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Summary Intervals for Model-Based Classification Accuracy and Consistency Indices.

Oscar Gonzalez1

  • 1The University of North Carolina at Chapel Hill, USA.

Educational and Psychological Measurement
|March 3, 2023
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Summary
This summary is machine-generated.

This study introduces methods for estimating uncertainty in classification accuracy (CA) and classification consistency (CC) using bootstrap and Bayesian intervals. Results show bootstrap intervals offer appropriate coverage for decision-making accuracy.

Keywords:
classification accuracyclassification consistencyconfidence intervalsfactor modelscreening

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Measurement theory

Background:

  • Estimating classification accuracy (CA) and classification consistency (CC) is crucial for decision-making based on measurement scores.
  • Existing model-based estimates of CA and CC from linear factor models lack investigation into parameter uncertainty.
  • Quantifying uncertainty in CA and CC is essential for reliable interpretation and application of measurement results.

Purpose of the Study:

  • To demonstrate methods for estimating confidence intervals for classification accuracy (CA) and classification consistency (CC) indices.
  • To incorporate the sampling variability of linear factor model parameters into summary intervals for CA and CC.
  • To evaluate the performance of percentile bootstrap confidence intervals and Bayesian credible intervals for CA and CC.

Main Methods:

  • Estimation of percentile bootstrap confidence intervals for CA and CC indices.
  • Estimation of Bayesian credible intervals for CA and CC indices, exploring both diffused and empirical priors.
  • A simulation study to assess the coverage properties of the proposed interval estimation methods.
  • Application of the procedures to estimate CA and CC indices from a mindfulness measure.

Main Results:

  • Percentile bootstrap confidence intervals demonstrated appropriate coverage for CA and CC indices, with minor negative bias.
  • Bayesian credible intervals showed poor coverage with diffused priors but improved significantly with empirical, weakly informative priors.
  • The study successfully illustrated the estimation of CA and CC indices for a real-world measure.

Conclusions:

  • Percentile bootstrap confidence intervals provide a reliable method for assessing uncertainty in classification accuracy and consistency.
  • Empirical, weakly informative priors enhance the performance of Bayesian credible intervals for CA and CC estimation.
  • The proposed methods and provided R code facilitate the practical implementation of uncertainty estimation for CA and CC indices.