The Effects of sexism against women on men's self-efficacy and performance in Mathematics: structural equation models from the theory of ambivalent sexism

Keywords: Sexism, Mathematics self-efficacy, Math Tests, Structural equations models, Bayesian statistics


[Objective] This study is intended to explain the performance in mathematics tests of men at the high school and college levels, majoring in Social Sciences, Humanities and STEM, using a SEM model based on the theory of ambivalent sexism towards women. [Methodology] Data was obtained from high school boys in urban areas (2015), as well as college men majoring in the careers mentioned before. A structural equation model was estimated using maximum likelihood and generalized least squares estimation methods. Given non-compliance with the assumptions, estimates were made using Bayesian statistical methods. Finally, goodness-of-fit measures were evaluated. [Results] In the three groups studied, the relationships matched initial expectations. For high school boys, the relationship between hostile sexism and perceived equality in mathematics was not significant (coefficient: -0.02). In the case of college men majoring in Social Sciences and Humanities, the relationship between benevolent sexism and perceived equality in mathematics was also not significant (coefficient: 0.00). In the three cases, the higher the perception of equality, the higher the level of self-efficacy of the male students, which generates better performance in mathematical tests. Likewise, the higher the level of reasoning skills of the students, the higher their levels of self-efficacy. [Conclusions] Sexist ideologies negatively influence the perception of equality in mathematical contexts. Higher levels of perception of equality are related to higher levels of performance in Mathematics tests. The importance of reasoning skills in mathematical contexts was shown by the fact that all the estimated models showed such skills to be highly positively related to mathematical test results.


Ayotola, A., & Adedeji, T. (2009). The relationship between mathematics self-efficacy and achievement in mathematics. Procedia - Social and Behavioral Sciences, 1(1), 953–957.
Bandura, A. & Adams, N. E. (1977). Analysis of self-efficacy theory of behavioral change. Cognitive Therapy and Research, 1(4), 287–310.
Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44(9), 1175–1184.
Bayes, T. (1763). LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S. Philosophical Transactions of the Royal Society of London, 53, 370–418.
Bengtsson, H. (2021). A unifying framework for parallel and distributed processing in r using futures. The R Journal, 13(2), 208–227.
Birnberg, J. G. (1964). Bayesian Statistics: A Review. Journal of Accounting Research, 2(1), 108–116.
Brooks, S. P., & Gelman, A. (1998). General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics, 7(4), 434–455.
Brown, C. S. & Leaper, C. (2010). Latina and European American Girls’ Experiences with Academic Sexism and their Self-Concepts in Mathematics and Science During Adolescence. Sex Roles, 63(11), 860–870.
Casad, B. J., Franks, J. E., Garasky, C. E., Kittleman, M. M., Roesler, A. C., Hall, D. Y., & Petzel, Z. W. (2021). Gender inequality in academia: Problems and solutions for women faculty in STEM. Journal of Neuroscience Research, 99(1), 13–23.
Cheung, S. F. & Lai, M. H. C. (2021). Semptools: Customizing structural equation modelling plots.
Cliff, A., & Montero, E. (2010). El Balance entre Excelencia y Equidad en Pruebas de Admisión: Contribuciones de Experiencias en Sudáfrica y Costa Rica. Revista Iberoamericana De Evaluación Educativa, 3(2).
Drury, B. J., & Kaiser, C. R. (2014). Allies against Sexism: The Role of Men in Confronting Sexism. Journal of Social Issues, 70(4), 637–652.
Epskamp, S. (2022). semPlot: Path diagrams and visual analysis of various SEM packages’ output.
Fennema, E., & Sherman, J. A. (1976). Fennema-Sherman Mathematics Attitudes Scales: Instruments designed to measure attitudes toward the learning of mathematics by females and males. Journal for Research in Mathematics Education, 7(5), 324-326.
Forgasz, H. J., Leder, G. C., & Gardner, P. L. (1999). The Fennema-Sherman Mathematics as a Male Domain Scale Reexamined. Journal for Research in Mathematics Education, 30(3), 342.
Garaigordobil, M., Aliri, J. (2011). Sexismo hostil y benevolente relaciones con el autoconcepto, el racismo y la sensibilidad intercultural. Universidad del País Vasco. Revista de psicodidáctica, 16(2), 331-350.
Glick, P. & Fiske, S. T. (1996). The Ambivalent Sexism Inventory: Differentiating Hostile and Benevolent Sexism. Journal of Personality and Social Psychology, 70(450), 491–512.
Glick, P. & Fiske, S. T. (1999). The ambivalence toward men inventory: Differentiating Hostile and Benevolent Beliefs about Men. Psychology of Women Quarterly, 23(3), 519–536.
Glick, P., & Whitehead, J. (2010). Hostility toward men and the perceived stability of male dominance. Social Psychology, 41(3), 177–185.
Glick, P., Lameiras, M., Fiske, S. T., Eckes, T., Masser, B., Volpato, C., Manganelli, A. M., Pek, J. C. X., Huang, L., Sakalli-Uğurlu, N., Castro, Y. R., D’Avila Pereira, M. L., Willemsen, T. M., Brunner, A., Six-Materna, I., & Wells, R. (2004). Bad but Bold: Ambivalent Attitudes Toward Men Predict Gender Inequality in 16 Nations. Journal of Personality and Social Psychology, 86(5), 713–728.
Goldberger, A. S. (1972). Maximum-Likelihood Estimation of Regressions Containing Unobservable Independent Variables. International Economic Review, 13(1), 1.
Graham, S. (1994). Motivation in African Americans. Review of Educational Research, 64(1), 55.
Heywood, H. B. (1931). On finite sequences of real numbers. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 134(824), 486–501.
Hoofs, H., van de Schoot, R., Jansen, N. W. H., & Kant, Ij. (2017). Evaluating Model Fit in Bayesian Confirmatory Factor Analysis with Large Samples: Simulation Study Introducing the BRMSEA. Educational and Psychological Measurement, 78(4), 537–568.
Hoyle, R. H. (Ed.). (2012). Handbook of structural equation modeling. The Guilford Press.
Kaplan, D. & Depaoli, S. (2012). Bayesian Structural Equation Modeling. En R. H. Doyle (Ed.), Handbook of Structural Equation Modeling (pp. 650–673).
Lent, R. W., Lopez, F. G., & Bieschke, K. J. (1991). Mathematics self-efficacy: Sources and relation to science-based career choice. Journal of Counseling Psychology, 38(4), 424–430.
Lunn, D., Jackson, C., Best, N., Thomas, A., & Spiegelhalter, D. (2012). The BUGS Book. Chapman and Hall/CRC.
Mena Castillo, J. P. (2015). Desarrollo en la prueba nacional de bachillerato de Matemática: Una necesidad. Cuadernos de Investigación y Formación en Educación Matemática, 10 (13), 53-66.
Merkle, E. C., & Rosseel, Y. (2018). Blavaan: Bayesian Structural Equation Models via Parameter Expansion. Journal of Statistical Software, 85(4).
Meyer, D. L. (1966). Bayesian Statistics. Review of Educational Research, 36(5), 503–516.
Montero-Rojas, E., Castelain, T., Moreira, T. E., Alfaro-Rojas, L., Cerdas-Núñez, D., García-Segura, A., & Roldán-Villalobos, M. G. (2013). Evidencias iniciales de validez de criterio de los resultados de una Prueba de razonamiento con figuras para la selección de estudiantes indígenas para la Universidad de Costa Rica y el Instituto Tecnológico de Costa Rica. Revista Educación, 37(2), 103-117.
Nosek, B. A., Smyth, F. L., Sriram, N., Lindner, N. M., Devos, T., Ayala, A., Bar-Anan, Y., Bergh, R., Cai, H., Gonsalkorale, K., Kesebir, S., Maliszewski, N., Neto, F., Olli, E., Park, J., Schnabel, K., Shiomura, K., Tulbure, B. T., Wiers, R. W., … Greenwald, A. G. (2009). National differences in gender–science stereotypes predict national sex differences in science and math achievement. Proceedings of the National Academy of Sciences, 106(26), 10593–10597.
Plummer, M., Stukalov, A., Denwood, M. (2022). Rjags: Bayesian Graphical Models using MCMC_. R package version 4-13,
R Core Team. (2022a). Foreign: Read data stored by ‘minitab’, ‘s’, ‘SAS’, ‘SPSS’, ‘stata’, ‘systat’, ‘weka’, ‘dBase’, ...
R Core Team. (2022b). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Raftery, A. E. & Lewis, S. M. (1992). [Practical Markov Chain Monte Carlo]: Comment: One Long Run with Diagnostics: Implementation Strategies for Markov Chain Monte Carlo. Statistical Science, 7(4).
Rojas Pedregosa, P. & Moreno Díaz, R. (2016). Sexismo hostil y benevolente en adolescentes. Una aproximación étnico-cultural. Revista Iberoamericana de Educación, 72(1).
Rossi, S., Xenidou‐Dervou, I., Simsek, E., Artemenko, C., Daroczy, G., Nuerk, H., & Cipora, K. (2022). Mathematics–gender stereotype endorsement influences mathematics anxiety, self‐concept, and performance differently in men and women. Annals of the New York Academy of Sciences, 1513(1), 121–139.
RStudio Team. (2022). RStudio: Integrated Development Environment for R.
Rubin, D. B. (1984). Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician. The Annals of Statistics, 12(4), 1151–1172.
Schunk, D. H. (1989). Self-efficacy and achievement behaviors. Educational Psychology Review, 1(3), 173–208.
Smith-Castro, V. (comp.) (2014). Compendio de Instrumentos de Medición IIP-2014. Serie Cuadernos Metodológicos. Instituto de Investigaciones Psicológicas.
Smith-Castro, V., Montero-Rojas, E., Moreira-Mora, T. E., & Zamora-Araya, J. A. (2019). Expected and unexpected effects of sexism on women’s mathematics performance. Revista Interamericana De Psicología/Interamerican Journal of Psychology, 53(1), 28–44.
Stawski, R. S., Almeida, D. M., Lachman, M. E., Tun, P. A., & Rosnick, C. B. (2010). Fluid cognitive ability is associated with greater exposure and smaller reactions to daily stressors. Psychology and aging, 25(2), 330–342.
Waring, E., Quinn, M., McNamara, A., Arino de la Rubia, E., Zhu, H., & Ellis, S. (2022). Skimr: Compact and flexible summaries of data.
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., . . . Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686.
Zamora-Araya, J. A. (2020). Impacts of attitudes, social development, mother’s educational level and self efficacy on academic achievement in mathematics. Uniciencia, 34(1), 74–87.
How to Cite
García-Goñi, S., Loría-García, A., & Quesada-Leitón, H. (2023). The Effects of sexism against women on men’s self-efficacy and performance in Mathematics: structural equation models from the theory of ambivalent sexism. Uniciencia, 37(1), 1-21.
Original scientific papers (evaluated by academic peers)

Comentarios (ver términos de uso)