Data for the paper titled "Cross-country predictors of academic achievement: An applied inquiry with data from 41 countries"

The main purpose of this study is to examine potential predictors of academic achievement using OECD’s original PISA 2018 dataset that has recently been released. Our sample covers 41 countries and data used in our analysis are collected from 282,461 students distributed in 9,317 schools worldwide. Multiple linear regressions are developed and analyzed using stepwise estimation techniques. The results indicate that (i) a strong correlation exists between high test scores and student characteristics & learning climate, i.e. students’ sense of belonging at school, disciplinary climate and students’ fear of failure, (ii) there is a negative relationship between academic performance and teacher enthusiasm & support, (iii) student success is negatively correlated with income level and political and economic freedoms but positively associated with economic competitiveness of a country, (iv) the students in overcrowded classes perform worse than those in lightly populated ones and inadequate or poor educational material deteriorates educational outcomes, and (v) availability of internet connection at home and student’s possession of enjoyable pastime activity have positive impacts on student’s success.