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Educational Research and Evaluation
An International Journal on Theory and Practice
Volume 26, 2020 - Issue 3-4
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Articles

Exploring the complex relationship between students’ reading skills and their performance in mathematics: a population-based study

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Pages 126-149 | Received 14 Aug 2020, Accepted 28 Apr 2021, Published online: 13 May 2021

References

  • Abedi, J., & Lord, C. (2001). The language factor in mathematics tests. Applied Measurement in Education, 14(3), 219–234. https://doi.org/10.1207/S15324818AME1403_2
  • Aikens, N. L., & Barbarin, O. (2008). Socioeconomic differences in reading trajectories: The contribution of family, neighborhood, and school contexts. Journal of Educational Psychology, 100(2), 235–251. https://doi.org/10.1037/0022-0663.100.2.235
  • Ajello, A. M., Caponera, E., & Palmerio, L. (2018). Italian students’ results in the PISA mathematics test: Does reading competence matter? European Journal of Psychology of Education, 33(3), 505–520. https://doi.org/10.1007/s10212-018-0385-x
  • Arán-Filippetti, V., & Richaud de Minzi, M. C. (2012). A structural analysis of executive functions and socioeconomic status in school-age children: Cognitive factors as effect mediators. Journal of Genetic Psychology, 173(4), 393–416. https://doi.org/10.1080/00221325.2011.602374
  • Avvisati, F. (2020). The measure of socio-economic status in PISA: A review and some suggested improvements. Large-Scale Assessments in Education, 8, Article 8, 1–37. https://doi.org/10.1186/s40536-020-00086-x
  • Baird, K. (2012). Class in the classroom: The relationship between school resources and math performance among low socioeconomic status students in 19 rich countries. Education Economics, 20(5), 484–509. https://doi.org/10.1080/09645292.2010.511848
  • Bolondi, G., Branchetti, L., & Giberti, C. (2018). A quantitative methodology for analyzing the impact of the formulation of a mathematical item on students learning assessment. Studies in Educational Evaluation, 58, 37–50. https://doi.org/10.1016/j.stueduc.2018.05.002
  • Boudon, R. (1974). Education, opportunity, and social inequality: Changing prospects in Western society. Wiley.
  • Branchetti, L., & Viale, M. (2015). Tra italiano e matematica: Il ruolo della formulazione sintattica nella comprensione del testo matematico [Between Italian and mathematics: The role of syntax in students’ text comprehension]. https://rsddm.dm.unibo.it/wp-publications/2015-branchetti-3/
  • Buckingham, J., Wheldall, K., & Beaman-Wheldall, R. (2013). Why poor children are more likely to become poor readers: The school years. Australian Journal of Education, 57(3), 190–213. https://doi.org/10.1177/0004944113495500
  • Campodifiori, E., Figura E., Papini, M., & Ricci, R. (2010). Un indicatore di status socioeconomico-culturale degli allievi della quinta primaria in Italia [A socioeconomic-cultural index calculated for (primary) Grade 5 students in Italy] (INVALSI Working Paper No. 02/2010). https://www.invalsi.it/download/wp/wp02_Ricci.pdf
  • Caponera, E., Sestito, P., & Russo, P. M. (2016). The influence of reading literacy on mathematics and science achievement. The Journal of Educational Research, 109(2), 197–204. https://doi.org/10.1080/00220671.2014.936998
  • Cascella, C. (2020). Intersectional effects of Socioeconomic status, phase and gender on Mathematics achievement. Educational Studies, 46(4), 476–496. https://doi.org/10.1080/03055698.2019.1614432
  • Cascella, C., & Giberti, C. (2020). Beyond text comprehension: Exploring items’ characteristics and their effect on foreign students’ disadvantage in mathematics. International Journal of Mathematical Education in Science and Technology. Advance online publication. https://doi.org/10.1080/0020739X.2020.1836408
  • Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., & York. R. L. (1966). Equality of educational opportunity. U.S. Department of Health, Education and Welfare. https://files.eric.ed.gov/fulltext/ED012275.pdf
  • Ding, H., & Homer, M. (2020). Interpreting mathematics performance in PISA: Taking account of reading performance. International Journal of Educational Research, 102, Article 101566. https://doi.org/10.1016/j.ijer.2020.101566
  • Doerschuk, P., Bahrim, C., Daniel, J., Kruger, J., Mann, J., & Martin, C. (2016). Closing the gaps and filling the STEM pipeline: A multidisciplinary approach. Journal of Science Education and Technology, 25(4), 682–695. https://doi.org/10.1007/s10956-016-9622-8
  • Dubow, E. F., Boxer, P., & Huesmann, L. R. (2009). Long-term effects of parents’ education on children’s educational and occupational success. Merrill-Palmer Quarterly, 55(3), 224–249. https://doi.org/10.1353/mpq.0.0030
  • Education, Audiovisual and Culture Executive Agency. (2010). Gender differences in educational outcomes: Study on the measures taken and the current situation in Europe. https://doi.org/10.2797/3598
  • Falorsi, P. D., Falzetti, P., & Ricci, R. (2019). Le metodologie di campionamento e scomposizione della devianza nelle rilevazioni nazionali dell’INVALSI [Sampling strategies and methods to decompose deviance in INVALSI surveys]. FrancoAngeli.
  • Falorsi, S. (2008). Nota metodologica sulla strategia di campionamento [A methodological note about the (INVALSI) sampling strategy]. INVALSI. https://www.invalsi.it/download/INVALSI_indagine_SNV_strategia.pdf
  • Falzetti, P. (2013). Metodi di identificazione, analisi e trattamento del cheating [Method to detect, analyse, and deal with cheating]. https://www.invalsi.it/invalsi/ri/sis/documenti/022013/falzetti.pdf
  • Fowler, W. J., Jr., & Walberg, H. J. (1991). School size, characteristics, and outcomes. Educational Evaluation and Policy Analysis, 13(2), 189–202. https://doi.org/10.3102/01623737013002189
  • Friedman, L. (1989). Mathematics and the gender gap: A meta-analysis of recent studies on sex differences in mathematical tasks. Review of Educational Research, 59(2), 185–213. https://doi.org/10.3102/00346543059002185
  • Galdi, S., Cadinu, M., & Tomasetto, C. (2014). The roots of stereotype threat: When automatic associations disrupt girls’ math performance. Child Development, 85(1), 250–263. https://doi.org/10.1111/cdev.12128
  • González de San Román, A., & de la Rica, S. (2016). Gender gaps in PISA test scores: The impact of social norms and the mother’s transmission of role attitudes. Estudios de Economia Aplicada, 34(1), 79–108.
  • Grimm, K. J. (2008). Longitudinal associations between reading and mathematics achievement. Developmental Neuropsychology, 33(3), 410–426. https://doi.org/10.1080/87565640801982486
  • Guiso, L., Monte, F., Sapienza, P., & Zingales, L. (2008, May 30). Culture, gender, and math. Science, 320(5880), 11164–1165. https://doi.org/10.1126/science.1154094
  • Hox, J. J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). Routledge.
  • Hyde, J. S., Fennema, E., & Lamon, S. J. (1990). Gender differences in mathematics performance: A meta-analysis. Psychological Bulletin, 107(2), 139–155. https://doi.org/10.1037/0033-2909.107.2.139
  • Hyde, J. S., Fennema, E., Ryan, M., Frost, L. A., & Hopp, C. (1990). Gender comparisons of mathematics attitudes and affect: A meta-analysis. Psychology of Women Quarterly, 14(3), 299–324. https://doi.org/10.1111/j.1471-6402.1990.tb00022.x
  • INVALSI. (2017). Rilevazioni nazionali degli apprendimenti 2016-17: Raporto risultati (Measurement of learning outcomes. Field study 2016-17]. https://invalsi-areaprove.cineca.it/docs/file/Rapporto_Prove_INVALSI_2017.pdf
  • INVALSI. (2018). Quadro di Riferimento delle prove INVALSI di Italiano [The Italian framework for INVALSI tests to assess students’ reading literacy]. https://www.engheben.it/prof/materiali/invalsi/invalsi_seconda_superiore/2018-2019/QdR_ITALIANO.pdf
  • Ireson, G. (2017). Gender achievement and social, political and economic equality: A European perspective. Educational Studies, 43(1), 40–50. https://doi.org/10.1080/03055698.2016.1237868
  • Jimerson, S. R. (2001). Meta-analysis of grade retention research: Implications for practice in the 21st century. School Psychology Review, 30(3), 420–437. https://doi.org/10.1080/02796015.2001.12086124
  • Kessels, U. (2005). Fitting into the stereotype: How gender-stereotyped perceptions of prototypic peers relate to liking for school subjects. European Journal of Psychology of Education, 20(3), 309–323. https://doi.org/10.1007/BF03173559
  • Kintsch, W., & Greeno, J. G. (1985). Understanding and solving word arithmetic problems. Psychological Review, 92(1), 109–129. https://doi.org/10.1037/0033-295X.92.1.109
  • Kiray, S. A., Gok, B., & Bozkir, A. S. (2015). Identifying the factors affecting science and mathematics achievement using data mining methods. Journal of Education in Science, Environment and Health, 1(1), 28–48. https://www.jeseh.net/index.php/jeseh/article/view/4/64
  • Korpershoek, H., Kuyper, H., & van der Werf, G. (2015). The relationship between students’ math and reading ability and their mathematics, physics, and chemistry examination grades in secondary education. International Journal of Science and Mathematics Education, 13(5), 1013–1037. https://doi.org/10.1007/s10763-014-9534-0
  • Lamb, S., & Fullarton, S. (2002). Classroom and school factors affecting mathematics achievement: A comparative study of Australia and the United States using TIMSS. Australian Journal of Education, 46(2), 154–171. https://doi.org/10.1177/000494410204600205
  • Larzelere, R. E., & Patterson, G. R. (1990). Parental management: Mediator of the effect of socioeconomic status on early delinquency. Criminology, 28(2), 301–324. https://doi.org/10.1111/j.1745-9125.1990.tb01327.x
  • Legewie, J., & DiPrete, T. A. (2012). School context and the gender gap in educational achievement. American Sociological Review, 77(3), 463–485. https://doi.org/10.1177/0003122412440802
  • Lindberg, S. M., Hyde, J. S., Petersen, J. L., & Linn, M. C. (2010). New trends in gender and mathematics performance: A meta-analysis. Psychological Bulletin, 136(6), 1123–1135. https://doi.org/10.1037/a0021276
  • Logan, S., & Johnston, R. (2010). Investigating gender differences in reading. Educational Review, 62(2), 175–187. https://doi.org/10.1080/00131911003637006
  • Longobardi, S., Falzetti, P., & Pagliuca, M. M. (2018). Quis custiodet ipsos custodes? How to detect and correct teacher cheating in Italian student data. Statistical Methods & Applications, 27(3), 515–543. https://doi.org/10.1007/s10260-018-0426-2
  • Lucifora, C., & Tonello, M. (2015). Cheating and social interactions. Evidence from a randomized experiment in a national evaluation program. Journal of Economic Behavior & Organization, 115, 45–66. https://doi.org/10.1016/j.jebo.2014.12.006
  • Maccoby, E. E., & Jacklin, C. N. (1974). The psychology of sex differences. Stanford University Press.
  • Marks, G. N. (2008). Accounting for the gender gaps in student performance in reading and mathematics: Evidence from 31 countries. Oxford Review of Education, 34(1), 89–109. https://doi.org/10.1080/03054980701565279
  • Matthews, K. A., Gallo, L. C., & Taylor, S. E. (2010). Are psychosocial factors mediators of socioeconomic status and health connections? A progress report and blueprint for the future. Annals of the New York Academy of Sciences, 1186(1), 146–173. https://doi.org/10.1111/j.1749-6632.2009.05332.x
  • Meinck, S., & Brese, F. (2019). Trends in gender gaps: Using 20 years of evidence from TIMSS. Large-Scale Assessments in Education, 7(8), 1–23. https://doi.org/10.1186/s40536-019-0076-3
  • Morgan, P. L., Farkas, G., Hillemeier, M. M., & MacZuga, S. (2009). Risk factors for learning-related behavior problems at 24 months of age: Population-based estimates. Journal of Abnormal Child Psychology, 37(3), 401–413. https://doi.org/10.1007/s10802-008-9279-8
  • Murray, T. C., Rodgers, W. M., & Fraser, S. N. (2012). Exploring the relationship between socioeconomic status, control beliefs and exercise behavior: A multiple mediator model. Journal of Behavioral Medicine, 35(1), 63–73. https://doi.org/10.1007/s10865-011-9327-7
  • Nasser, F., & Birenbaum, M. (2005). Modeling mathematics achievement of Jewish and Arab eighth graders in Israel: The effects of learner-related variables. Educational Research and Evaluation, 11(3), 277–302. https://doi.org/10.1080/13803610500101108
  • Nollenberger, N., Rodríguez-Planas, N., & Sevilla, A. (2016). The math gender gap: The role of culture. American Economic Review, 106(5), 257–261. https://doi.org/10.1257/aer.p20161121
  • Organisation for Economic Co-operation and Development. (2009). PISA 2009 assessment framework: Key competencies in reading, mathematics and science. https://doi.org/10.1787/9789264062658-en
  • Organisation for Economic Co-operation and Development. (2015a). The ABC of gender equality in education: Aptitude, behaviour, confidence. https://doi.org/10.1787/9789264229945-en
  • Organisation for Economic Co-operation and Development. (2015b). Can the performance gap between immigrant and non-immigrant students be closed (PISA in Focus No. 53). https://doi.org/10.1787/5jrxqs8mv327-en
  • Organisation for Economic Co-operation and Development. (2016). PISA 2015 results (Volume 1): Excellence and equity in education. https://doi.org/10.1787/9789264266490-en
  • Pampaka, M., Williams, J., Hutcheson, G., Black, L., Davis, P., Hernandez-Martinez, P., & Wake, G. (2013). Measuring alternative learning outcomes: Dispositions to study in higher education. Journal of Applied Measurement, 14(2), 197–218.
  • Passolunghi, M. C., Vercelloni, B., & Schadee, H. (2007). The precursors of mathematics learning: Working memory, phonological ability and numerical competence. Cognitive Development, 22(2), 165–184. https://doi.org/10.1016/j.cogdev.2006.09.001
  • Praet, M., Titeca, D., Ceulemans, A., & Desoete, A. (2013). Language in the prediction of arithmetics in kindergarten and grade 1. Learning and Individual Differences, 27, 90–96. https://doi.org/10.1016/j.lindif.2013.07.003
  • Purpura, D. J., Hume, L. E., Sims, D. M., & Lonigan, C. J. (2011). Early literacy and early numeracy: The value of including early literacy skills in the prediction of numeracy development. Journal of Experimental Child Psychology, 110(4), 647–658. https://doi.org/10.1016/j.jecp.2011.07.004
  • Quintano, C., Castellano, R., & Longobardi, S. (2009). A fuzzy clustering approach to improve the accuracy of Italian student data: An experimental procedure to correct the impact of outliers on assessment test scores. Statistica & Applicazioni, 7(2), 149–171. https://doi.org/10.1400/209647
  • Radovic, D., Black, L., Williams, J., & Salas, C. E. (2018). Towards conceptual coherence in the research on mathematics learner identity: A systematic review of the literature. Educational Studies in Mathematics, 99(1), 21–42. https://doi.org/10.1007/s10649-018-9819-2
  • Rasbash, J., Steele, F., Browne, W. J., & Goldenstein, H. (2017). A user ‘s guide to MLwiN (Version 3.01). Centre for Multilevel Modelling, University of Bristol. http://www.bristol.ac.uk/cmm/media/software/mlwin/downloads/manuals/3-01/manual-web.pdf
  • Rasch, G. (1960). Probabilistic models for some intelligence and achievement tests. Danish Institute for Educational Research.
  • Reardon, S. F., Valentino, R. A., Kalogrides, D., Shores, K. A., Greenberg, E. H. (2013). Patterns and trends in racial academic achievement gaps among states, 1999–2011. https://cepa.stanford.edu/sites/default/files/reardon%20et%20al%20state%20achievement%20gaps%20aug2013.pdf
  • Reardon, S. F., Yun, J. T., & Kurlaender, M. (2006). Implications of income-based school assignment policies for racial school segregation. Educational Evaluation and Policy Analysis, 28(1), 49–75. https://doi.org/10.3102/01623737028001049
  • 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. https://doi.org/10.1177/0143034309107070
  • Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. https://doi.org/10.3102/00346543075003417
  • Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype threat and women’s math performance. Journal of Experimental Social Psychology, 35(1), 4–28. https://doi.org/10.1006/jesp.1998.1373
  • Steele, C. M., & Aronson, J. (1995). Stereotype threat and intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69(5), 797–811. https://doi.org/10.1037/0022-3514.69.5.797
  • Stoet, G., & Geary, D. C. (2013). Sex differences in mathematics and reading achievement are inversely related: Within- and across-nation assessment of 10 years of PISA data. PLoS ONE, 8(3), Article e57988. https://doi.org/10.1371/journal.pone.0057988
  • Stoet, G., & Geary, D. C. (2015). Sex differences in academic achievement are not related to political, economic, or social equality. Intelligence, 48, 137–151. https://doi.org/10.1016/j.intell.2014.11.006
  • Stoet, G., & Geary, D. C. (2018). The gender-equality paradox in science, technology, engineering, and mathematics education. Psychological Science, 29(4), 581–593. https://doi.org/10.1177/0956797617741719
  • Strand, S. (2014a). Ethnicity, gender, social class and achievement gaps at age 16: Intersectionality and “getting it” for the white working class. Research Papers in Education, 29(2), 131–171. https://doi.org/10.1080/02671522.2013.767370
  • Strand, S. (2014b). School effects and ethnic, gender and socio-economic gaps in educational achievement at age 11. Oxford Review of Education, 40(2), 223–245. https://doi.org/10.1080/03054985.2014.891980
  • Troncoso, P., Pampaka, M., & Olsen, W. (2016). Beyond traditional school value-added models: A multilevel analysis of complex school effects in Chile. School Effectiveness and School Improvement, 27(3), 293–314. https://doi.org/10.1080/09243453.2015.1084010
  • van Bergen, E., van Zuijen, T., Bishop, D., & de Jong, P. F. (2017). Why are home literacy environment and children’s reading skills associated? What parental skills reveal. Reading Research Quarterly, 52(2), 147–160. https://doi.org/10.1002/rrq.160
  • van Ewijk, R., & Sleegers, P. (2009). Peer ethnicity and achievement: A meta-analysis into the compositional effect. SSRN, 3453. https://doi.org/10.2139/ssrn.1402651
  • van Ewijk, R., & Sleegers, P. (2010). The effect of peer socioeconomic status on student achievement: A meta-analysis. Educational Research Review, 5(2), 134–150. https://doi.org/10.1016/j.edurev.2010.02.001
  • Vukovic, R. K., & Lesaux, N. K. (2013). The relationship between linguistic skills and arithmetic knowledge. Learning and Individual Differences, 23, 87–91. https://doi.org/10.1016/j.lindif.2012.10.007
  • Walker, C. M., Zhang, B., & Surber, J. (2008). Using a multidimensional differential item functioning framework to determine if reading ability affects student performance in mathematics. Applied Measurement in Education, 21(2), 162–181. https://doi.org/10.1080/08957340801926201
  • White, K. R. (1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91(3), 461–481. https://doi.org/10.1037/0033-2909.91.3.461

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