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---
license: cc-by-4.0
tags:
- education
- psychometrics
- DIF
- differential-item-functioning
- item-response-theory
- assessment
---
# DIF Dataset Comparison & Collected Data
A curated collection of educational assessment datasets evaluated for **Differential Item Functioning (DIF)** analysis, with actual downloaded data where available.
## What's Here
### πŸ“Š Comparison Spreadsheets
| File | Description |
|------|-------------|
| `DIF_Dataset_Comparison_VERIFIED.xlsx` | Main Excel with 3 sheets: Master Comparison, Column Inventory, DIF Readiness Checklist |
| `DIF_Dataset_Master_Comparison.csv` | Dataset-level comparison (responses, demographics, text, scale, access) |
| `DIF_Dataset_Column_Inventory.csv` | Every verified column per dataset with data types and sources |
| `DIF_Dataset_Readiness_Checklist.csv` | βœ…/❌/⚠️ checklist: Can you do MH DIF? Distractor DIF? Text-based DIF? |
### πŸ“ Actual Data (`data/` folder)
#### `data/ednet/` β€” EdNet (Riiid/Santa, Korean TOEIC prep)
| File | Description | Size |
|------|-------------|------|
| `kt1_merged_500k.parquet` | 500K student responses merged with question metadata | 9 MB |
| `questions.parquet` | All 13,169 questions (IDs, correct answer, part, tags β€” NO text) | 323 KB |
| `item_stats.parquet` | Per-item statistics (p-value, n, mean elapsed time) | 397 KB |
| `answer_distributions.parquet` | Distractor analysis: count of each answer option per item | 154 KB |
| `student_stats.parquet` | Per-student accuracy and timing | 74 KB |
**Columns in kt1:** `timestamp, solving_id, question_id, user_answer (a/b/c/d), elapsed_time, subject_id, correct_answer, is_correct`
**DIF status:** βœ… Selected answers, βœ… Correct answer, ❌ No question text, ❌ No demographics
#### `data/assistments/` β€” ASSISTments 2009 (Massachusetts middle school math)
| File | Description | Size |
|------|-------------|------|
| `assist2009_expanded.parquet` | 274K item-level responses (expanded from nested format) | 608 KB |
| `assist2009_item_stats.parquet` | Per-skill statistics (139 skills) | 9 KB |
| `assist2009_student_stats.parquet` | Per-student accuracy (4,148 students) | 45 KB |
**Columns:** `user_id, item_idx, skill_id, skill_name, correct (0/1), attempt_count, answer_type`
**DIF status:** ❌ This HF version lacks gender. Original 2009 file has gender (~50% missing). **2012-2013 Challenge version** (download from [ASSISTments site](https://sites.google.com/site/assistmentsdata/datasets)) has gender + school + economically_disadvantaged + ELL + IEP.
#### `data/pisa/` β€” PISA 2018 (schema + download script)
**Too large for HF upload (~1 GB). Use the download script instead.**
- `schema.json` β€” Complete file/column documentation
- `scripts/download_pisa_locally.py` β€” Run locally to download + process
**What PISA has:** βœ… Individual scored item responses (0/1/2), βœ… Rich demographics (gender, SES, immigration, country), ❌ No raw selected answer (A/B/C/D), ⚠️ Question text only for ~30-50% of items (in separate PDFs)
#### `data/timss/` β€” TIMSS 2019 (schema + processing script)
**Requires manual download from IEA website.**
- `schema.json` β€” File structure documentation
- `scripts/download_timss_locally.py` β€” Process after manual download
#### `data/foundational_assist/` β€” FoundationalASSIST (schema only)
**Gated dataset β€” requires data agreement on HF.**
- `schema.json` β€” Complete table/column documentation
- Request access: https://huggingface.co/datasets/ASSISTments/FoundationalASSIST
**What it has:** βœ… Full question text (HTML), βœ… Student answer text, βœ… Distractor options, ❌ No demographics
#### `data/oulad/` β€” OULAD (schema only)
- `schema.json` β€” Table/column documentation
- Download from: https://analyse.kmi.open.ac.uk/open_dataset (or DOI: 10.1038/sdata.2017.171)
**What it has:** βœ… Rich demographics (gender, IMD/SES, disability, age), ❌ No item-level responses (assessment-level only)
## Quick DIF Feasibility Matrix
| Dataset | Individual Responses | Selected Option | Question Text | Demographics | **Can Do MH DIF?** |
|---------|---------------------|----------------|---------------|-------------|-------------------|
| **EEDI** (your current) | βœ… | βœ… A/B/C/D | βœ… | βœ… Gender, SES | βœ… **YES** |
| **PISA 2018** | βœ… Scored 0/1/2 | ❌ | ⚠️ ~30-50% PDFs | βœ… Gender, SES, Immigration | βœ… **YES** |
| **TIMSS 2019** | βœ… Scored | ❌ | ⚠️ ~30-50% PDFs | βœ… Gender, SES proxy | βœ… **YES** |
| **NAEP** | ❌ public | ❌ | ⚠️ partial | βœ… (restricted only) | ❌ **NO** (restricted) |
| **ASSISTments 2012** | βœ… | ❌ | ❌ | ⚠️ Gender (gaps) | ⚠️ **PARTIAL** |
| **FoundationalASSIST** | βœ… + answer text | βœ… | βœ… | ❌ | ❌ **NO** (no groups) |
| **EdNet** | βœ… | βœ… a/b/c/d | ❌ | ❌ | ❌ **NO** (no groups) |
| **Junyi** | βœ… | ❌ | ❌ | ❌ | ❌ **NO** |
| **OULAD** | ❌ assessment-level | ❌ | ❌ | βœ… | ❌ **NO** (no items) |
## Download URLs (Verified Working)
| Dataset | URL | Registration |
|---------|-----|-------------|
| **PISA 2018 COG** | https://webfs.oecd.org/pisa2018/SPSS_STU_COG.zip (456 MB) | None |
| **PISA 2018 QQQ** | https://webfs.oecd.org/pisa2018/SPSS_STU_QQQ.zip (478 MB) | None |
| **TIMSS 2019** | https://timssandpirls.bc.edu/timss2019/international-database/ | None (website nav) |
| **ASSISTments** | https://sites.google.com/site/assistmentsdata/datasets | None (Google Drive) |
| **FoundationalASSIST** | https://huggingface.co/datasets/ASSISTments/FoundationalASSIST | HF gated |
| **EdNet** | https://huggingface.co/datasets/mgor/EDNet | None |
| **OULAD** | https://analyse.kmi.open.ac.uk/open_dataset | None |
| **NAEP** | https://www.nationsreportcard.gov/nde/ | Restricted-use license |
## ⚠️ Corrections from Common Misconceptions
1. **PISA/TIMSS do NOT give raw selected answers** (A/B/C/D) β€” only scored 0/1/2
2. **NAEP does NOT provide individual responses publicly** β€” restricted federal license required
3. **ASSISTments 2015 does NOT have a gender column** β€” widely misclaimed
4. **Junyi does NOT have gender** β€” confirmed absent in all versions
5. **OULAD has great demographics but NO item-level responses** β€” assessment-level only
6. **FoundationalASSIST has the best item content but ZERO demographics**
7. **EdNet has excellent response data but NO demographics at all**
## Recommended Path for DIF Research
1. **PISA 2018** β€” Best combination of individual item responses + demographics. Run `scripts/download_pisa_locally.py`.
2. **FoundationalASSIST** β€” Best for distractor-level analysis if you can get demographic linkage (email etrials@assistments.org)
3. **ASSISTments 2012-2013** β€” Best ASSISTments version for demographics (gender, SES, ELL)