Skip to main content

Advertisement

Log in

Gender differences in mathematical achievement development: a family psychobiosocial model

  • Published:
European Journal of Psychology of Education Aims and scope Submit manuscript

Abstract

This study proposes a family psychobiosocial model on gender differences in cognitive development. Specifically, the aim is to investigate how family biological, socioeconomic, and psychological factors predict child mathematics achievement (MAch) development. The data were obtained from the Millennium Cohort Study. Children’s pattern construction scores collected at ages 5 and 7 years worked as MAch (n = 18,497). The predictors were family data collected when the children were 9 months. The results of path analyses for all students indicate that all three factors in the family psychobiosocial model play some roles in children’s MAch development. Analyses for the female and male students separately reveal that girls’ positive MAch development was significantly predicted by four psychobiosocial factors (fewer mother in-pregnancy alcohol intakes, more family income, higher mother education levels, and more mother cognitive stimulation); boys’ MAch development is predicted by only one factor (higher mother education levels). The results support the psychobiosocial model as a whole. Family psychobiosocial factors, especially social factors, impact children’s cognitive development more for females than for males.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Data Availability

The initial datasets are available to researchers upon request to the U.K. Data Service.

References

  • Chiu, M. -S. (2016). Using demographics to predict mathematics achievement development and academic ability and job income expectations. Open Journal of Social Sciences, 4, 103–107. https://doi.org/10.4236/jss.2016.47017

  • Chiu, M. -S. (2018). Effects of early numeracy activities on mathematics achievement and affect: Parental value and child gender conditions and socioeconomic status mediation. EURASIA Journal of Mathematics, Science and Technology Education, 14(12), em1634. https://doi.org/10.29333/ejmste/97191

  • Chiu, M. -S. (2020). Exploring models for increasing the effects of school information and communication technology use on learning outcomes through outside-school use and socioeconomic status mediation: The Ecological Techno-Process. Educational Technology Research and Development, 68, 413–436. https://doi.org/10.1007/s11423-019-09707-x

  • Chiu, M. -S. (2022). Transcend socioeconomic status constraints to mathematics and science achievement by collaborative problem-solving: The female people-smartness hypothesis. Frontiers in Psychology, 13, 944329. https://doi.org/10.3389/fpsyg.2022.944329

  • Baye, A., & Monseur, C. (2016). Gender differences in variability and extreme scores in an international context. Large-Scale Assessments in Education, 4(1), 1–16. https://doi.org/10.1186/s40536-015-0015-x

    Article  Google Scholar 

  • Bodovski, K., & Youn, M. J. (2010). Love, discipline and elementary school achievement: The role of family emotional climate. Social Science Research, 39, 585–595. https://doi.org/10.1016/j.ssresearch.2010.03.008

    Article  Google Scholar 

  • Bollen, K. A., & Long, J. S. (1993). Testing structural equation models. Sage.

    Google Scholar 

  • Booth-LaForce, C., & Oxford, M. L. (2008). Trajectories of social withdrawal from grades 1 to 6: Prediction from early parenting, attachment, and temperament. Developmental Psychology, 44, 1298–1313. https://doi.org/10.1037/a0012954

    Article  Google Scholar 

  • Bronfenbrenner, U. (1986). Ecology of the family as a context for human development: Research perspectives. Developmental Psychology, 22, 723–742. https://doi.org/10.1037/0012-1649.22.6.723

    Article  Google Scholar 

  • Bronfenbrenner, U. (1994). Ecological models of human development. International Encyclopedia of Education, 3(2), 37–43.

    Google Scholar 

  • Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nurture reconceptualized in developmental perspective: A bioecological model. Psychological Review, 101, 568–586. https://doi.org/10.1037/0033-295X.101.4.568

    Article  Google Scholar 

  • Brumariu, L. E., Kerns, K. A., & Seibert, A. (2012). Mother–child attachment, emotion regulation, and anxiety symptoms in middle childhood. Personal Relationships, 19, 569–585. https://doi.org/10.1111/j.1475-6811.2011.01379.x

    Article  Google Scholar 

  • Castelao, C. F., & Kröner-Herwig, B. (2013). Different trajectories of depressive symptoms in children and adolescents: Predictors and differences in girls and boys. Journal of Youth and Adolescence, 42, 1169–1182. https://doi.org/10.1007/s10964-012-9858-4

    Article  Google Scholar 

  • Chen, P. P. (2003). Exploring the accuracy and predictability of the self-efficacy beliefs of seventh-grade mathematics students. Learning and Individual Differences, 14, 77–90. https://doi.org/10.1016/j.lindif.2003.08.003

    Article  Google Scholar 

  • Chen, H., Chen, M. F., Chang, T. S., Lee, Y. S., & Chen, H. P. (2010). Gender reality on multi-domains of school-age children in Taiwan: A developmental approach. Personality and Individual Differences, 48, 475–480. https://doi.org/10.1016/j.paid.2009.11.027

    Article  Google Scholar 

  • Dickerson, A., & Popli, G. K. (2016). Persistent poverty and children’s cognitive development: Evidence from the UK Millennium Cohort Study. Journal of the Royal Statistical Society: Series A (statistics in Society), 179(2), 535–558. https://doi.org/10.1111/rssa.12128

    Article  Google Scholar 

  • Duchesne, S., & Ratelle, C. (2010). Parental behaviors and adolescents’ achievement goals at the beginning of middle school: Emotional problems as potential mediators. Journal of Educational Psychology, 102, 497–507. https://doi.org/10.1037/a0019320

    Article  Google Scholar 

  • Duncan, G. J., Morris, P. A., & Rodrigues, C. (2011). Does money really matter? Estimating impacts of family income on young children’s achievement with data from random-assignment experiments. Developmental Psychology, 47, 1263–1279. https://doi.org/10.1037/a0023875

    Article  Google Scholar 

  • Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-national patterns of gender differences in mathematics: A meta-analysis. Psychological Bulletin, 136, 103–127. https://doi.org/10.1037/a0018053

    Article  Google Scholar 

  • Evans, J., Melotti, R., Heron, J., Ramchandani, P., Wiles, N., Murray, L., & Stein, A. (2012). The timing of maternal depressive symptoms and child cognitive development: A longitudinal study. Journal of Child Psychology and Psychiatry, 53, 632–640. https://doi.org/10.1111/j.1469-7610.2011.02513.x

    Article  Google Scholar 

  • Filho, E. (2020). Shared zones of optimal functioning: A framework to capture peak performance, momentum, psycho–bio–social synchrony, and leader–follower dynamics in teams. Journal of Clinical Sport Psychology, 14, 330–358.

    Article  Google Scholar 

  • Flak, A. L., Su, S., Bertrand, J., Denny, C. H., Kesmodel, U. S., & Cogswell, M. E. (2014). The association of mild, moderate, and binge prenatal alcohol exposure and child neuropsychological outcomes: A meta-analysis. Alcoholism: Clinical and Experimental Research, 38, 214–226. https://doi.org/10.1111/acer.12214

    Article  Google Scholar 

  • Frazier, L. D. (2020). The past, present, and future of the biopsychosocial model: A review of the biopsychosocial model of health and disease: New philosophical and scientific developments by Derek Bolton and Grant Gillett. New Ideas in Psychology, 57, 100755.

    Article  Google Scholar 

  • Gray, H., Lyth, A., McKenna, C., Stothard, S., Tymms, P., & Copping, L. T. (2019). Sex differences in variability across nations in reading, mathematics and science: A meta-analytic extension of Baye and Monseur (2016). Large-Scale Assessments in Education, 7(2), 1–29. https://doi.org/10.1186/s40536-019-0070-9

    Article  Google Scholar 

  • Guiso, L., Monte, F., Sapienza, P., & Zingales, L. (2008). Culture, gender, and mathematics. Science, 320, 1164–1165. https://doi.org/10.1126/science.1154094

    Article  Google Scholar 

  • Hair, J. F., Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Prentice-Hall.

    Google Scholar 

  • Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson Education.

    Google Scholar 

  • Halpern, D. F., Wai, J., & Saw, A. (2005). A psychobiosocial model: Why females are sometimes greater than and sometimes less than males in math achievement. In J. Kaufman & A. Gallagher (Eds.), Gender differences in mathematics: An integrative psychological approach (pp. 48–72). Cambridge University Press.

    Google Scholar 

  • Hansen, K. (Ed.). (2014). Millennium cohort study: A guide to the datasets (8th ed.). UK: Centre for Longitudinal Studies.

    Google Scholar 

  • Howard, M. C. (2016). A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve? International Journal of Human-Computer Interaction, 32(1), 51–62. https://doi.org/10.1080/10447318.2015.1087664

    Article  Google Scholar 

  • Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60, 581–592. https://doi.org/10.1037/0003-066X.60.6.581

    Article  Google Scholar 

  • Hyde, J. S. (2014). Gender similarities and differences. Annual Review of Psychology, 65, 373–398. https://doi.org/10.1146/annurev-psych-010213-115057

    Article  Google Scholar 

  • Hyde, J. S., & Mertz, J. E. (2009). Gender, culture, and mathematics performance. Proceedings of the National Academy of Sciences, 106, 8801–8807. https://doi.org/10.1073/pnas.0901265106

    Article  Google Scholar 

  • Jeynes, W. (2012). A meta-analysis of the efficacy of different types of parental involvement programs for urban students. Urban Education, 47, 706–742. https://doi.org/10.1177/0042085912445643

    Article  Google Scholar 

  • Jones, E. M., & Ketende, S. C. (2010). Millennium cohort study: User guide to analyzing MCS data using SPSS. London, UK: Centre for Longitudinal Studies.

    Google Scholar 

  • Kelly, Y., Iacovou, M., Quigley, M. A., Gray, R., Wolke, D., Kelly, J., & Sacker, A. (2013). Light drinking versus abstinence in pregnancy–behavioral and cognitive outcomes in 7-year-old children: A longitudinal cohort study. BJOG: An International Journal of Obstetrics & Gynaecology, 120, 1340–1347. https://doi.org/10.1111/1471-0528.12246

    Article  Google Scholar 

  • Leavitt, C. E., Siedel, A. J., Yorgason, J. B., Millett, M. A., & Olsen, J. (2021). Little things mean a lot: Using the biopsychosocial model for daily reports of sexual intimacy. Journal of Social and Personal Relationships, 38(3), 1066–1084. https://doi.org/10.1177/0265407520977665

  • Lyubomirsky, S., King, L., & Diener, E. (2005). The benefits of frequent positive affect: Does happiness lead to success? Psychological Bulletin, 131, 803–855. https://doi.org/10.1037/0033-2909.131.6.803

    Article  Google Scholar 

  • Martin, A. J., Liem, G. A., Mok, M., & Xu, J. (2012). Problem solving and immigrant student mathematics and science achievement: Multination findings from the Program for International Student Assessment (PISA). Journal of Educational Psychology, 104, 1054–1073. https://doi.org/10.1037/a0029152

    Article  Google Scholar 

  • McCarthy, B., Koman, C. A., & Cohn, D. (2018). A psychobiosocial model for assessment, treatment, and relapse prevention for female sexual interest/arousal disorder. Sexual and Relationship Therapy, 33, 353–363.

    Article  Google Scholar 

  • McCulloch, A., & Joshi, H. E. (2001). Neighborhood and family influences on the cognitive ability of children in the British National Child Development Study. Social Science & Medicine, 53, 579–591. https://doi.org/10.1016/S0277-9536(00)00362-2

    Article  Google Scholar 

  • Morin, A. J., Rodriguez, D., Fallu, J. S., Maïano, C., & Janosz, M. (2012). Academic achievement and smoking initiation in adolescence: A general growth mixture analysis. Addiction, 107, 819–828. https://doi.org/10.1111/j.1360-0443.2011.03725.x

    Article  Google Scholar 

  • Mullis, I. V. S., Martin, M. O., Foy, P., & Hooper, M. (2016). TIMSS advanced 2015 international results in advanced mathematics and physics. Retrieved on September 10, 2022, from Boston College, TIMSS & PIRLS International Study Center website: http://timssandpirls.bc.edu/timss2015/international-results/advanced/

  • Nykjaer, C., Alwan, N. A., Greenwood, D. C., Simpson, N. A., Hay, A. W., White, K. L., & Cade, J. E. (2014). Maternal alcohol intake prior to and during pregnancy and risk of adverse birth outcomes: Evidence from a British cohort. Journal of Epidemiology and Community Health 68(6), 524–549. https://doi.org/10.1136/jech-2013-202934

  • Organization for Economic Co-operation and Development. (2014). PISA 2012 results: What students know and can do – student performance in mathematics, reading and science (Volume I, Revised edition, February 2014). Paris, France: Author. Retrieved from https://doi.org/10.1787/9789264201118-en

  • Oxford, M. L., & Lee, J. O. (2011). The effect of family processes on school achievement as moderated by socioeconomic context. Journal of School Psychology, 49, 597–612. https://doi.org/10.1016/j.jsp.2011.06.001

    Article  Google Scholar 

  • Pargulski, J. R., & Reynolds, M. R. (2017). Sex differences in achievement: Distributions matter. Personality and Individual Differences, 104, 272–278. https://doi.org/10.1016/j.paid.2016.08.016

    Article  Google Scholar 

  • Raykov, T., Lee, C. L., Marcoulides, G. A., & Chang, C. (2013). A commentary on the relationship between model fit and saturated path models in structural equation modeling applications. Educational and Psychological Measurement, 73(6), 1054–1068. https://doi.org/10.1177/0013164413487905

    Article  Google Scholar 

  • Raykov, T., Marcoulides, G. A., & Akaeze, H. O. (2017). Comparing between- and within-group variances in a two-level study: A latent variable modeling approach to evaluating their relationship. Educational and Psychological Measurement, 77, 351–361. https://doi.org/10.1177/0013164416634166

    Article  Google Scholar 

  • Rees, G. (2018). The association of childhood factors with children’s subjective well-being and emotional and behavioral difficulties at 11 years old. Child Indicators Research, 11, 1107–1129. https://doi.org/10.1007/s12187-017-9479-2

    Article  Google Scholar 

  • Simmons, B. L., Gooty, J., Nelson, D. L., & Little, L. M. (2009). Secure attachment: Implications for hope, trust, burnout, and performance. Journal of Organizational Behavior, 30, 233–247. https://doi.org/10.1002/job.585

    Article  Google Scholar 

  • Slominski, L., Sameroff, A., Rosenblum, K., & Kasser, T. (2011). Longitudinal predictors of adult socioeconomic attainment: The roles of socioeconomic status, academic competence, and mental health. Development and Psychopathology, 23, 315–324. https://doi.org/10.1017/S0954579410000829

    Article  Google Scholar 

  • Su, Y., Doerr, H. S., Johnson, W., Shi, J., & Spinath, F. M. (2015). The role of parental control in predicting school achievement independent of intelligence. Learning and Individual Differences, 37, 203–209. https://doi.org/10.1016/j.lindif.2014.11.023

    Article  Google Scholar 

  • Taylor, R. (1990). Interpretation of the correlation coefficient: A basic review. Journal of Diagnostic Medical Sonography, 6, 35–39. https://doi.org/10.1177/875647939000600106

    Article  Google Scholar 

  • Watson, J. C. (2017). Establishing evidence for internal structure using exploratory factor analysis. Measurement and Evaluation in Counseling and Development, 50(4), 232–238.

    Article  Google Scholar 

  • West, K. K., Mathews, B. L., & Kerns, K. A. (2013). Mother–child attachment and cognitive performance in middle childhood: An examination of mediating mechanisms. Early Childhood Research Quarterly, 28, 259–270. https://doi.org/10.1016/j.ecresq.2012.07.005

    Article  Google Scholar 

  • Willis, J. O., Elliott, C. D., & Dumont, R. (2008). Essentials of Das-ii assessment. John Wiley & Sons.

    Google Scholar 

  • Ximénez, C., Maydeu-Olivares, A., Shi, D., & Revuelta, J. (2022). Assessing cutoff values of SEM fit indices: Advantages of the unbiased SRMR index and its cutoff criterion based on communality. Structural Equation Modeling: A Multidisciplinary Journal, 29(3), 368–380. https://doi.org/10.1080/10705511.2021.1992596

    Article  Google Scholar 

Download references

Funding

This work was supported by the Ministry of Science and Technology, Taiwan (NSTC 110–2511-H-004–001-MY3). The funder only provides financial support and does not substantially influence the entire research process, from study design to submission. The authors are fully responsible for the content of the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mei-Shiu Chiu.

Ethics declarations

Conflicting interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Prof. Dr. Mei-Shiu Chiu. She is currently a professor of education at National Chengchi University, Taiwan. She received a B. A. and an M. A. Degree in Education from National Taiwan Normal University and completed her doctoral study at the Faculty of Education, Cambridge University, U.K.

Current themes of research:

Her primary interest is affective education. Research topics focus on the design, implementation, and effectiveness evaluation of learning, teaching, and assessment in a variety of areas of knowledge (e.g., mathematics, science, and energy); interactions between emotions, cognition, and culture; and multiple research methods and data analysis methods (including educational and data science methods). She has developed several research-based educational theories, relevant assessment tools, as well as school and teacher development courses for educational and research practices.

Relevant publications:

Chiu, M.-S. (2022). Transcend socioeconomic status constraints to mathematics and science achievement by collaborative problem-solving: The female people-smartness hypothesis. Frontiers in Psychology, 13, 944,329. 10.3389/fpsyg.2022.944329.

Chiu, M.-S. (2021). Gender differences in effects of father/mother parenting on mathematics achievement growth: A bioecological model of human development. European Journal of Psychology of Education, 36(3), 827–844. 10.1007/s10212-020–00,506-0.

Chiu, M.-S. (2021). An ecological approach to adolescent mathematics ability development: Differences in demographics, parenting, mathematics teaching, and student behaviors and emotions. Educational Studies, 47(2), 155–178. 10.1080/03055698.2019.1672522.

Chiu, M.-S., Xiong, W., and Kuan, P.-Y. (2021). Graduates’ career success predicted by mathematical and affective abilities, effective higher-education learning and economic contexts: A bioecological positivity to success model. Journal of Education and Work, 34(3), 313–330. 10.1080/13639080.2021.1931668.

Chiu, M.-S. (2020). Exploring models for increasing the effects of school information and communication technology use on learning outcomes through outside-school use and socioeconomic status mediation: The ecological techno-process. Educational Technology Research and Development, 68, 413–436. 10.1007/s11423-019–09,707-x.

Chiu, M.-S. (2018). Effects of early numeracy activities on mathematics achievement and affect: Parental value and child gender conditions and socioeconomic status mediation EURASIA Journal of Mathematics, Science and Technology Education, 14(12), em1634. 10.29333/ejmste/97191.

Chiu, M.-S. (2016). Using demographics to predict mathematics achievement development and academic ability and job income expectations Open Journal of Social Sciences, 4, 103–107. 10.4236/jss.2016.47017.

Current themes of research:

Population health sciences using large-scale medical datasets.

Relevant publications:

Risk of dementia associated with body mass index, changes in body weight and waist circumference in older people with type 2 diabetes: The Edinburgh Type 2 Diabetes Study (in submission).

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 26 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chiu, MS. Gender differences in mathematical achievement development: a family psychobiosocial model. Eur J Psychol Educ 38, 1481–1504 (2023). https://doi.org/10.1007/s10212-022-00674-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10212-022-00674-1

Keywords

Navigation