Are PHQ-A and GAD-7 Fair Across Cultures?

ISEF Category: Behavioral and Social Sciences

Subcategory: Other  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: Full Year

The Hook

A survey question can work well in one country and misfire in another. That matters when you turn a few answers into a mental-health score. If two groups read the same item in different ways, the score stops being a clean comparison. You can test that problem with real public data.

What Is It?

PHQ-A and GAD-7 are short screeners. They ask teens about depression and anxiety symptoms, then turn the answers into a score. Cross-cultural validity asks a simple question, does the screener measure the same thing the same way for every group you compare?

Think of it like a ruler. If the ruler stretches a little in one country, the numbers still look real, but the measurement shifts. Differential item functioning, or DIF, checks whether one question behaves differently across groups even when the underlying symptom level is the same. Measurement invariance checks the bigger picture, whether the full screener keeps the same structure across groups.

Why This Is a Good Topic

This is a strong science fair topic because you can test a real fairness question with public data, careful coding, and statistics. You do not need to recruit participants or run a wet lab. You can learn how wording, translation, and response patterns change what a mental-health score really means.

Research Questions

• How does the factor structure of PHQ-A change across countries or language groups?
• Does any PHQ-A item show differential item functioning across gender groups after matching on symptom level?
• To what extent does GAD-7 keep measurement invariance across countries with different languages?
• Which items contribute most to non-invariance in the two screeners?
• What is the effect of collapsing response categories on DIF results?
• Does age group within adolescence change item behavior after controlling for symptom level?

Basic Materials

• Public multi-country dataset with item-level adolescent screener responses and group labels.
• Laptop or desktop computer.
• R and RStudio for cleaning and analysis.
• Spreadsheet software for codebook review and data checks.
• A notebook or document for tracking variable names, recodes, and model choices.

Advanced Materials

• Protected-access cross-national dataset with original language forms and metadata.
• R with lavaan, semTools, and difR packages.
• High-capacity computer for repeated subgroup models.
• Full codebook and translation notes for every survey version.
• Access to a statistician, faculty mentor, or methods advisor for model review.

Software & Tools

• R: Fits measurement models, DIF tests, and group comparisons on ordinal survey data.
• RStudio: Gives you a clear workspace for writing, running, and checking R code.
• lavaan: Fits confirmatory factor analysis and measurement invariance models.
• difR: Tests whether individual items behave differently across groups.
• jamovi: Helps you inspect data, spot coding issues, and do quick checks before modeling.

Experiment Steps

1. Define the exact comparison groups and the screener version you will study.
2. Decide whether you will test measurement invariance, item-level DIF, or both.
3. Clean the dataset so every group follows the same scoring rules, age range, and missing-data rule.
4. Build the item-level model and check whether the screener measures the same construct in each group.
5. Interpret any flagged items in terms of wording, translation, and response style.
6. Plan a sensitivity check that shows whether your conclusions change when you adjust the grouping choices.

Common Pitfalls

• Mixing datasets with different response scales, which makes item scores impossible to compare.
• Comparing groups with tiny sample sizes, which can create false DIF flags from noise.
• Treating translation as the only source of bias, when response style and stigma can also shift item behavior.
• Reporting total score differences without checking whether the screener measures the same construct in each group.
• Ignoring missing data patterns, which can leave one country or language group underrepresented.

What Makes This Competitive

A stronger project compares both the full factor structure and the item-level behavior, instead of stopping at one score test. You can go beyond flagging a problem item by showing how much partial invariance changes the final score. The best version also explains why an item shifts, such as wording, translation, or response style. That makes the project about measurement quality, not just group differences.

Project Variations

• Compare PHQ-A item behavior across age bands within adolescence instead of across countries.
• Test whether English and translated versions of GAD-7 behave differently within one international dataset.
• Compare ordinal factor analysis and DIF results to see whether both methods flag the same weak items.

Learn More

• PubMed: Search for review articles on adolescent depression screening, anxiety screening, measurement invariance, and differential item functioning.
• WHO Global School-based Student Health Survey: Read the questionnaires and documentation to find public adolescent mental-health data across countries.
• NIH Office of Behavioral and Social Sciences Research: Find free resources on survey measurement and cross-group comparisons.
• UCLA IDRE: Use free tutorials on confirmatory factor analysis, measurement invariance, and ordinal data analysis in R.
• R Project and package vignettes: Find documentation for lavaan, semTools, and difR when you build your analysis workflow.