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Summary Intervals for Model-Based Classification Accuracy and Consistency Indices
1The University of North Carolina at Chapel Hill, USA.
Educational and Psychological Measurement
|March 3, 2023
Summary
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.
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.
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