There has been a large number of studies on the performance gap between immigrant and native students (Arikan, Van de Vijver, & Yagmur, 2017; Martin, Liem, Mok, & Xu, 2012; Pivovarova and Powers, 2019; Rodriguez, Valle, Gironelli, Guerrero, Regueiro, & Estévez, 2020). It is critical to identify variables that could have relation with the performance of immigrant students. Numerous studies have been conducted on the variables that are related to immigrant students’ performance. Some studies emphasize immigrants' resilience (Rodriguez et al, 2020), while others focus on exposure to bullying (Karakus, Courtney, & Aydin, 2022; Ponzo, 2013). It was found that native students had higher scores than immigrant students on three indicators of wellbeing such as positive affect, self-efficacy-resilience, and a sense of belonging to the school (Rodríguez et al., 2020). Investigating factors at the student- and country-level that predict immigrant students’ performance may assist policymakers in taking education related action.

Thus, this study focus on identifying student- and country-level variables that are associated with mathematics performance of immigrant students using PISA 2018 data. In this regard, student-level variables are chosen based on Walberg’s theory (Walberg, 2004).  According to Walberg's theory of academic achievement, a student's success is impacted by their characteristics and their environment. The main psychological factors influencing academic achievement were categorized into three groups. Student ability, instruction, and psychological environment are the categories. Student aptitude refers to a student's capacity, growth, drive, or predisposition for extreme perseverance in academic work. Both the quantity and the quality of the instructional time are part of instruction. Psychological environments refer to the morale or students' views of their peers in the classroom and the home environment. The morale of students or their perceptions of their classmates in the classroom and at home constitute psychological settings (Walberg, 2004). On the other hand, country-level variables are chosen based on research. Some research suggests that migrant integration policy index (MIPEX) is associated with achievement (Arikan et al, 2017; He et al., 2017). In addition to this, some research claims that the human development index (HDI) was found associated with achievement (Arikan et al., 2020).

The following research questions of the current study are 

RQ1: Which student-level (motivation to master tasks, resilience, cognitive flexibility/adaptivity, exposure to bullying, sense of belonging, discriminating school climate, students’ attitudes toward immigrants) and country-level (MIPEX and HDI) variables could predict mathematics performance of immigrant students and native students across European countries in PISA 2018?

RQ2: Is there a statistically significant difference between the mathematics performance of first-generation immigrant students, second-generation immigrant students and native students across European countries in PISA 2018?

RQ3: Is there a statistically significant difference between the mathematics performance of first-generation immigrant students, second-generation immigrant students and native students after controlling economic and social status (ESCS)? 

Participants Participants are immigrant students (first- and second-generation) and native students who took the PISA assessment in 2018. Students who were born in another country and whose parents were born in another country are considered first-generation. Second-generation students are those who were born in the country of assessment but whose parents were born elsewhere. Native students are those whose parents (at least one of them) were born in the assessment country (OECD, 2019). The data from 14 European countries such as Croatia, Estonia, Germany, Greece, Iceland, Ireland, Italy, Latvia, Malta, Portugal, Serbia, Slovenia, Spain, Switzerland were included. Measures PISA does not only measure the performance of students but also gather data about students’ backgrounds by applying questionnaires. Student-level variables are chosen from the student questionnaires. Student-level variables are motivation to master tasks, resilience, and cognitive flexibility/adaptivity, exposure to bullying, sense of belonging, discriminating school climate, students’ attitudes toward immigrants. At the country-level, migrant integration policy index and human development index was used. As a control variable economic and social status (ESCS) was used. Data Analysis In order to answer the first research question, multilevel regression analysis will be used to investigate which student-level and country-level variables could predict the mathematics performance of immigrant and native student. For the multilevel regression analyses, MPLUS 7.4 will be used. For the second research question, independent samples t-test will be used to compare the performance of immigrant students and native students. The sample weights and plausible values will be included in the analyses to have unbiased results by using IDB Analyzer (Rutkowski, Gonzalez, Joncas, & Von Davier, 2010). In order to answer the third research question, propensity score matching will be used first and then related comparisons will be performed to examine the performance gap between immigrant students and native students after controlling economic and social status. The MatchIt R package (Ho, Imai, King, & Stuart, 2011) will be used for propensity score matching.

The intraclass correlation will be reported to partition the variation in immigrant students’ math performance by country-level and student-level differences. Moreover, R-square will be used to understand explained variances in mathematics performance by student-level and country-level variables of the current study. Then, student- and country-level variables that are significantly related to mathematics performance will be reported. Multiple independent samples t-test will be used to test if statistically significant difference exists between the mathematics performance of first-generation immigrant students, second-generation immigrant students and native students. The mathematics performance of first-generation immigrant students and native students will be compared. Then, second-generation immigrant students’ and native students’ mathematics performance will be compared. After that, first-generation and second-generation immigrant students’ mathematics performance will be compared. Confidence intervals, t-values and effect sizes will be presented. Since the sample weights and plausible values had to be included in the analyses, ANOVA could not be used. IDB Analyzer will be used because it considers sample weights and plausible values. Applying multiple t-test may increase the chance of type 1 error. Therefore, the Bonferroni adjustment will be used to lower the likelihood of receiving false-positive findings. The adjustment is made by dividing the p-value into the number of t-test (Napierala, 2012). Therefore, the correction will be made by dividing the p-value (0.5) by the number of t-tests (3). Propensity score matching will be used to investigate the performance difference between immigrant students and native students after ESCS has been controlled. The scores of economic and social status will be matched for immigrant and native groups so that the groups will be similar regarding ESCS. Then, the performance of immigrant students and native students will be compared by applying the t-test. Effect size of performance difference before and after matching will be compared.

Arikan, S., Van de Vijver, F. J., & Yagmur, K. (2017). PISA mathematics and reading performance differences of mainstream European and Turkish immigrant students. Educational Assessment, Evaluation and Accountability, 29(3), 229-246. Arikan, S., van de Vijver, F. J., & Yagmur, K. (2020). Mainstream and immigrant students’ primary school mathematics achievement differences in European countries. European Journal of Psychology of Education, 35(4), 819-837. Ho, D., Imai, K., King, G., & Stuart, E. A. (2011). MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. Journal of Statistical Software, 42(8), 1–28. doi:10.18637/jss IEA (2022). Help Manual for the IEA IDB Analyzer (Version 5.0). Hamburg, Germany. (Available from www.iea.nl) Karakus, M., Courtney, M., & Aydin, H. (2022). Understanding the academic achievement of the first-and second-generation immigrant students: A multi-level analysis of PISA 2018 data. Educational Assessment, Evaluation and Accountability, 1–46. Martin, A. J., Liem, G. A., Mok, M., & Xu, J. (2012). Problem solving and immigrant student mathematics and science achievement: Multination findings from the Programme for International Student Assessment (PISA). Journal of educational psychology, 104(4), 1054. Napierala, M. A. (2012). What is the Bonferroni correction? Aaos Now, 40-41. OECD (2019), PISA 2018 Results (Volume III): What School Life Means for Students’ Lives, PISA, OECD Publishing, Paris, https://doi.org/10.1787/acd78851-en. Pivovarova, M., & Powers, J. M. (2019). Generational status, immigrant concentration and academic achievement: comparing first and second-generation immigrants with third-plus generation students. Large- scale Assessments in Education, 7(1), 1-18. Ponzo, M. (2013). Does bullying reduce educational achievement? An evaluation using matching estimators. Journal of Policy Modeling, 35(6), 1057–1078. Rodríguez, S., Valle, A., Gironelli, L. M., Guerrero, E., Regueiro, B., & Estévez, I. (2020). Performance and well-being of native and immigrant students. Comparative analysis based on PISA 2018. Journal of Adolescence, 85, 96–105. Rutkowski, L., Gonzalez, E., Joncas, M., & Von Davier, M. (2010). International large-scale assessment data: Issues in secondary analysis and reporting. Educational researcher, 39(2), 142-151. Walberg, H. J. (2004). Improving educational productivity: An assessment of extant research. The LSS Review, 3(2), 11-14.