pisa-international

To calculate PISA country means with correct standard errors, you must combine all 10 plausible values using Rubin's rules. This Skill automates that aggregation process while accounting for both sampling and imputation variability.

BRR replicate weight variance estimation for TIMSS data works by computing sampling variance across 80 replicate weights. This Skill applies the correct Fay coefficient of k=0.5 to produce statistically valid standard errors.

You can run SES gradient regression for cross-national comparisons of PISA scores by fitting ESCS regressions by country. This Skill generates valid cross-country figures complete with accurate uncertainty intervals.

Yes, you can use pandas and statsmodels to analyze PISA 2022 data with plausible values. This Skill leverages those dependencies alongside numpy and scipy to compute BRR-based standard errors from your student datasets.

PISA cross-national comparison confidence intervals appear too narrow when plausible values or BRR replicate weights are ignored. This Skill resolves the issue by combining all 10 PVs with Fay-correct BRR variance estimation using Rubin's rules.