Interest in understanding creativity through Programme for International Student Assessment (PISA) data is on the rise, yet researchers face methodological challenges in synthesizing findings across various constructs, measures, and datasets. Meta-analysis--a valuable methodology for synthesizing quantitative data--remains underutilized in creativity research involving large-scale assessments like PISA. This paper provides guidelines for applying meta-analytic techniques to PISA creative thinking assessment data to help researchers address these challenges. It introduces meta-analysis by outlining its definition and advantages, followed by key steps and methodological considerations for synthesizing bivariate and multivariate relationships within PISA. Finally, the paper discusses techniques for managing the computational complexity of meta-analyzing PISA data. Ultimately, these guidelines aim to support researchers in effectively synthesizing PISA data to advance the study of creativity.