NanaSomuah0233/DIF-Dataset-Comparison

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) 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)