustering methods: An application to identity |
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| Institution: | 1. Independent Researcher, 3200 Port Royale Dr. North, Fort Lauderdale, FL 33308, USA;2. Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Miami, FL 33136, USA;3. Department of Humanistic Studies, University of Naples Federico II, Via Porta di Massa, 1, 80138 Naples, Italy;4. Department of Psychological Sciences, 210 McAlester Hall, University of Missouri Columbia, MO 65211, USA;5. Department of Psychology, Old Dominion University, 250 Mills Godwin Life Sciences Bldg #134A, Norfolk, VA 23529, USA;6. Center of Atheneum SInAPSi, University of Naples Federico II, Via Giulio Cesare Cortese, 80138 Naples, Italy;1. The John Paul II Catholic University of Lublin, Lublin, Poland;2. National Aviation University, Kyiv, Ukraine;3. National University Odessa Law Academy, Odessa, Ukraine;4. Ukrainian Catholic University, Lviv, Ukraine;5. O.M. Beketov National University of Urban Economy in Kharkiv, Kharkiv, Ukraine;6. National Mining University, Dnipropetrovsk, Ukraine;7. University of Zielona Gora, Zielona Gora, Poland;1. University of Western Ontario, 1151 Richmond Street, London, Ontario, Canada;2. Curtin School of Business, Curtin University, Perth, Western Australia, Australia;1. Department of Psychology, University of Sheffield, Sheffield, UK;2. Department of Child and Youth Studies, Brock University, St. Catharines, ON, Canada;3. Research Institute on Addictions, University at Buffalo, Buffalo, NY, USA |
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| Abstract: | 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. |
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| Keywords: | Cluster analysis Bridging constructs Identity Measurement |
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