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Evaluating patient satisfaction through latent class factor analysis.

Giulia Cavrini1, Giuliano Galimberti, Gabriele Soffritti

  • 1Department of Statistics, University of Bologna, via delle Belle Arti, 41-40126 Bologna, Italy. giulia.cavrini@unibo.it

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This study explains latent variable analysis for satisfaction research using ordinal survey data. It details methods for analyzing patient hospital care satisfaction, including multi-level data considerations.

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Area of Science:

  • Health Services Research
  • Quantitative Psychology
  • Geographic Analysis

Background:

  • Patient satisfaction is a key indicator of healthcare quality.
  • Analyzing subjective opinions, especially on ordinal scales, presents methodological challenges.
  • Latent variable analysis offers robust methods for understanding complex constructs like satisfaction.

Purpose of the Study:

  • To introduce latent variable analysis methods for studying satisfaction.
  • To illustrate practical application using patient satisfaction data from Italian hospitals.
  • To describe techniques suitable for ordinal and multi-level data structures.

Main Methods:

  • Explanation of latent variable analysis theory.
  • Step-by-step guidance on applying methods to ordinal data (e.g., Likert scales).
  • Description of methods for handling two-category and multi-category ordinal opinions, including clustered/multi-level data.

Main Results:

  • Demonstration of latent variable methods on a real-world patient satisfaction dataset.
  • Analysis of patient opinions on various aspects of hospital care.
  • Application to a dataset with patients nested within hospital wards.

Conclusions:

  • Latent variable analysis is a powerful tool for quantifying patient satisfaction.
  • The presented methods are applicable to ordinal and multi-level health outcome data.
  • This approach enhances the understanding of healthcare quality through patient-reported experiences.