| { |
| "dataset": "PISA 2018 (OECD)", |
| "source": "https://webfs.oecd.org/pisa2018/ (direct SPSS downloads available)", |
| "access": "FREE direct download", |
| "scale": "~600K students, ~900 item columns, 80+ countries", |
| "files": { |
| "SPSS_STU_COG.zip (456 MB)": { |
| "CNTSTUID": "Student ID", |
| "CNT": "Country", |
| "BOOKID": "Booklet", |
| "CM*Q*S": "Math items (scored 0/1/2, 7=not administered)", |
| "CR*Q*S": "Reading items", |
| "CS*Q*S": "Science items" |
| }, |
| "SPSS_STU_QQQ.zip (478 MB)": { |
| "CNTSTUID": "Student ID (merge key)", |
| "CNT": "Country", |
| "ST004D01T": "Gender (1=Female, 2=Male)", |
| "ESCS": "SES index (continuous)", |
| "IMMIG": "Immigration (1=native,2=1st,3=2nd gen)", |
| "LANGN": "Language at home", |
| "PV1MATH-PV10MATH": "Plausible values" |
| } |
| }, |
| "download_urls": { |
| "cognitive": "https://webfs.oecd.org/pisa2018/SPSS_STU_COG.zip", |
| "questionnaire": "https://webfs.oecd.org/pisa2018/SPSS_STU_QQQ.zip" |
| }, |
| "python_code": "\nimport pyreadstat\n# Download and extract the zips first, then:\ndf_cog, meta = pyreadstat.read_sav('COG/CY07_MSU_STU_COG.sav')\ndf_qqq, meta = pyreadstat.read_sav('QQQ/CY07_MSU_STU_QQQ.sav')\ndf = df_qqq.merge(df_cog, on=['CNTSTUID','CNT'])\n# Now df has items + demographics per student\n", |
| "has_demographics": true, |
| "has_item_responses": true, |
| "has_selected_answer": false, |
| "has_question_text": "PARTIAL ~30-50% in separate OECD PDFs" |
| } |
