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Multi-Group Multidimensional Classification Accuracy Analysis (MMCAA): A General Framework for Evaluating the

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Measurement invariance (MI) is crucial for valid score comparisons. This study introduces a multi-group framework to assess the impact of noninvariance on decision accuracy and fairness across multiple subpopulations, offering a more precise analysis than two-group methods.

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Sociology

Background:

  • Measurement invariance (MI) is essential for valid score comparisons across groups.
  • Existing methods often simplify multi-group populations into two categories, risking inaccurate inferences.
  • High-stakes decisions based on test scores necessitate understanding the practical impact of MI violations.

Purpose of the Study:

  • To introduce a general framework for assessing the practical impact of measurement noninvariance.
  • To evaluate the accuracy and fairness of test-based decisions across multiple subpopulations.
  • To demonstrate the advantages of a multi-group approach over dichotomized analyses.

Main Methods:

  • Development of the multi-group multidimensional classification accuracy analysis (MMCAA) framework.
  • Application of MMCAA to a depression scale across four ethnic groups using national data.
  • Utilizing the R package 'unbiasr' for automated analysis.

Main Results:

  • Collapsing grouping variables leads to significant loss of information and precision.
  • The MMCAA framework provides a more accurate assessment of decision accuracy and fairness in multi-group settings.
  • Illustrative example highlights the practical impact of MI on classification accuracy.

Conclusions:

  • The MMCAA framework offers a robust method for investigating measurement noninvariance in diverse populations.
  • Accurate assessment of test fairness requires considering all relevant subpopulations.
  • The 'unbiasr' R package facilitates the application of this advanced framework.