Cronbach Alpha Calculator
Inspect the internal consistency of a numeric item matrix with explicit item selection, reverse coding and missing-row handling. Compare raw and standardized alpha, then examine covariance, correlations and item diagnostics on the same complete observations.
Up to 10,000 rows and 100 selected items. Blank, NA and N/A mean missing. Other selected text must be corrected. No upload or saved questionnaire data.
3 selected of 3 columns · 3 data rows
Uses sample covariance and the variance of each complete-case total score.
A separate correlation-based calculation. Different item scales can make these values differ.
Inspect covariance, corrected item-total correlation and alpha if each item were deleted. No threshold certifies a scale or recommends removing an item.
Every variance uses n − 1. Corrected item-total correlation excludes that item from the remaining-item score. Undefined values include their specific reason.
Original example shown. Calculate to analyze the current selected items.
- Complete rows
- 3 / 3
- Selected items
- 3
- Excluded rows
- 0
- Total-score variance
- 9
Item diagnostics
Means and variances use the selected reverse coding. Alpha if deleted is raw alpha on the same complete rows; it does not recover excluded observations.
| Original item | Mean | Sample variance | Corrected item-total r | Raw alpha if deleted |
|---|---|---|---|---|
| 1. Item A | 2 | 1 | 1 | 1 |
| 2. Item B | 2 | 1 | 1 | 1 |
| 3. Item C | 2 | 1 | 1 | 1 |
Item matrix
Scroll to inspect every column. Numeric display uses 8 significant digits; the CSV retains unrounded values.
| Item | 1. Item A | 2. Item B | 3. Item C |
|---|---|---|---|
| 1. Item A | 1 | 1 | 1 |
| 2. Item B | 1 | 1 | 1 |
| 3. Item C | 1 | 1 | 1 |
Row-exclusion report
No data rows were excluded.
Worked examples
- Raw α = 1, standardized α = 1.
- Total-score variance is zero. Raw alpha is undefined.
- 1 → 5, 3 → 3, 5 → 1.
Accuracy. Internal consistency does not establish unidimensionality, validity or an individual ability/health judgment. Negative alpha remains negative. No automated scale certification or recommended deletions; standardized alpha uses correlations and is labeled separately.
Common questions
- How do I prepare a CSV item matrix?
- Put one observation on each data row and one item in each column. Paste comma-separated values or choose a local UTF-8 CSV, state whether the first row contains column names, and read the columns. Select 2 to 100 numeric items and leave identifiers unselected. Up to 10,000 data rows are supported. Quoted commas and escaped quotes are accepted; malformed rows are reported instead of partially imported.
- How is raw Cronbach alpha calculated?
- For k selected items, raw alpha is k divided by k minus 1, multiplied by 1 minus the sum of item sample variances divided by total-score sample variance. Each sample variance uses n minus 1. Three identical varying columns containing 1, 2 and 3 give raw alpha 1. Constant total scores make raw alpha undefined.
- How does standardized alpha differ?
- Standardized alpha uses the item correlation matrix, equivalent to giving every item unit sample variance before forming the score. Raw alpha uses the original covariance scale. Different item variances can make the results differ. A constant item makes its correlations and standardized alpha undefined, while raw alpha may still be defined.
- How do reverse coding and missing observations work?
- Select Reverse beside each item to reverse code and enter its declared minimum and maximum. The transformed value is minimum plus maximum minus the original value: on a 1 to 5 scale, 1 becomes 5, 3 stays 3 and 5 becomes 1. Blank, NA and N/A selected cells are missing. The calculation requires explicit consent before removing their entire rows. It never imputes values; malformed selected numbers must be corrected even on a row with another missing item.
- What do corrected item-total correlation and alpha if deleted mean?
- Corrected item-total correlation compares an item with the sum of the other selected items. Alpha if deleted is raw alpha for the remaining items, using the same complete rows as the full analysis. It is undefined when fewer than two items remain or the remaining total has zero variance. These are descriptive diagnostics and do not recommend deleting an item.
- Can alpha be negative or undefined?
- Yes. Negative estimates remain negative rather than being clipped to zero. For paired rows (1, 4), (2, 2), (3, 1), raw alpha is -18. Rows (1, 3), (2, 2), (3, 1) have constant total scores, so raw alpha is undefined. Undefined diagnostics show the specific variance or item-count reason. Extreme numeric scales that exceed representable covariance precision require rescaling.
- What can I copy or export?
- After calculating, copy a report with both alpha results, item diagnostics, reverse coding and every excluded row. The diagnostics CSV also includes the complete covariance and correlation matrices, sample means and variances. Displayed numbers use 8 significant digits; CSV numeric fields retain their unrounded floating-point values. Imported labels receive spreadsheet formula protection. No individual response rows are included in these exports.
- Is my questionnaire data uploaded or saved?
- No. CSV parsing and analysis run locally in a cancellable background worker. Inputs and results are kept in memory and are not stored in browser storage or sent to analytics. A 50 MB file or 50-million-character pasted-text budget and a two-million-cell structural budget protect browser memory. If a recording exceeds a budget, export just the needed columns or use a smaller matrix.
Internal consistency does not establish unidimensionality, validity or an individual ability/health judgment. Negative alpha remains negative. No automated scale certification or recommended deletions; standardized alpha uses correlations and is labeled separately.