Scale Reliability
Last updated: Apr 13, 2026, 1:39 PM EDT
Scale reliability is assessed using Cronbach’s alpha (α), the most widely used measure of internal consistency. A coefficient of α ≥ 0.70 is generally considered acceptable for research purposes (Nunnally & Bernstein, 1994), while values above 0.80 indicate good to excellent reliability.
All three TABS constructs demonstrate excellent internal consistency, with every Cronbach’s alpha exceeding 0.84 across all five sample definitions. This indicates that the survey items within each construct are measuring the same underlying factor reliably, regardless of which inclusion criteria are applied.
Cronbach’s Alpha by Construct and Sample
| Construct | Conservative Clean N=87 | Flexible Clean N=134 | Prolific Accepted N=243 | All V2 Finished N=391 | All V2 N=462 |
|---|---|---|---|---|---|
| Barriers | 0.8545 | 0.8746 | 0.8778 | 0.8993 | 0.9006 |
| Readiness | 0.8670 | 0.9132 | 0.9171 | 0.9312 | 0.9312 |
| Maturity | 0.8327 | 0.8805 | 0.8866 | 0.8910 | 0.8910 |
Interpretation
Several key observations emerge from the reliability analysis:
- All alphas exceed 0.84, well above the commonly cited 0.70 threshold, indicating excellent internal consistency across all constructs and samples.
- Reliability coefficients are stable across sample definitions, meaning the scales perform consistently whether computed on the strictest clean sample or the full dataset.
- Alpha values tend to increase slightly with larger sample sizes, which is expected behavior and does not indicate measurement problems.
- The Readiness construct shows the highest alphas (up to 0.9312), while Maturity shows the lowest (still above 0.8327), which may reflect the smaller number of items in the Maturity scale (9 vs. 18–19).
The consistency of these results across five sample definitions demonstrates that the TABS scales are reliable instruments regardless of the inclusion criteria applied. This robustness is critical for supporting any downstream inferential analyses.
References
- Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
- Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Related
- Descriptive Statistics — means, SDs, and correlations for each construct
- Sensitivity Analysis — all metrics across five sample definitions
- Data Quality Pipeline — how the samples are defined and validated
- ← Back to Results Overview