Students’ 2018 PISA reading self-concept: Identifying predictors and examining model generalizability for emergent bilinguals

Decades of research have indicated that reading self-concept is an important predictor of reading achievement. During this period, the population of emergent bilinguals has continued to increase within United States' schools. However, the existing literature has tended to examine native English speakers' and emergent bilinguals' reading self-concept in the aggregate, thereby potentially obfuscating the unique pathways through which reading self-concept predicts reading achievement. Furthermore, due to the overreliance of native English speakers in samples relating to theory development, researchers attempting to examine predictors of reading achievement may a priori select variables that are more aligned with native English speakers' experiences. To address this issue, we adopted Elastic Net, which is a theoretically agnostic methodology and machine learning approach to variable selection to identify the proximal and distal predictors of reading self-concept for the entire population; in our study, participants from the United States who participated in PISA 2018 served as the baseline group to determine significant predictors of reading self-concept with the intent of identifying potential new directions for future researchers. Based on Elastic Net analysis, 20 variables at the student level, three variables at the teacher level, and 12 variables at the school level were identified as the most salient predictors of reading self-concept. We then utilized a multilevel modeling approach to test model generalizability of the identified predictors of reading self-concept for emergent bilinguals and native English speakers. We disaggregated and compared findings for both emergent bilinguals and native English speakers. Our results indicate that although some predictors were important for both groups (e.g., perceived information and communications technologies competence), other predictors were not (e.g., competitiveness). Suggestions for future directions and implications of the present study are examined.