A secondary analysis of PISA 2018 data was conducted to investigate the reading achievement of students with an immigrant background. While prior research suggests the importance of demographic characteristics in secondary PISA analyses, a major critique is that PISA centers destination characteristics over origin ones, limiting the study of the association between origin characteristics and academic achievement. This study addressed the critique by linking PISA data with data of origin country characteristics to allow for analyses that could not be conducted with the PISA data set alone. The study linked PISA data with data of origin country characteristics, centered the origin-based characteristics in the analyses, and then evaluated the utility of linking this additional data for explaining education outcomes. Multilevel statistical modeling was used to model the association between origin country characteristics and the academic achievement of students with an immigrant background. Linked data were: (1) Language Distance—student-level measure of similarity between home and school language; (2) Human Development Index—country-level measure of human development; (3) Global Adaptation Index—country-level measure of climate vulnerability and readiness to improve resilience/climate adaptation; and (4) forced displacement ratio—country-level ratio between inward/outward forced displacement. The principal findings were the negative association between language distance and PISA reading scores (i.e., one unit increase in language distance associated with -0.31 unit change in reading score) and that language distance afforded a closer look at the association between language and PISA reading scores when applied to four particular countries and their language pairs (e.g., Switzerland, Finland, Qatar, Israel).
Read
