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A guide for applying principal-components analysis and confirmatory factor analysis to quantitative
J E Arruda1, M D Weiler, D Valentino
1Department of Psychiatry and Human Behavior, Brown University School of Medicine, Providence, RI, USA. arruda_je@mercer.edu
Summary
Principal-components analysis (PCA) in quantitative electroencephalogram (qEEG) research can yield unstable results due to small sample sizes. This study validates a seven-component qEEG solution using confirmatory factor analysis (CFA), demonstrating its stability and reliability.
Area of Science:
- Neuroscience
- Psychometrics
- Signal Processing
Background:
- Quantitative electroencephalogram (qEEG) research often employs principal-components analysis (PCA) for dimensionality reduction.
- Small sample sizes in previous qEEG PCA studies have led to unstable component solutions.
- Independent validation of qEEG component solutions using confirmatory factor analysis (CFA) is lacking.
Purpose of the Study:
- To illustrate the application of PCA and CFA to qEEG data.
- To establish decision rules for applying PCA and CFA in qEEG research.
- To validate a qEEG component solution using an independent sample.
Main Methods:
- PCA was performed on qEEG measures from 102 healthy individuals during an auditory continuous performance task.
- The resulting component solution was validated using CFA in an independent sample of 106 healthy individuals.
- Internal consistency and test-retest reliability of the validated component solution were assessed.
Main Results:
- A stable, oblique, seven-component solution for qEEG measures was confirmed via CFA.
- The seven-component solution demonstrated high internal consistency and test-retest reliability.
- The findings support the use of qEEG data as a stable and valid neurophysiological measure.
Conclusions:
- The validated seven-component qEEG solution offers a reliable method for neurophysiological assessment.
- These qEEG measures may aid in differentiating between clinical and control populations.
- The study highlights the importance of rigorous validation methods like CFA in qEEG research.