You have a multi-item questionnaire and need to report reliability. Should you calculate Cronbach’s alpha, McDonald’s omega, or both? Start with the score you intend to interpret and the structure of the items. Choosing the larger coefficient is not a defensible decision rule.
This guide concerns reliability of questionnaire composite scores. Agreement between observers is a different question; see the inter-rater reliability guide.
Alpha is computed from item variances and covariances. Interpreting it as score reliability requires assumptions about how the items measure the construct and how their errors relate. A familiar sufficient model has equal factor loadings and uncorrelated errors. Real questionnaires often depart from that model.
A high alpha does not establish that the items measure one construct or that the score supports your intended decision. Adding similar items can raise alpha while narrowing what the questionnaire covers. Review the item content and dimensional structure before interpreting the coefficient. Morera and Stokes’s review of common alpha misconceptions explains why reliability and dimensionality should not be conflated.
Omega uses an explicit factor model to estimate reliability and can accommodate unequal loadings. That is useful when items contribute differently to the construct. Its value depends on the model being a credible description of the responses; an impressive omega cannot rescue a poorly fitting model.
Specify which omega you report. Omega total concerns variance attributable to all modeled common factors. In an appropriate bifactor model, omega hierarchical concerns the general factor alone. Those are different questions. Do not use the names interchangeably or fit a complex model merely to obtain a preferred coefficient.
Consider a fictional staff survey with separate support and workload subscales. An overall coefficient for every item does not establish that a single combined score is meaningful. If the intended reporting uses two subscales, assess each score and explain why its items belong together.
For ordered response categories, distinguish estimates based on observed responses from estimates based on modeled underlying continuous responses. A coefficient calculated from polychoric correlations does not automatically describe the reliability of the raw sum your organization reports.
Identify the instrument version, population, sample size, scoring rules, coefficient, and estimation method. Include uncertainty intervals when feasible, along with the factor model and relevant diagnostics when reporting omega. If you report both alpha and omega, explain why; do not quietly select whichever exceeds a threshold.
A value of .70 is not a universal pass mark. Required precision depends on the use and consequences of error. An exploratory group summary and an individual eligibility decision demand different evidence. Internal consistency also does not answer whether scores are stable over time; that may require a separate retest study.
Check coding errors, poorly understood wording, restricted response variation, multidimensionality, and missing-data handling. Avoid deleting items only to increase a coefficient: doing so may remove essential content and creates a revised instrument that needs review. See the questionnaire adaptation guide.
Use the free measurement-review checklist to record the score, intended use, and evidence still needed. If the factor structure is uncertain, read EFA vs. CFA before treating reliability estimation as the first step.
Discuss a measurement review with DASS if you need a plan for your instrument, respondent population, and reporting decisions.
Deng and Chan: Testing the Difference Between Reliability Coefficients Alpha and Omega; Raykov and Marcoulides: Thanks Coefficient Alpha, We Still Need You!
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