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

Published: 11-04-2020| Version 1 | DOI: 10.17632/b2zkx4nwh9.1
Contributor:
Erkan Erdogdu

Description

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.

Files

Steps to reproduce

* Stata do file clear use "C:\Data_41Countries_WorkingSample.dta" gen lscrre = log(scrre) gen lscrmt = log(scrmt) gen lscrsc = log(scrsc) * Backward stepwise selection, removing terms with p >= 0.2 and adding those with p < 0.1 * stepwise, pr(.2) pe(.1): regress y x1 x2 x3 x4 stepwise, pr(.2) pe(.1): regress lscrre leacs lespt lepvs less lems ictis ictihw ictihd ictvgh qsmta qsmem qsmts qsmpi slcdc slcbs slcsco slcscm slcls slcml slcse slcff slcthl slcte slcts peegdp peegci peecpi peeefi peefiw peeglo, robust stepwise, pr(.2) pe(.1): regress lscrmt leacs lespt lepvs less lems ictis ictihw ictihd ictvgh qsmta qsmem qsmts qsmpi slcdc slcbs slcsco slcscm slcls slcml slcse slcff slcthl slcte slcts peegdp peegci peecpi peeefi peefiw peeglo, robust stepwise, pr(.2) pe(.1): regress lscrsc leacs lespt lepvs less lems ictis ictihw ictihd ictvgh qsmta qsmem qsmts qsmpi slcdc slcbs slcsco slcscm slcls slcml slcse slcff slcthl slcte slcts peegdp peegci peecpi peeefi peefiw peeglo, robust regress lscrre leacs lepvs lespt less ictihd qsmem qsmta qsmts slcbs slcdc slcff slcse slcte slcts peeefi peefiw peegci peegdp estat hettest regress lscrmt leacs lepvs ictihd qsmem qsmts slcbs slcdc slcff slcte slcts peefiw peegci peegdp estat hettest regress lscrsc leacs lespt less ictihd ictvgh qsmem qsmta qsmts slcbs slcdc slcff slcls slcml slcscm slcse slcte slcts peefiw peegci peegdp estat hettest clear exit