Cross-Cultural Validity

Coming soon

Psychometrics (legacy hub)

This test is implemented and is currently going through StatMinds’ production verification: every statistic is independently checked against a trusted reference (scipy / R), locked with regression tests, and the screen is exercised across assumption-met/violated and significant/non-significant scenarios before it opens up.

See what’s live now

Comprehensive cross-cultural validity assessment for an adapted instrument.

Three layers: (1) SOURCE-VS-TARGET DISTRIBUTION COMPARISON — score distributions, item-endorsement frequencies, response-style indices across source and target populations; (2) MEASUREMENT INVARIANCE LADDER — configural / metric / scalar / strict CFA invariance across groups; (3) PER-ITEM DIF — Mantel-Haenszel + logistic per item to identify items that function differently in the target culture. Required by ITC Translation and Adaptation Guidelines (2017) for any cross-cultural / cross-language scale use.

Worked example

Does a translated scale work equivalently in a new culture?

After translation and back-translation, cross-cultural validity was checked via measurement invariance and reliability in the new sample.

Result

Reliability held (α = .85) and metric invariance was supported (ΔCFI = −.007), indicating the translated scale is usable cross-culturally.

How you'd report it (APA)

The translated scale showed good reliability (α = .85) and metric invariance across cultures (ΔCFI = −.007).

When to use it

  • Translated-scale validation against source data
    20-item depression scale translated English → Vietnamese, validated on US source (n=400) + Vietnamese target (n=380).
  • Multi-country international scale validation
    Cross-cultural anxiety scale used in 5 countries (US, Vietnam, Brazil, Egypt, Japan).

When NOT to — use instead

  • Single group / single culture
    Cross-cultural validity requires ≥ 2 groups / cultures. → Basic Validity
  • Construct validity within a single culture
    If both groups are within one culture, use measurement_invariance for the basic invariance ladder. → Measurement Invariance
  • Item-level DIF analysis only
    If you only need DIF across groups, use the dif analysis directly — cross_cultural is the FULL battery. → Differential Item Func.
  • Per-group sample too small (< 200)
    Multi-group invariance + DIF need adequate per-group sample. → Confirmatory FA

Assumptions (and what to do if they fail)

Items are numeric (binary or ordinal)medium

Check: See the assumption diagnostics in the workspace.

If violated: The workspace flags this and suggests a robust or nonparametric alternative.

Same items administered in both groupsmedium

Check: See the assumption diagnostics in the workspace.

If violated: The workspace flags this and suggests a robust or nonparametric alternative.

Group variable is exactly 2 levelsmedium

Check: See the assumption diagnostics in the workspace.

If violated: The workspace flags this and suggests a robust or nonparametric alternative.

Listwise exclusion of missing item responsesmedium

Check: See the assumption diagnostics in the workspace.

If violated: The workspace flags this and suggests a robust or nonparametric alternative.

Ready to run a Cross-Cultural Validity on your own data?

Guided setup, automatic assumption checks, effect sizes, figures and an APA write-up.

Run this test →