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Model-based clustering using S-PLUS.

Morven Leese1, Sabine Landau

  • 1Department of Health Services Research, Institute of Psychiatry, King's College, London, UK. m.leese@iop.kcl.ac.uk

International Journal of Methods in Psychiatric Research
|October 6, 2006
PubMed
Summary
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Model-based cluster analysis is effective for continuous questionnaire data. However, for ordinal data, its benefits, especially with correlations, are limited, with simpler methods performing comparably.

Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Data Analysis

Background:

  • Cluster analysis identifies subgroups in psychology and psychiatry.
  • Many statistical packages use heuristic methods lacking statistical models.
  • Standard clustering often ignores variable correlations.

Purpose of the Study:

  • Evaluate model-based clustering for correlated continuous and ordinal questionnaire data.
  • Assess commercial software (S-PLUS) for clustering correlated data.
  • Compare performance on simulated and real-world data.

Main Methods:

  • Utilized simulated datasets with multivariate normal subpopulations.
  • Mapped simulated continuous data onto an ordinal scale.
  • Applied model-based clustering methods to assess performance.

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Main Results:

  • Model-based methods are highly effective for continuous data.
  • Benefits for ordinal data, particularly with correlations, were not significant.
  • Simpler methods like Ward's method showed comparable effectiveness for ordinal data.

Conclusions:

  • Model-based clustering is recommended for continuous questionnaire data.
  • For ordinal questionnaire data, simpler methods may suffice.
  • The advantage of model-based methods diminishes with ordinal data scales.