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Using Hierarchical Linear Modelling (HLM), this study aimed to identify factors such as ESL/ELL/EAL status that would predict students’ reading performance in an English language arts exam taken across Canada. Using data from the 2007 administration of the Pan-Canadian Assessment Program (PCAP) along with the accompanying surveys for students and the schools, a two-level (student level and school level) HLM model was analyzed for predictive relationships. Results showed that, at the student level, predictors such as students’ participation in class discussions, language spoken at home, parents’ encouragement to read at a young age, and the number of individual projects requiring students to work outside of class contributed significantly to the students’ reading scores. However, none of the school-level predictors were found to be significant. All the significant predictors contributed to only 12% of the variability in this HLM model. Identifi cation of more signi cant variables is needed in order to have a full picture of students’ reading competence and achievement. S’appuyant sur la modelisation lineaire hierarchique (MLH), ce e etude porte sur l’identi cation des facteurs, comme le statut ALS/ELL/ALA, qui prediraient les acquis en lecture d’eleves lors d’un examen d’anglais administre partout au Canada. Les auteures ont employe des donnees du Programme pancanadien d’eva luation (PPCE), y compris les sondages connexes pour les eleves et les ecoles, a n d’analyser les liens predictifs d’un modele HLM a deux niveaux (eleve et ecole). Les resultats indiquent que les predicteurs tels la participation des eleves aux dis- cussions en classe, la langue parlee a la maison, la mesure dans laquelle les parents encouragent leurs enfants a lire des un jeune age et le nombre de projets individuels exigeant du travail a l’exterieur de la salle de classe, contribuaient de facon significative aux resultats des eleves en lecture. Toutefois, aucun des predicteurs au niveau de l’ecole ne s’est revele comme etant significatif. Dans leur ensemble, les predicteurs significatifs n’ont contribue qu’a 12% de la variabilite du modele MLH. A n d’arriver a une vue globale du rendement et de la competence en lec ture des eleves, il faudra identifier plus de variables signi catifs.

Using Hierarchical Linear Modelling to Examine Factors Predicting English Language Students’ Reading Achievement