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School characteristics mediating the relationship between school socioeconomic status and mathematics achievement

Year 2022, Volume: 9 Issue: 1, 98 - 117, 10.03.2022

Abstract

While numerous studies have reported the effect of school socioeconomic status (SES) on achievement, the factors that can cause this relationship are not well established. This study is, therefore, an attempt to understand school SES and students' mathematics achievement relationship by assuming that this relationship occurs through a correlation between school SES and school characteristics. Identifying these school characteristics is crucial to reduce the relation between SES and achievement for educational equity. Focusing on the 8th-grade mathematics data from Trends in International Mathematics and Science Study (TIMSS) 2015, this study aimed to identify school characteristics (quality of mathematics teaching at school, discipline at school, sense of school belonging, and school academic emphasis) that can mediate the relationship between school SES and students' mathematics achievement. The results of multilevel regression analyses showed that controlling school characteristics reduced the relationship between school SES and students' mathematics achievement in most of the educational systems. However, the results of multilevel multiple mediation analysis showed that the relationship between school SES and students' mathematics achievement were mediated through discipline at school, school academic emphasis, or sense of school belonging in some educational systems. In addition, the results indicated that the quality of mathematics teaching at school was not a mediator in this relationship. These results suggest the need for eliminating the effect of school SES on some school characteristics to improve equity in education.

References

  • Akyuz, G. (2014). The effects of student and school factors on mathematics achievement in TIMSS 2011. Egitim ve Bilim, 39(172), 150-162.
  • Armor, D.J., Cotla, C.R., & Stratmann, T. (2017). Spurious relationships arising from aggregate variables in linear regression. Quality and Quantity, 51(3), 1359-1379.
  • Atlay, C., Tieben, N., Fauth, B., & Hillmert, S. (2019). The role of socioeconomic background and prior achievement for students’ perception of teacher support. British Journal of Sociology of Education, 40(7), 970-991.
  • Bauer, D.J., Preacher, K.J., & Gil, K.M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. Psychological Methods, 11(2), 142-163.
  • Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., & Tsai, Y.M. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom, and student progress. American Educational Research Journal, 47(1), 133–180.
  • Berkowitz, R., Glickman, H., Benbenishty, R., Ben-Artzi, E., Raz, T., Lipshtadt, N., & Astor, R.A. (2015). Compensating, mediating, and moderating effects of school climate on academic achievement gaps in Israel. Teachers College Rec., 117, article no: 070308, 1-34.
  • Berkowitz, R., Moore, H., Astor, R.A., & Benbenishty, R. (2017). A research synthesis of the associations between socioeconomic background, inequity, school climate, and academic achievement. Review of Educational Research, 87(2), 425-469.
  • Boonen, T., Pinxten, M., Van Damme, J., & Onghena, P. (2014). Should schools be optimistic? An investigation of the association between academic optimism of schools and student achievement in primary education. Educational Research and Evaluation, 20, 3–24.
  • Borman, G., & Dowling, M. (2010). Schools and inequality: A multilevel analysis of Coleman’s equality of educational opportunity data. Teachers College Record, 112(5), 1201-1246.
  • Brantlinger, E.A. (2003). Dividing classes: How the middle class negotiates and rationalizes school advantage. Routledge Falmer.
  • Brault, M.C., Janosz, M., & Archambault, I. (2014). Effects of school composition and school climate on teacher expectations of students: A multilevel analysis. Teaching and Teacher Education, 44, 148-159.
  • Broer, M., Bai, Y., & Fonseca, F. (2019). A Review of the Literature on Socioeconomic Status and Educational Achievement. In Socioeconomic Inequality and Educational Outcomes (pp. 7-17). Springer, Cham.
  • Brophy, J., & Good, T.L. (1986). Teacher behavior and student achievement. In M. C. Wittrock (Ed.), Handbook of Research on Teaching (3rd ed., pp. 328–375). Macmillan.
  • Bryk, A., & Schneider, B. (2002). Trust in schools: A core resource for improvement. Russell Sage Foundation.
  • Chmielewski, A.K. (2019). The global increase in the socioeconomic achievement gap, 1964 to 2015. American Sociological Review, 84(3), 517 544. https://doi.org/10.1177/0003122419847165
  • Contini, D., DiTommaso, M.L., & Mendolia, S. (2017). The gender gap in mathematics achievement: Evidence from Italian data. Economics of Education Review, 58, 32-42.
  • Driessen, G. (2002). School composition and achievement in primary education: A large scale multilevel approach. Studies in Educational Evaluation, 28, 347-368.
  • Dumay, X., & Dupriez, V. (2008). Does the school composition effect matter? Evidence from Belgian data. British Journal of Educational Studies, 56, 440 477. http://dx.doi.org/10.1111/j.1467-8527.2008.00418.x
  • Eriksson, K., Helenius, O., & Ryve, A. (2019). Using TIMSS items to evaluate the effectiveness of different instructional practices. Instructional Science, 47(1), 1-18.
  • Goodenow, C. (1993). The psychological sense of school membership among adolescents: Scale development and educational correlates. Psychology in the Schools, 30(1), 79-90.
  • Gustafsson, J.E., Nilsen, T., & Hansen, K.Y. (2016). School characteristics moderating the relation between student socio-economic status and mathematics achievement in grade 8. Evidence from 50 countries in TIMSS 2011. Studies in Educational Evaluation, 57, 16-30.
  • Hansen, K.Y., & Strietholt, R. (2018). Does schooling actually perpetuate educational inequality in mathematics performance? A validity question on the measures of opportunity to learn in PISA. ZDM, 1-16.
  • Hoy, W.K., Tarter, C.J., & Hoy, A.W. (2006). Academic optimism of schools: A force for student achievement. American Educational Research Journal, 43(3), 425-446.
  • Hoy, W. (2012). School characteristics that make a difference for the achievement of all students: A 40-year odyssey. Journal of Educational Administration, 50(1), 76-97.
  • International Association for the Evaluation of Educational Achievement. (2017). IDB Analyzer (version 4.0). IEA Hamburg. http://www.iea.nl/data.html
  • Ker, H.W. (2016). The impacts of student- teacher and school-level factors on mathematics achievement: an exploratory comparative investigation of Singaporean students and the USA students. Educational Psychology, 36(2), 254 276. https://doi.org/10.1080/01443410.2015.1026801
  • Klieme, E., Pauli, C., & Reusser, K. (2009). The Pythagoras study: Investigating effects of teaching and learning in Swiss and German Classrooms, in T. Janik, & T. Seidel (Eds.). The power of video studies in investigating teaching and learning in the classroom, pp. 137–160. Waxmann Verlag.
  • Krull, J.L., & MacKinnon, D.P. (2001). Multilevel modeling of individual and group level mediated effects. Multivariate Behavioral Research,36, 249-277.
  • Lei, H., Cui, Y. & M.M. Chiu. 2016. Affective teacher-student relationships and students ‘externalizing behavior problems: A meta-analysis. Frontiers in Psychology, 7, 1-12. https://doi.org/10.3389/fpsyg.2016.01311
  • Liu, H., Van Damme, J., Gielen, S., & Van Den Noortgate, W. (2015). School processes mediate school compositional effects: model specification and estimation. British Educational Research Journal, 41(3), 423-447.
  • Ma, X., & Wilkins, J.L. (2002). The development of science achievement in middle and high school: individual differences and school effects. Evaluation Review, 26, 395-417.
  • Martin, M.O., Mullis, I.V.S., & Hooper, M. (Eds.). (2016). Methods and Procedures in TIMSS 2015.TIMSS and PIRLS International Study Center Boston College. http://timss.bc.edu/publications/timss/2015-methods.html
  • McCoy, D.C., Roy, A.L., & Sirkman, G.M. (2013). Neighborhood crime and school climate as predictors of elementary school academic quality: A cross-lagged panel analysis. American Journal of Community Psychology, 52, 128–140. https://doi.org/10.1007/ s10464-013-9583-5
  • Mullis, I.V.S., Martin, M.O., Foy, P., & Arora, A. (2011). TIMSS 2011 International Results in Mathematics. Boston College, TIMSS & PIRLS International Study Center, https://timssandpirls.bc.edu/timss2011/international-results-mathematics.html
  • Mullis, I.V.S., Martin, M.O., Foy, P., & Hooper, M. (2016). TIMSS 2015 International Results in Mathematics. Boston College, TIMSS and PIRLS International Study Center. http://timssandpirls.bc.edu/timss2015/international-results/
  • National Council of Teachers of Mathematics [NCTM] (2000). Principles and standards for school mathematics. Author.Reston.VA.
  • Munk, T. (2007). Full-school engagement as a mediator of ethnic and economic composition effects on grade 8 mathematics test scores: a two-level structural equation model. [Unpublished doctoral dissertation]. https://doi.org/10.17615/8n2x-6s56
  • Nilsen, T., Blömeke, S., Hansen, K.Y., & Gustafsson, J.E. (2016). Are school characteristics related to equity? The answer may depend on a country's developmental level. International Association for the Evaluation of Educational Achievement. Policy Brief No. 10.
  • Nilsen, T., & Gustafsson, J.E. (2014). School emphasis on academic success: Exploring changes in science performance in Norway between 2007 and 2011 employing two-level SEM. Educational Research and Evaluation, 20(4), 308-327.
  • Opdenakker, M.C., &Van Damme, J. (2001). Relationship between school composition and characteristics of school process and their effect on mathematics achievement. British Educational Research Journal, 27(4), 407-432.
  • Olmez, I.B. (2020). Modeling mathematics achievement using hierarchical linear models. Elementary Education Online, 19(2), 944 957. https://doi:10.17051/ilkonline.2020.695837
  • Perry, L.B., & McConney, A. (2010). Does the SES of the school matter? An examination of socioeconomic status and student achievement using PISA 2003. Teachers College Record, 112(4), 1137-1162.
  • Preacher, K.J., & Selig, J.P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6(2), 77-98.
  • R Development Core Team (2017). R: A language and environment for statistical computing. The R foundation of statistical computing.
  • Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Sage.
  • Rjosk, C., Richter, D., Hochweber, J., Lüdtke, O., Klieme, E., & Stanat, P. (2014). Socioeconomic and language minority classroom composition and individual reading achievement: The mediating role of instructional quality. Learning and Instruction, 32, 63-72.
  • Rumberger, R. W., & Palardy, G. J. (2005). Does segregation still matter? The impact of student composition on academic achievement in high school. Teachers’College Record, 107(9), 1999-2045.
  • Schmidt, W.H., Burroughs, N.A., Zoido, P., & Houang, R.T. (2015). The role of schooling in perpetuating educational inequality: An international perspective. Educational Researcher, 44(7), 371-386.
  • Shin, J., Lee, H., & Kim, Y. (2009). Student and school factors affecting mathematics achievement: International comparisons between Korea, Japan and the USA. School Psychology International, 30(5), 520-537.
  • Sirin, S.R. (2005). Socioeconomic status and academic achievement: A meta-analysis. Review of Educational Research, 75(3), 417–453.
  • Thapa, A., Cohen, J., Guffey, S., & Higgins-D’Alessandro, A. (2013). A review of school climate research. Review of Educational Research, 83(3), 357-385.
  • Thrupp, M., Lauder, H., & Robinson, T. (2002). School composition and peer effects. International Journal of Educational Research, 37(5), 483-504.
  • Wang, Z., Osterlind, S.J., & Bergin, D.A. (2012). Building mathematics achievement models in four countries using TIMSS 2003. International Journal of Science and Mathematics Education,10(5), 1215-1242.
  • Wang, M.T., & Degol, J.L. (2016). School climate: A review of the construct, measurement, and impact on student outcomes. Educational Psychology Review, 28(2), 315-352.
  • Wu, M. (2004). Plausible values. Rasch Measurement Transactions, 18(2), 976-978.
  • Wu, J.H., Hoy, W.K., & Tarter, C.J. (2013). Enabling school structure, collective responsibility, and a culture of academic optimism. Journal of Educational Administration, 51(2), 176-193.
  • Yavuz, H.C., Demirtasli, R.N., Yalcin, S., & Dibek, M.I. (2017). The effects of student and teacher level variables on TIMSS 2007 and 2011 mathematics achievement of Turkish students. Education and Science, 42(189), 27-47.
  • Zhang, Z., Zyphur, M.J., & Preacher, K.J. (2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12(4), 695-719.

School characteristics mediating the relationship between school socioeconomic status and mathematics achievement

Year 2022, Volume: 9 Issue: 1, 98 - 117, 10.03.2022

Abstract

While numerous studies have reported the effect of school socioeconomic status (SES) on achievement, the factors that can cause this relationship are not well established. This study is, therefore, an attempt to understand school SES and students' mathematics achievement relationship by assuming that this relationship occurs through a correlation between school SES and school characteristics. Identifying these school characteristics is crucial to reduce the relation between SES and achievement for educational equity. Focusing on the 8th-grade mathematics data from Trends in International Mathematics and Science Study (TIMSS) 2015, this study aimed to identify school characteristics (quality of mathematics teaching at school, discipline at school, sense of school belonging, and school academic emphasis) that can mediate the relationship between school SES and students' mathematics achievement. The results of multilevel regression analyses showed that controlling school characteristics reduced the relationship between school SES and students' mathematics achievement in most of the educational systems. However, the results of multilevel multiple mediation analysis showed that the relationship between school SES and students' mathematics achievement were mediated through discipline at school, school academic emphasis, or sense of school belonging in some educational systems. In addition, the results indicated that the quality of mathematics teaching at school was not a mediator in this relationship. These results suggest the need for eliminating the effect of school SES on some school characteristics to improve equity in education.

References

  • Akyuz, G. (2014). The effects of student and school factors on mathematics achievement in TIMSS 2011. Egitim ve Bilim, 39(172), 150-162.
  • Armor, D.J., Cotla, C.R., & Stratmann, T. (2017). Spurious relationships arising from aggregate variables in linear regression. Quality and Quantity, 51(3), 1359-1379.
  • Atlay, C., Tieben, N., Fauth, B., & Hillmert, S. (2019). The role of socioeconomic background and prior achievement for students’ perception of teacher support. British Journal of Sociology of Education, 40(7), 970-991.
  • Bauer, D.J., Preacher, K.J., & Gil, K.M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. Psychological Methods, 11(2), 142-163.
  • Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., & Tsai, Y.M. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom, and student progress. American Educational Research Journal, 47(1), 133–180.
  • Berkowitz, R., Glickman, H., Benbenishty, R., Ben-Artzi, E., Raz, T., Lipshtadt, N., & Astor, R.A. (2015). Compensating, mediating, and moderating effects of school climate on academic achievement gaps in Israel. Teachers College Rec., 117, article no: 070308, 1-34.
  • Berkowitz, R., Moore, H., Astor, R.A., & Benbenishty, R. (2017). A research synthesis of the associations between socioeconomic background, inequity, school climate, and academic achievement. Review of Educational Research, 87(2), 425-469.
  • Boonen, T., Pinxten, M., Van Damme, J., & Onghena, P. (2014). Should schools be optimistic? An investigation of the association between academic optimism of schools and student achievement in primary education. Educational Research and Evaluation, 20, 3–24.
  • Borman, G., & Dowling, M. (2010). Schools and inequality: A multilevel analysis of Coleman’s equality of educational opportunity data. Teachers College Record, 112(5), 1201-1246.
  • Brantlinger, E.A. (2003). Dividing classes: How the middle class negotiates and rationalizes school advantage. Routledge Falmer.
  • Brault, M.C., Janosz, M., & Archambault, I. (2014). Effects of school composition and school climate on teacher expectations of students: A multilevel analysis. Teaching and Teacher Education, 44, 148-159.
  • Broer, M., Bai, Y., & Fonseca, F. (2019). A Review of the Literature on Socioeconomic Status and Educational Achievement. In Socioeconomic Inequality and Educational Outcomes (pp. 7-17). Springer, Cham.
  • Brophy, J., & Good, T.L. (1986). Teacher behavior and student achievement. In M. C. Wittrock (Ed.), Handbook of Research on Teaching (3rd ed., pp. 328–375). Macmillan.
  • Bryk, A., & Schneider, B. (2002). Trust in schools: A core resource for improvement. Russell Sage Foundation.
  • Chmielewski, A.K. (2019). The global increase in the socioeconomic achievement gap, 1964 to 2015. American Sociological Review, 84(3), 517 544. https://doi.org/10.1177/0003122419847165
  • Contini, D., DiTommaso, M.L., & Mendolia, S. (2017). The gender gap in mathematics achievement: Evidence from Italian data. Economics of Education Review, 58, 32-42.
  • Driessen, G. (2002). School composition and achievement in primary education: A large scale multilevel approach. Studies in Educational Evaluation, 28, 347-368.
  • Dumay, X., & Dupriez, V. (2008). Does the school composition effect matter? Evidence from Belgian data. British Journal of Educational Studies, 56, 440 477. http://dx.doi.org/10.1111/j.1467-8527.2008.00418.x
  • Eriksson, K., Helenius, O., & Ryve, A. (2019). Using TIMSS items to evaluate the effectiveness of different instructional practices. Instructional Science, 47(1), 1-18.
  • Goodenow, C. (1993). The psychological sense of school membership among adolescents: Scale development and educational correlates. Psychology in the Schools, 30(1), 79-90.
  • Gustafsson, J.E., Nilsen, T., & Hansen, K.Y. (2016). School characteristics moderating the relation between student socio-economic status and mathematics achievement in grade 8. Evidence from 50 countries in TIMSS 2011. Studies in Educational Evaluation, 57, 16-30.
  • Hansen, K.Y., & Strietholt, R. (2018). Does schooling actually perpetuate educational inequality in mathematics performance? A validity question on the measures of opportunity to learn in PISA. ZDM, 1-16.
  • Hoy, W.K., Tarter, C.J., & Hoy, A.W. (2006). Academic optimism of schools: A force for student achievement. American Educational Research Journal, 43(3), 425-446.
  • Hoy, W. (2012). School characteristics that make a difference for the achievement of all students: A 40-year odyssey. Journal of Educational Administration, 50(1), 76-97.
  • International Association for the Evaluation of Educational Achievement. (2017). IDB Analyzer (version 4.0). IEA Hamburg. http://www.iea.nl/data.html
  • Ker, H.W. (2016). The impacts of student- teacher and school-level factors on mathematics achievement: an exploratory comparative investigation of Singaporean students and the USA students. Educational Psychology, 36(2), 254 276. https://doi.org/10.1080/01443410.2015.1026801
  • Klieme, E., Pauli, C., & Reusser, K. (2009). The Pythagoras study: Investigating effects of teaching and learning in Swiss and German Classrooms, in T. Janik, & T. Seidel (Eds.). The power of video studies in investigating teaching and learning in the classroom, pp. 137–160. Waxmann Verlag.
  • Krull, J.L., & MacKinnon, D.P. (2001). Multilevel modeling of individual and group level mediated effects. Multivariate Behavioral Research,36, 249-277.
  • Lei, H., Cui, Y. & M.M. Chiu. 2016. Affective teacher-student relationships and students ‘externalizing behavior problems: A meta-analysis. Frontiers in Psychology, 7, 1-12. https://doi.org/10.3389/fpsyg.2016.01311
  • Liu, H., Van Damme, J., Gielen, S., & Van Den Noortgate, W. (2015). School processes mediate school compositional effects: model specification and estimation. British Educational Research Journal, 41(3), 423-447.
  • Ma, X., & Wilkins, J.L. (2002). The development of science achievement in middle and high school: individual differences and school effects. Evaluation Review, 26, 395-417.
  • Martin, M.O., Mullis, I.V.S., & Hooper, M. (Eds.). (2016). Methods and Procedures in TIMSS 2015.TIMSS and PIRLS International Study Center Boston College. http://timss.bc.edu/publications/timss/2015-methods.html
  • McCoy, D.C., Roy, A.L., & Sirkman, G.M. (2013). Neighborhood crime and school climate as predictors of elementary school academic quality: A cross-lagged panel analysis. American Journal of Community Psychology, 52, 128–140. https://doi.org/10.1007/ s10464-013-9583-5
  • Mullis, I.V.S., Martin, M.O., Foy, P., & Arora, A. (2011). TIMSS 2011 International Results in Mathematics. Boston College, TIMSS & PIRLS International Study Center, https://timssandpirls.bc.edu/timss2011/international-results-mathematics.html
  • Mullis, I.V.S., Martin, M.O., Foy, P., & Hooper, M. (2016). TIMSS 2015 International Results in Mathematics. Boston College, TIMSS and PIRLS International Study Center. http://timssandpirls.bc.edu/timss2015/international-results/
  • National Council of Teachers of Mathematics [NCTM] (2000). Principles and standards for school mathematics. Author.Reston.VA.
  • Munk, T. (2007). Full-school engagement as a mediator of ethnic and economic composition effects on grade 8 mathematics test scores: a two-level structural equation model. [Unpublished doctoral dissertation]. https://doi.org/10.17615/8n2x-6s56
  • Nilsen, T., Blömeke, S., Hansen, K.Y., & Gustafsson, J.E. (2016). Are school characteristics related to equity? The answer may depend on a country's developmental level. International Association for the Evaluation of Educational Achievement. Policy Brief No. 10.
  • Nilsen, T., & Gustafsson, J.E. (2014). School emphasis on academic success: Exploring changes in science performance in Norway between 2007 and 2011 employing two-level SEM. Educational Research and Evaluation, 20(4), 308-327.
  • Opdenakker, M.C., &Van Damme, J. (2001). Relationship between school composition and characteristics of school process and their effect on mathematics achievement. British Educational Research Journal, 27(4), 407-432.
  • Olmez, I.B. (2020). Modeling mathematics achievement using hierarchical linear models. Elementary Education Online, 19(2), 944 957. https://doi:10.17051/ilkonline.2020.695837
  • Perry, L.B., & McConney, A. (2010). Does the SES of the school matter? An examination of socioeconomic status and student achievement using PISA 2003. Teachers College Record, 112(4), 1137-1162.
  • Preacher, K.J., & Selig, J.P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6(2), 77-98.
  • R Development Core Team (2017). R: A language and environment for statistical computing. The R foundation of statistical computing.
  • Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Sage.
  • Rjosk, C., Richter, D., Hochweber, J., Lüdtke, O., Klieme, E., & Stanat, P. (2014). Socioeconomic and language minority classroom composition and individual reading achievement: The mediating role of instructional quality. Learning and Instruction, 32, 63-72.
  • Rumberger, R. W., & Palardy, G. J. (2005). Does segregation still matter? The impact of student composition on academic achievement in high school. Teachers’College Record, 107(9), 1999-2045.
  • Schmidt, W.H., Burroughs, N.A., Zoido, P., & Houang, R.T. (2015). The role of schooling in perpetuating educational inequality: An international perspective. Educational Researcher, 44(7), 371-386.
  • Shin, J., Lee, H., & Kim, Y. (2009). Student and school factors affecting mathematics achievement: International comparisons between Korea, Japan and the USA. School Psychology International, 30(5), 520-537.
  • Sirin, S.R. (2005). Socioeconomic status and academic achievement: A meta-analysis. Review of Educational Research, 75(3), 417–453.
  • Thapa, A., Cohen, J., Guffey, S., & Higgins-D’Alessandro, A. (2013). A review of school climate research. Review of Educational Research, 83(3), 357-385.
  • Thrupp, M., Lauder, H., & Robinson, T. (2002). School composition and peer effects. International Journal of Educational Research, 37(5), 483-504.
  • Wang, Z., Osterlind, S.J., & Bergin, D.A. (2012). Building mathematics achievement models in four countries using TIMSS 2003. International Journal of Science and Mathematics Education,10(5), 1215-1242.
  • Wang, M.T., & Degol, J.L. (2016). School climate: A review of the construct, measurement, and impact on student outcomes. Educational Psychology Review, 28(2), 315-352.
  • Wu, M. (2004). Plausible values. Rasch Measurement Transactions, 18(2), 976-978.
  • Wu, J.H., Hoy, W.K., & Tarter, C.J. (2013). Enabling school structure, collective responsibility, and a culture of academic optimism. Journal of Educational Administration, 51(2), 176-193.
  • Yavuz, H.C., Demirtasli, R.N., Yalcin, S., & Dibek, M.I. (2017). The effects of student and teacher level variables on TIMSS 2007 and 2011 mathematics achievement of Turkish students. Education and Science, 42(189), 27-47.
  • Zhang, Z., Zyphur, M.J., & Preacher, K.J. (2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12(4), 695-719.
There are 58 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Özlem Albayrakoğlu 0000-0002-6234-5309

Selda Yıldırım 0000-0003-0535-4353

Publication Date March 10, 2022
Submission Date September 24, 2020
Published in Issue Year 2022 Volume: 9 Issue: 1

Cite

APA Albayrakoğlu, Ö., & Yıldırım, S. (2022). School characteristics mediating the relationship between school socioeconomic status and mathematics achievement. International Journal of Assessment Tools in Education, 9(1), 98-117.

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