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Clusters that are not there: An R tutorial and a Shiny app to quantify a priori inferential risks when using

Enrico Toffalini1, Filippo Gambarota2, Ambra Perugini2

  • 1Department of General Psychology, University of Padova, Padova, Italy.

International Journal of Psychology : Journal International De Psychologie
|September 20, 2024
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Clustering methods in social science research may incorrectly identify non-existent groups. This study highlights common risks in psychological research and offers tools to assess these inferential risks before analysis.

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Area of Science:

  • Social Science Research
  • Psychological Research
  • Data Analysis

Background:

  • Clustering methods are widely used in social sciences to identify distinct individual types and uncover hidden heterogeneity.
  • However, the validity of clustering results depends on specific assumptions and conditions.
  • Common risks include failing to detect existing clusters or identifying clusters that are not truly present.

Purpose of the Study:

  • To investigate the risk of false positive cluster detection in psychological research using common clustering methods.
  • To identify specific data conditions that increase the likelihood of detecting non-existent clusters.
  • To provide practical tools for researchers to assess inferential risks associated with clustering methods.

Main Methods:

  • The study employed simple data simulations to mimic conditions frequently encountered in applied psychological research.
  • Simulated data varied in sample size, number of clusters, indicator correlation, and skewness.
  • An R tutorial and a Shiny app were developed to aid researchers in quantifying a priori inferential risks.

Main Results:

  • Simulations indicated that commonly used clustering methods are prone to detecting clusters that do not exist under typical psychological research conditions.
  • Violations of statistical assumptions, often overlooked in psychology, contribute to this over-detection.
  • Conditions that inflate the number of detected clusters are prevalent in applied psychological research.

Conclusions:

  • Researchers using clustering methods in psychology face a high risk of identifying spurious clusters.
  • A preliminary risk assessment using the provided tools is crucial before applying clustering techniques.
  • Implementing these checks can improve the reliability and validity of cluster analysis in social science research.