Datasets:

NanaSomuah0233
/

DIF-Dataset-Comparison

+---
+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
+### 📊 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` | ✅/❌/⚠️ checklist: Can you do MH DIF? Distractor DIF? Text-based DIF? |
+### 📁 Actual Data (`data/` folder)
+#### `data/ednet/` — 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 — 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:** ✅ Selected answers, ✅ Correct answer, ❌ No question text, ❌ No demographics
+#### `data/assistments/` — 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/` — PISA 2018 (schema + download script)
+**Too large for HF upload (~1 GB). Use the download script instead.**
+- `schema.json` — Complete file/column documentation
+- `scripts/download_pisa_locally.py` — Run locally to download + process
+**What PISA has:** ✅ Individual scored item responses (0/1/2), ✅ 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/` — TIMSS 2019 (schema + processing script)
+**Requires manual download from IEA website.**
+- `schema.json` — File structure documentation
+- `scripts/download_timss_locally.py` — Process after manual download
+#### `data/foundational_assist/` — FoundationalASSIST (schema only)
+**Gated dataset — requires data agreement on HF.**
+- `schema.json` — Complete table/column documentation
+- Request access: https://huggingface.co/datasets/ASSISTments/FoundationalASSIST
+**What it has:** ✅ Full question text (HTML), ✅ Student answer text, ✅ Distractor options, ❌ No demographics
+#### `data/oulad/` — OULAD (schema only)
+- `schema.json` — Table/column documentation
+- Download from: https://analyse.kmi.open.ac.uk/open_dataset (or DOI: 10.1038/sdata.2017.171)
+**What it has:** ✅ 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) | ✅ | ✅ A/B/C/D | ✅ | ✅ Gender, SES | ✅ **YES** |
+| **PISA 2018** | ✅ Scored 0/1/2 | ❌ | ⚠️ ~30-50% PDFs | ✅ Gender, SES, Immigration | ✅ **YES** |
+| **TIMSS 2019** | ✅ Scored | ❌ | ⚠️ ~30-50% PDFs | ✅ Gender, SES proxy | ✅ **YES** |
+| **NAEP** | ❌ public | ❌ | ⚠️ partial | ✅ (restricted only) | ❌ **NO** (restricted) |
+| **ASSISTments 2012** | ✅ | ❌ | ❌ | ⚠️ Gender (gaps) | ⚠️ **PARTIAL** |
+| **FoundationalASSIST** | ✅ + answer text | ✅ | ✅ | ❌ | ❌ **NO** (no groups) |
+| **EdNet** | ✅ | ✅ a/b/c/d | ❌ | ❌ | ❌ **NO** (no groups) |
+| **Junyi** | ✅ | ❌ | ❌ | ❌ | ❌ **NO** |
+| **OULAD** | ❌ assessment-level | ❌ | ❌ | ✅ | ❌ **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) — only scored 0/1/2
+2. **NAEP does NOT provide individual responses publicly** — restricted federal license required
+3. **ASSISTments 2015 does NOT have a gender column** — widely misclaimed
+4. **Junyi does NOT have gender** — confirmed absent in all versions
+5. **OULAD has great demographics but NO item-level responses** — 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** — Best combination of individual item responses + demographics. Run `scripts/download_pisa_locally.py`.
+2. **FoundationalASSIST** — Best for distractor-level analysis if you can get demographic linkage (email etrials@assistments.org)
+3. **ASSISTments 2012-2013** — Best ASSISTments version for demographics (gender, SES, ELL)