load

Load scripts — one per PISA wave

Each script reads the raw OECD student-questionnaire file for its wave, extracts the variables used in the dissertation, recodes PISA's special missing-value codes to missing, attaches readable labels, and writes cleaned output to scripts/output/.

Raw data is expected under scripts/data/<year>/ — see the root README for filenames and download instructions.

Script	Input format	Variables extracted	Main output
load_pisa2000.py	fixed-width .txt	reading time (ST34), 9 attitude items (ST35a–i), books at home (ST37), WEALTH, HISEI, parental education, cultural activities	pisa2000_cleaned.csv
load_pisa2003.py	fixed-width .txt	books at home	books-at-home table + bar chart
load_pisa2006.py	fixed-width .txt	books at home	books-at-home table + bar chart
load_pisa2009.py	fixed-width .txt	books at home, reading time (ST23), 11 attitude items (ST24), reading types (ST25), WEALTH, HISEI	pisa2009_cleaned.csv
load_pisa2012.py	fixed-width .txt	books at home	books-at-home table + bar chart
load_pisa2015.py	SPSS .sav	books at home (ST013)	pisa2015_amountbooks.csv + bar chart
load_pisa2018.py	SPSS .sav	~130 variables: books at home, reading time, attitudes, reading preferences/format, metacognition indices, SES (ESCS, WEALTH, HISEI), parental education, ICT use, school climate, well-being, global-competence indices, and more	2018output/newpisa2018_cleaned_all_countries.csv
load_pisa2022.py	SPSS .sav	books at home (ST255)	pisa2022_books_overall.csv + bar chart

Notes:

• The 2003, 2006, 2012, 2015 and 2022 waves are only used for the books-at-home trend (the reading-time and attitude questions were asked in the reading-focus waves: 2000, 2009, 2018), so their load scripts extract just the books variable and also produce the wave's descriptive table/chart directly.
• Column positions for the fixed-width waves come from the official OECD codebooks.
• 2018 is the analysis wave for the regressions, hence the much larger variable set: every raw PISA variable name is renamed to a self-documenting label (e.g. ST160Q02IA → att_2_reading_hobby) — the rename map inside load_pisa2018.py doubles as the codebook.