Cronbach's Alpha Calculator

Cronbach's Alpha (α) is a coefficient that measures the internal consistency reliability of a multi-item scale or questionnaire. It reflects how closely related a set of items are as a group — essentially, whether all items are measuring the same underlying construct. Values range from 0 to 1, where higher values indicate greater internal consistency.

Using the George & Mallery (2003) guidelines: α > .9 is Excellent, α > .8 is Good, α > .7 is Acceptable, α > .6 is Questionable, α > .5 is Poor, and α < .5 is Unacceptable. Note that values much higher than .9 can also be problematic, as they may indicate item redundancy — items are too similar and not contributing distinct information.

Enter one respondent (participant) per row, with each column representing one item (question). Separate values with spaces, tabs, or commas. All rows must have the same number of items, and all values should be numeric (e.g., Likert scale scores like 1–5 or 1–7). Rows containing non-numeric or missing values are automatically excluded using listwise deletion.

The standard Cronbach (1951) formula is: α = (k / (k−1)) × (1 − Σσᵢ² / σₜ²), where k is the number of items, Σσᵢ² is the sum of item variances, and σₜ² is the variance of the total scores. This calculator uses population variance (dividing by n) consistently across items and total scores.

You need at least 2 items and at least 2 respondents to compute Cronbach's Alpha. However, for reliable estimates, a minimum of 5–10 items and 30+ respondents is generally recommended. Very small samples can produce unstable alpha values that don't generalize well.

The corrected item-total correlation is the Pearson correlation between a single item's scores and the sum of all other items (excluding that item). It measures how well each individual item relates to the overall scale. Items with very low corrected item-total correlations (below ~0.3) may be candidates for removal to improve scale reliability.

Raw Cronbach's Alpha is calculated from the actual item variances and is appropriate when all items use the same response scale. Standardized alpha (α = (k × r̄) / (1 + (k−1) × r̄)) is based on the average inter-item correlation and is more appropriate when items are measured on different scales. This calculator uses the raw formula by default.

Cronbach's Alpha assumes tau-equivalence (equal true-score variances across items). For ordinal data, polychoric alpha may be more appropriate. For congeneric measures (items with different relationships to the latent trait), McDonald's Omega (ω) is considered a more accurate reliability estimate. Alpha also tends to increase simply as you add more items, so scale length should be considered.