Cronbach's Alpha Calculator
Estimate Cronbach's alpha to assess the internal consistency of multi-item scales used in surveys, tests, and questionnaires. Commonly used in psychology, education, social sciences, and survey research.
Enter how many items are in your scale or subscale (e.g., 10 Likert items measuring the same construct).
This is the mean of all pairwise correlations among items. It is often between 0 and 1 for well-functioning scales.
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How This Cronbach's Alpha Calculator Works
Cronbach's alpha is a widely used index of internal consistency reliability for scales composed of multiple items. It is especially common in survey research, psychology, education, and other social science fields where researchers want to know whether a set of questions is measuring a single underlying construct. Examples include attitude scales, satisfaction surveys, personality inventories, and composite indices used in academic and applied research.
This calculator uses a simplified and transparent form of Cronbach's alpha based on two key inputs: the number of items in the scale (k) and the average inter-item correlation (r̄). Under standard assumptions, Cronbach's alpha can be expressed as α = (k × r̄) ÷ [1 + (k − 1) × r̄]. When the average inter-item correlation is higher, items tend to move together more strongly, and the estimated reliability increases. When k is larger, alpha also tends to increase, provided the items are reasonably consistent with one another.
In practice, researchers sometimes view values around 0.70 as a starting point for acceptable internal consistency in many applied contexts, while values of 0.80 or 0.90 may be preferred for higher stakes decisions. However, these guidelines are not strict rules. Very high alpha values can occasionally suggest that items are almost redundant, while lower values may be appropriate for broad or exploratory constructs. It is important to consider both statistical indices and substantive knowledge of the content area.
This Cronbach's Alpha Calculator is designed to give students, academic researchers, and survey designers a quick way to explore how the number of items and the average inter-item correlation jointly influence reliability. It works entirely in the browser and does not store any data. For more advanced applications, researchers may wish to compute alpha directly from raw item-level data, examine item-total correlations, or evaluate alternative reliability estimates such as omega. The current tool focuses on the classic alpha formula for clarity and ease of use and should be viewed as a complement to, not a replacement for, in-depth psychometric analysis.
Frequently Asked Questions
What is Cronbach's alpha?
Cronbach's alpha is a measure of internal consistency for a set of items intended to assess the same construct. It is often used to summarize the reliability of multi-item scales in survey and test development.
How do I obtain the average inter-item correlation?
Many statistical packages can compute inter-item correlations for a set of variables. The average inter-item correlation is the mean of all pairwise correlations among items in the scale. Some researchers also report it alongside Cronbach's alpha when describing reliability.
Is there a universal cutoff value for Cronbach's alpha?
There is no single universal cutoff that applies to all fields and purposes. Values around 0.70 are sometimes described as a starting point for acceptable internal consistency, but appropriate levels depend on the context, the stakes of decisions, and the nature of the construct being measured.
Can Cronbach's alpha be negative?
Yes, alpha can be negative when items are strongly negatively related or when the assumptions underlying the formula are violated. A negative value usually indicates that items may not be measuring a single construct in a coherent way and that the scale needs careful review.