This study uses hierarchical linear modeling (HLM) to analyze data from PISA 2006 for nations experiencing high rates of immigration (i.e., Germany, Spain, Canada, the United States, Australia and New Zealand). The outcome measures used were achievement scores in science (i.e., scientific literacy). The variables examined at the student level were science self-efficacy, science self-concept, immigrant status and socioeconomic status. The variables examined at the school level were student level aggregates of school proportion of immigrants and school socioeconomic status. In the HLM null models, the intraclass correlations for the all countries except for Germany ranged from .16 to .29 (Germany’s was between .57 and .68). In the final models, at level-1 country, immigrant status tended to negatively influence achievement (i.e., non-native students are predicted to have lower performance), while science self-efficacy and science self-concept positively influenced achievement. The student level ESCS variable also impacted achievement positively. At the school level, level-2, school mean ESCS or school proportion of immigrants was found to significantly influence the level-1 predictors; however, agood deal of variability across nations was observed. The findings from this study demonstrate distinct national differences in the relationships between science self-beliefs, immigrant status and academic achievement.