Reliability Calculator

Acceptable reliability varies by context and metric. For Cronbach's alpha (internal consistency): α ≥ 0.70 is acceptable, α ≥ 0.80 is good, α ≥ 0.90 is excellent. Values above 0.95 may indicate item redundancy. For test-retest reliability: r ≥ 0.70 is acceptable, r ≥ 0.80 is good. For inter-rater reliability (Cohen's kappa): κ ≥ 0.60 is substantial, κ ≥ 0.80 is almost perfect. Standards are stricter for high-stakes assessment (clinical diagnosis, personnel decisions) than exploratory research. Always report exact values, not just "acceptable" - let readers judge adequacy for their purposes.

Cronbach's alpha measures internal consistency - how closely related a set of items are as a group (do scale items measure the same construct?). Calculated from a single administration. Test-retest reliability measures stability over time - do participants get similar scores when tested twice (days or weeks apart)? Assesses temporal consistency. A scale can have high internal consistency but low test-retest reliability if the construct itself changes over time. Both are important: internal consistency shows items cohere, test-retest shows scores are stable and reproducible.

Calculate inter-rater reliability whenever human judgment codes, rates, or scores data: qualitative coding of interviews, behavioral observations, content analysis, essay scoring, medical diagnosis, video analysis, or any subjective categorization. Have 2-3 raters independently code the same sample (typically 10-20% of total data). Calculate agreement using Cohen's kappa (2 raters, nominal data), Fleiss' kappa (3+ raters), or intraclass correlation (continuous ratings). Inter-rater reliability demonstrates coding is systematic and not idiosyncratic to one researcher, strengthening validity and trustworthiness of findings.