Cronbach's Alpha Reliability Analysis
Cronbach's Alpha measures the internal consistency of a multi-item scale — the degree to which items "hang together" and measure the same underlying construct. The most-reported reliability statistic in questionnaire-based research.
Step 1 — Provide your data
Matrix of respondents x items (numeric, e.g. Likert 1-5).
Supports Excel Import, Sample Data Generator, and Manual Entry.
Step 2 — Results
Overall reliability:
- Cronbach's Alpha with interpretation label
- Standardised Alpha (from inter-item correlations)
- Number of items and respondents
- Mean inter-item correlation
Item-level statistics (per item):
- Item mean and SD
- Corrected item-total correlation
- Squared multiple correlation
- Alpha-if-item-deleted (with change indicator)
Inter-item correlation matrix
A UniversalChatBot is available for discussion.
Statistical methods used
Cronbach's Alpha
alpha = (k/(k-1)) * (1 - sum(item_variances) / total_variance)
Standardised Alpha
alpha_std = (k * mean_r) / (1 + (k-1) * mean_r)
Interpretation
| Alpha | Label |
|---|---|
| >= 0.90 | Excellent |
| 0.80 - 0.90 | Good |
| 0.70 - 0.80 | Acceptable |
| 0.60 - 0.70 | Questionable |
| 0.50 - 0.60 | Poor |
| < 0.50 | Unacceptable |
Corrected item-total correlation — correlation between item and sum of all other items. < 0.30 = weak.
Alpha-if-item-deleted — alpha recomputed without each item. If alpha increases when item removed, that item may be hurting reliability.
Assumptions: tau-equivalence and unidimensionality. Very high alpha (> 0.95) may indicate item redundancy.