The Impact of Outliers on Cronbach's Coefficient Alpha Estimate of Reliability: Visual Analogue Scales
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Bibliographic record
Abstract
The impact of outliers on Cronbach's coefficient α has not been documented in the psychometric or statistical literature. This is an important gap because coefficient α is the most widely used measurement statistic in all of the social, educational, and health sciences. The impact of outliers on coefficient α is investigated for varying values of population reliability and sample sizes for visual analogue scales. Results show that coefficient α is not affected by symmetric outlier contamination, whereas asymmetric outliers artificially inflate the estimates of coefficient α. Coefficient α estimates are upwardly biased and more variable sample to sample, with increasing asymmetry and proportion of outlier contamination in the population. However, these effects of outliers on the bias and sample variability of coefficient α estimates are reduced for increasing population reliability. The results are discussed in the context of providing guidance for computing or interpreting coefficient α for visual analogue scales.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it