Cronbach's Alpha Calculator
Calculate internal consistency reliability for multi-item scales. Paste your data to get Cronbach's alpha with interpretation and item-level analysis.
Input Data
Cronbach's Alpha
Enter your data to calculate reliability
Methodology
Raw Alpha (Cronbach, 1951):
α = (k/(k-1)) × (1 - Σσᵢ²/σₜ²)Standardized Alpha:
α = (k × r̄) / (1 + (k-1) × r̄)Interpretation (George & Mallery, 2003):
Missing data: Listwise deletion (rows with any missing/non-numeric values are excluded).
95% CI: Bootstrap (1,000 resamples, percentile method).
Item-total r: Corrected (item excluded from total).
Assumptions: Tau-equivalence (equal true-score variances). For ordinal data or congeneric measures, consider polychoric alpha or McDonald's ω.
Built by Lensym — focused on valid, reliable survey research.
Understanding Cronbach's Alpha
Cronbach's alpha (α) measures the internal consistency of a multi-item scale. It answers the question: "Do the items in this scale measure the same underlying construct?"
When to Use This Calculator
- Validating a new survey instrument or questionnaire
- Checking reliability of established scales in your sample
- Identifying problematic items that reduce scale reliability
- Reporting reliability statistics for academic publications
Key Assumptions
- Unidimensionality — Items should measure a single construct. Use factor analysis first if unsure.
- Tau-equivalence — Items should have equal true score variances (a strong assumption rarely fully met).
- Continuous or interval-level data — Works best with Likert scales of 5+ points.
Limitations
- Inflated by more items — Adding redundant items increases alpha artificially. Very high alpha (> 0.95) may indicate redundancy.
- Not a measure of validity — High reliability doesn't mean you're measuring what you intend to measure.
- Sample-dependent — Alpha can vary across populations. Always report for your specific sample.
For a deeper discussion of scale reliability, see our guide on survey validity and reliability.