Psychometric Validation of the Mathematics Attitude Questionnaire (MAQ): A Confirmatory Factor Analysis Approach
Article Sidebar
Main Article Content
Abstract
Purpose – Mathematics underperformance remains a global challenge, especially in low-resource and conflict-affected contexts where students often face affective barriers such as anxiety, low enjoyment, and self-doubt. Although the Mathematics Attitude Questionnaire (MAQ) has been widely used internationally, its structural validity has rarely been examined in sub-Saharan Africa. This study aimed to validate the MAQ among Nigerian senior secondary school students.
Methodology – A cross-sectional quantitative design under a post-positivist paradigm was employed. Using multistage sampling, 204 students (mean age = 16.8 years; 55% male) from three educational zones in Kaduna State completed a culturally adapted 31-item MAQ. Exploratory Factor Analysis (EFA) was first conducted to identify the underlying structure, followed by Confirmatory Factor Analysis (CFA) in Mplus to evaluate model fit. Reliability was assessed using coefficient omega, while validity was examined through Average Variance Extracted (AVE) and Heterotrait-Monotrait ratio (HTMT).
Findings – EFA supported a two-factor structure: Enjoyment of Mathematics and Perception of Incompetence. CFA indicated suboptimal model fit (CFI = .831; TLI = .808; RMSEA = .141; SRMR = .100), though factor loadings (.49–.80) were significant. Reliability was strong (ω = .933; .872), AVE exceeded .58, and HTMT (.67) supported discriminant validity. The results affirm the relevance of the two constructs but highlight the need for theoretical refinement and cultural adaptation.
Novelty – This is the first empirical validation of the MAQ using CFA in Nigeria, addressing a critical methodological gap in sub-Saharan mathematics education research.
Significance – The validated MAQ provides educators, curriculum developers, and policymakers with a reliable diagnostic tool to assess and strengthen students’ affective engagement, guiding interventions to enhance enjoyment, self-efficacy, and mathematics performance.
Article Details
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Akendita, P. A., Boateng, F. O., Arthur, Y. D., Banson, Maccarthy Abil, & Marfo Ahenkorah. (2025). The Mediating Role of Teacher Effective Communication on the Relationship between Students’ Mathematics Interest and their Mathematics Performance. International Journal of Mathematics and Mathematics Education, 3(1), 1–17. https://doi.org/10.56855/ijmme.v3i1.1214
Alfred, B. G., Dissou, A. Y., & Adu, O. B. (2024). Effects of Perceived Mathematics Connection on Mathematics Motivation: Mediating Role of History of Mathematics Concepts. International Journal of Mathematics and Mathematics Education, 2(1), 1–14.
Bandura, A. (1997). Self-efficacy: The exercise of control. Macmillan.
Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford Publications.
Carcueva, R. E. (2024). Attitude in Learning as a Mediator for Engagement and Academic Performance in Mathematics. Journal of Interdisciplinary Perspectives, 2(6), 1–1.
Chen, L., Bae, S. R., Battista, C., Qin, S., Chen, T., Evans, T. M., & Menon, V. (2018). Positive Attitude Toward Math Supports Early Academic Success: Behavioral Evidence and Neurocognitive Mechanisms. Psychological Science, 29(3), 390–402. https://doi.org/10.1177/0956797617735528
Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(1). https://openpublishing.library.umass.edu/pare/article/id/1650/
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications.
Danlami, K. B., Ginga, U. A., Aliyu, A. S., Umahaba, R. E., & Tsoho, M. (2024). Impact of the Know-Want-Learn (KWL) Strategy on Geometry Performance Among Upper-Basic Students in Nigeria. Journal of Educational Studies in Science and Mathematics (JESSM), 3(2), 87–104.
Danlami, K. B., Zakariya, Y. F., Balarabe, B., Alotaibi, S. B., & Alrosa, T. M. (2025). Improving Students’ Performance in Geometry: An Empirical Evidence of the Effectiveness of Brainstorming Learning Strategy. Frontiers in Psychology, 16, 1577912.
Davadas, S. D., & Lay, Y. F. (2020). Contributing Factors of Secondary Students’ Attitude towards Mathematics Contributing Factors of Attitudes towards Mathematics. European Journal of Educational Research, 9(2), 489–498. https://doi.org/10.12973/eu-jer.9.2.489
Doruk, M., Öztürk, M., & Kaplan, A. (2016). Ortaokul Öğrencilerinin Matematiğe Yönelik Öz-yeterlik Algılarının Belirlenmesi: Kaygı ve Tutum Faktörleri. Adıyaman Üniversitesi Eğitim Bilimleri Dergisi, 283–283. https://doi.org/10.17984/adyuebd.306387
Facultada, E. B., & Sebialb, S. C. L. (2019). Development and Validation of a Mathematics Attitude Scale (MAS) for High School Students in Southern Philippines. Development, 8(7). https://www.academia.edu/download/83071109/8713_Facultad_2019_E_R.pdf
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Klein, S. A. (2001). Measuring, estimating, and understanding the psychometric function: A commentary. Perception & Psychophysics, 63(8), 1421–1455. https://doi.org/10.3758/BF03194552
Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford Publications.
Lapite, A. O. (2020). Some Psychological Constructs and Mathematics Performance in Nigerian Secondary Schools. In K. S. Adeyemo (Ed.), The Education Systems of Africa (pp. 1–14). https://doi.org/10.1007/978-3-030-43042-9_33-1
Laranang, J. A. I., & Bondoc, J. M. F. (2020). Attitudes and Self-Efficacy of Students toward Mathematics. International Journal of English Literature and Social Sciences, 5(5), 1392–1423. https://doi.org/10.22161/ijels.55.11
Lee, J.-Y., & Shin, Y.-J. (2022). Using the Theory of Planned Behavior to Predict Korean College Students’ Help-Seeking Intention. The Journal of Behavioral Health Services & Research, 49(1), 76–90. https://doi.org/10.1007/s11414-020-09735-z
Lijie, Z., Zongzhao, M., & Ying, Z. (2020). The influence of mathematics attitude on academic achievement: Intermediary role of mathematics learning engagement. Jurnal Cendekia, 4(2), 460–467.
Maamin, M., Maat, S. M., & H. Iksan, Z. (2021). The influence of student engagement on mathematical achievement among secondary school students. Mathematics, 10(1), 41.
Mamolo, L. A., & Sugano, S. G. C. (2020). Self-perceived and actual competencies of senior high school students in General Mathematics. Cogent Education, 7(1), 1779505. https://doi.org/10.1080/2331186X.2020.1779505
Mazana, M. Y., Montero, C. S., & Casmir, R. O. (2018). Investigating Students’ Attitude towards Learning Mathematics. International Electronic Journal of Mathematics Education, 14(1). https://doi.org/10.29333/iejme/3997
McNeish, D., An, J., & Hancock, G. R. (2018). The Thorny Relation Between Measurement Quality and Fit Index Cutoffs in Latent Variable Models. Journal of Personality Assessment, 100(1), 43–52. https://doi.org/10.1080/00223891.2017.1281286
McNeish, D., & Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods, 28(1), 61.
Millones-Liza, D. Y., García-Salirrosas, E. E., Apaza-Cáceres, J. A., & Norabuena-Diaz, R. Á. (2023). Influence of Self-Efficacy, Perceived Enjoyment and Ease of Use on Mathematics Learning Satisfaction in Virtual Environments. Journal of Educational and Social Research, 4(3), 47 - 59.
Nunnally, J. (1994). Psychometric theory. (No Title). https://cir.nii.ac.jp/crid/1370002219408110722
Oluyomi, A. J., Abdussalam, M. M., & Ismail, M. (2024). The Influence of Mathematics Anxiety, Motivation and Attitudes on the Academic Performance of Senior Secondary School Students in Katsina State. International Journal of Assessment and Evaluation in Education, 4(8), 153 - 168
Palacios, A., Arias, V., & Arias, B. (2013). Attitudes Towards Mathematics: Construction and Validation of a Measurement Instrument // Las actitudes hacia las matemáticas: Construcción y validación de un instrumento para su medida. Revista de Psicodidactica / Journal of Psychodidactics, 19(1), 67–91. https://doi.org/10.1387/RevPsicodidact.8961
Phillips, D. C., & Burbules, N. C. (2000). Postpositivism and educational research. Rowman & Littlefield.
Radišić, J., Buchholtz, N., Yang-Hansen, K., Liu, X., & Kaarstein, H. (2024). Do teachers’ beliefs about the nature and learning of mathematics affect students’ motivation and enjoyment of mathematics? Examining differences between boys and girls across six countries. European Journal of Psychology of Education, 39(2), 1587–1613. https://doi.org/10.1007/s10212-024-00809-6
Ross, N. S., & Rajkoomar, M. (2024). Exploring current student-centred assessment practices in higher education towards adaptive graduates. Perspectives in Education, 42(4), 153–170. https://doi.org/10.38140/pie.v42i4.8153
Serpell, R., & Simatende, B. (2016). Contextual responsiveness: An enduring challenge for educational assessment in Africa. Journal of Intelligence, 4(1), 3.
Singh, K., Granville, M., & Dika, S. (2002). Mathematics and Science Achievement: Effects of Motivation, Interest, and Academic Engagement. The Journal of Educational Research, 95(6), 323–332. https://doi.org/10.1080/00220670209596607
Tobih, D. O., Ayanwale, M. A., & Ndlovu, M. (2025). Development and validation of an attitudinal mathematics word problems scale for secondary school learners in Nigeria. International Journal of Educational Research Open, 8, 100440. https://doi.org/10.1016/j.ijedro.2025.100440
Van de Vijver, F. J., & Leung, K. (2021). Methods and data analysis for cross-cultural research (Vol. 116). Cambridge University Press.
Wang, M., Binning, K. R., Del Toro, J., Qin, X., & Zepeda, C. D. (2021). Skill, Thrill, and Will: The Role of Metacognition, Interest, and Self‐Control in Predicting Student Engagement in Mathematics Learning Over Time. Child Development, 92(4), 1369–1387. https://doi.org/10.1111/cdev.13531
Zakariya, Y. F. (2017). Development of Attitudes towards Mathematics Scale (ATMS) using Nigerian Data–Factor Analysis as a Determinant of Attitude Subcategories. International Journal of Progressive Education, 13(2), 74-84.
Zakariya, Y. F. (2022). Cronbach’s alpha in mathematics education research: Its appropriateness, overuse, and alternatives in estimating scale reliability. Frontiers in Psychology, 13, 1074430.
Zakariya, Y. F., Awofala, A. O., & Radmehr, F. (2024). Affective constructs in mathematics education. In Frontiers in Psychology, 15(4), 1373804).
Zakariya, Y. F., & Bamidele, E. F. (2016). Investigation into the causes of poor academic performance in mathematics among Obafemi Awolowo University undergraduate students, Nigeria. GYANODAYA - The Journal of Progressive Education, 9(1), 11. https://doi.org/10.5958/2229-4422.2016.00002.5
Zakariya, Y. F., Berg, M. T., Gjesteland, T., Umahaba, R. E., & Abanikannda, M. O. (2025). From single to multiple assessments in a foundational mathematics course for engineering students: What do we gain? Frontiers in Education, 10, 1544647.
Zakariya, Y. F., Nilsen, H. K., Bjørkestøl, K., & Goodchild, S. (2020). Impact of attitude on approaches to learning mathematics: A structural equation modeling approach. INDRUM 2020. https://hal.science/hal-03113844/