Content Validity
VerifiedPsychometrics (legacy hub)
Run this test straight away on a free built-in teaching dataset — no data of your own needed — or bring your own. Either opens the guided workspace: variable setup, assumption diagnostics, results with effect sizes and confidence intervals, figures, and APA-ready reporting.
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Quantifies whether a scale's items adequately represent the construct domain, judged by a panel of CONTENT EXPERTS (typically 5-10) rating each item's relevance and clarity.
Reports four complementary statistics: (1) Lawshe's CVR (Content Validity Ratio) per item; (2) Polit-Beck I-CVI (item-level CVI) and S-CVI (scale-level CVI, both /Ave and /UA versions); (3) Aiken's V (proportion of agreement above chance); (4) Fleiss' κ across raters (chance-corrected agreement). Items with CVR / I-CVI below thresholds are flagged for revision or removal BEFORE pilot testing.
Worked example
Do expert ratings support the items' content validity?
Ten experts rated each item's relevance; the item-level Content Validity Index (I-CVI) and scale-level S-CVI summarise agreement.
18 of 20 items had I-CVI ≥ .78 (acceptable) and the scale-level S-CVI/Ave was .91, supporting content validity.
Content validity was supported, S-CVI/Ave = .91, with 18 of 20 items at I-CVI ≥ .78.
When to use it
- Expert-panel content validation (pre-pilot)30-item depression scale candidate, 8 expert raters.
- Re-content-validation after scale revisionPilot of 25-item scale flags items 8 + 14 + 22 as psychometrically weak (item-total r < 0.20).
When NOT to — use instead
- Construct validity from data correlationsContent validity is JUDGED by experts; construct validity is COMPUTED from data. → Basic Validity
- Reliability assessmentContent validity is about item-construct fit; for reliability use α / ω. → Cronbach's Alpha
- No expert raters availableContent validity REQUIRES expert ratings. → Survey Data Quality Screen
- Single raterCVR / I-CVI / κ all require ≥ 3 raters (5-10 typical). → Survey Data Quality Screen
Assumptions (and what to do if they fail)
Check: See the assumption diagnostics in the workspace.
If violated: The workspace flags this and suggests a robust or nonparametric alternative.
Check: See the assumption diagnostics in the workspace.
If violated: The workspace flags this and suggests a robust or nonparametric alternative.
Check: See the assumption diagnostics in the workspace.
If violated: The workspace flags this and suggests a robust or nonparametric alternative.
Check: See the assumption diagnostics in the workspace.
If violated: The workspace flags this and suggests a robust or nonparametric alternative.
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