File size: 1,457 Bytes
f181a81 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"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"
} |