Inferential Statistical Literacy of Mathematics Education Students with Field Independent and Field Dependent Cognitive Styles

Background/purpose. This study aims to examine the inferential statistical literacy of mathematics education students based on their cognitive styles—field-dependent (FD) and field-independent (FI). Inferential statistical literacy involves not only understanding statistical concepts such as hypothesis testing and parameter estimation but also applying them critically and appropriately in real-world contexts.

Materials/methods. A qualitative descriptive method within a case study design was employed. Two students, each representing FD and FI cognitive styles, were selected as subjects. Data were collected using the Group Embedded Figures Test (GEFT), statistical literacy problems, and structured interviews. The data were analyzed qualitatively and categorized according to indicators of inferential statistical literacy: formulating hypotheses, testing hypotheses, and making decisions.

Results. The findings revealed clear differences in reasoning patterns between the two cognitive styles. FI students demonstrated strong analytical thinking and provided logical justifications throughout the problem-solving process. In contrast, FD students relied more on procedural thinking and often failed to explain their reasoning clearly, showing limited conceptual understanding.

Conclusion. Students with a field-independent cognitive style demonstrated high levels of inferential statistical literacy, whereas those with a field-dependent style were at a moderate level. The difference in inferential statistical literacy between the two groups was quite substantial, which qualitatively indicates the influence of cognitive style on inferential statistical literacy. These findings underscore the need for instructional strategies tailored to cognitive style differences in order to support the development of inferential statistical literacy in mathematics education.

Keywords: Inferential statistical literacy, mathematics education, field dependent, field independent, cognitive styles

References

Aberson, C. L., Berger, D. E., Healy, M. R., & Romero, V. L. (2003). Evaluation of an interactive tutorial for teaching hypothesis testing concepts. Teaching of Psychology, 30(1), 75–78. https://doi.org/10.1207/S15328023TOP3001_12

Afifah, D. S. N., & Nafi’an, M. I. (2019). An onto-semiotic approach: Analyzing of field-independent and field dependent students' understanding in solving statistical problems. J. Phys.: Conf. Ser. 1175 012148. https://doi.org/10.1088/1742-6596/1175/1/012148

Amalia, F., Wildani, J., & Rifa'i, M. (2020). Literasi Statistik Siswa Berdasarkan Gaya Kognitif Field Dependent dan Field Independent. JEMS (Jurnal Edukasi Matematika dan Sains), 8(1), 1-6. https://doi.org/10.25273/jems.v8i1.5626

Aoyama, K., & Stephens, M. (2003). Graph interpretation aspects of statistical literacy: A Japanese perspective. Mathematics Education Research Journal, 15(3), 207–225. https://doi.org/10.1007/BF03217380

Asmara, B. W. A., & Afifah, D. S. N. (2019). Profil Intuisi Matematis Siswa dalam Pemecahan Masalah Matematika Ditinjau Dari Gaya Kognitif Field Independent dan Field Dependent. Kontinu Jurnal Penelitian Didaktik Matematika, 3(1), 37-50, https://doi.org/10.30659/kontinu.3.1.37-50

Belia, S., Fidler, F., Williams, J., & Cumming, G. (2005). Researchers misunderstand confidence intervals and standard error bars. Psychological Methods, 10, 389–396. https://doi.org/10.1037/1082-989X.10.4.389

Ben-Zvi, D., & Garfield, J. B. (2004). Statistical literacy, reasoning, and thingking : Goals, definitions, and challenges. In D. Ben-Zvi & J. Garfield (Eds.), The Challenge of Developing Statistical Literacy, Reasoning and Thinking (pp. 3-15). Netherlands: Kluwer Academic Publishers. https://doi.org/10.1007/1-4020-2278-6_1

Chance, B., Ben-Zvi, D., Garfield, J., & Medina, E. (2007). The Role of Technology in Improving Student Learning of Statistics. Technology Innovations in Statistics Education, 1(1), 1-25. https://doi.org/10.5070/T511000026

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (p.273). SAGE Publications Inc. http://dx.doi.org/10.5539/elt.v12n5p40

Daniel, L. G. (1998). Statistical significance testing: A historical overview of misuse and misinterpretation with implications for the editorial policies of educational journals. Research in the Schools, 5(2), 23–32. http://www.stats.org.uk/statistical-inference/Daniel1998.pdf

DelMas, R., Garfield, J., Ooms, A., & Chance, B. (2007). Assessing students' conceptual understanding after a first course in statistics. Statistics Education Research Journal, 6(2), 28–58. http://www.stat.auckland.ac.nz/serj

Elfitra, & Siregar, T. M. (2020). Statistical Literacy Analysis of Mathematics Education Students Through KKNI Assignments. J. Phys.: Conf. Ser. 1462 012028. https://doi.org/10.1088/1742-6596/1462/1/012028

Elisa, A., Sotos, C., Vanhoof, S., Van den Noortgate, W., & Onghena, P. (2009). How confident are students in their misconceptions about hypothesis tests? Journal of Statistics Education, 17(2), 1-22. https://doi.org/10.1080/10691898.2009.11889514

Evans, J. S. B. T. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278. https://doi.org/10.1146/annurev.psych.59.103006.093629

Franklin, C. (2005). Guidelines for assessment and instruction in statistics education (GAISE) report. United States of America : American Statistical Association Alexandria

Gal, I. (2002). Adult statistical literacy: Meanings, components, responsibilities. International Statistical Review, 70(1), 1–25. https://doi.org/10.1111/j.1751-5823.2002.tb00336.x

Garfield, J., delMas, R., & Chance, B. (2003). Web-based assessment resource tools for improving statistical thinking. Paper presented at the annual meeting of the American Educational Research Association, Chicago. https://www.causeweb.org/cause/archive/artist/articles-/AERA_2003.pdf

Hadi, Abd., Asrori, & Rusman. (2021). Penelitian kualitatif studi fenomenologi, case study. Banyumas : Pena Persada.

Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus, and Giroux.

Kirk, R. E. (2001). Promoting good statistical practices: Some suggestions. Educational and Psychological Measurement, 61(2), 213–218. https://doi.org/10.1177/00131640121971185

Krauss, S., & Wassner, C. (2002). How significance tests should be presented to avoid the typical misinterpretations. In Proceedings of the sixth international conference on teaching statistics. Voorburg The Netherlands: International Statistical Institute.

Lailiyah, S., Muslimah, N., & Sutini, S. (2021). Do students with different cognitive styles have similar levels of statistical thinking?. Beta: Jurnal Tadris Matematika, 14(1), 15–33. https://doi.org/10.20414/betajtm.v14i1.438

L'Boy, D., & Nazim Khan, R. (2023). A Rasch-model-based hierarchical framework for statistical literacy and learning. International Journal of Mathematical Education in Science and Technology, 54(9), 1874-1887. https://doi.org/10.1080/0020739X.2023.2261453

Lukman & Wahyudin. (2020). Statistical literacy of undergraduate students in Indonesia: survey studies. International Conference on Mathematics and Science Education 2019 (ICMScE 2019). Journal of Physics: Conference Series 1521 (2020) 032050. https://doi.org/10.1088/1742-6596/1521/3/032050

Makar, K., & Rubin, A. (2009). A Framework for Thinking About Informal Statistical Inference. Statistics Education Research Journal, 8(1), 82-105. https://doi.org/10.52041/serj.v8i1.457

Marchy, F., & Juandi, D. (2023). Student’s Statistical Literacy Skills (1980-2023): A Systematic Literature Review with Bibliometric Analysis. Journal of Education and Learning Mathematics Research (JELMaR), 4(1), 31-45. https://doi.org/10.37303/jelmar.v4i1.105

Miles, M. B., Huberman, A. M., & Saldana, J. (2013). Qualitative data analysis (Third Edit). Arizona State University. California : sage Publication

Moleong, L. J. (2013). Metodologi Penelitian Kualitatif edisi revisi. Metodologi Penelitian Kualitatif edisi revisi. Bandung: PT Remaja Rosdakarya.

Rahman, M. S., Juniati, D., & Manuharawati. (2022). Strategic competence in solving-problem and productive disposition of high school students based on cognitive styles. AIP Conf. Proc. 2577, 020053. https://doi.org/10.1063/5.0096029

Roberge, J. J., & Flexer, B. K. (1983). Cognitive Style, Operativity, and Mathematics Achievement. Journal for Research in Mathematics Education, 14(5), 344-353. https://doi.org/10.2307/748679

Rumsey, D. J. (2002). Statistical Literacy as a Goal for Introductory Statistics Courses. Journal of Statistics Education, 10(3), https://doi.org/10.1080/10691898.2002.11910678

Sharma, S. (2017). Definitions and models of statistical literacy: a literature review, Open Review of Educational Research, 4(1), 118-133. https://doi.org/10.1080/23265507.2017.1354313

Shobikhah, A., Sukestiyarno, Y. L., Agoestanto, A., & Cahyono, A. N. (2025). Bibliometrics on the Development of Students' Statistical Literacy: A Scoping Review of Research Between the Years 2000 – 2024. TEM Journal. 14(1), 871-886. https://doi.org/10.18421/TEM141-77

Sugiyono. (2010). Metode Penelitian Pendidikan Pendekatan Kuantitatif, kualitatif, dan R&D. Bandung: Alfabeta

Sutama, Anif, S., Prayitno, H. J., Narimo, S., Fuadi, D., Sari, D. P., & Adnan, M. (2021). Metacognition of Junior High School Students in Mathematics Problem Solving Based on Cognitive Style. Asian Journal of University Education (AJUE). 17(1), 133-144. https://doi.org/10.24191/ajue.v17i1.12604

Takaria, J. (2015). Literasi Statistis dalam Pembelajaran Kolaboratif. Ambon : Universitas Pattimura

Testu, F. (1984). Rythmicité scolaire, nature de la tâche et dépendance-indépendance à l'égard du champ. L'année Psychologique, 84, 507–523. https://doi.org/10.3406/PSY.1984.29048

Van Blerkom, M. L. (1988). Field dependence, sex role self-perceptions, and mathematics achievement in college students: A closer examination. Contemporary Educational Psychology, 13, 339–347. https://doi.org/10.1016/0361-476X(88)90033-1

Watson, J. M., & Callingham, R. A. (2004). Statistical Literacy: From Idiosyncratic to Critical Thinking. Curricular Development in Statistics Education IASE Roundtable Conference. Sweden. https://doi/.org/10.52041/srap.04301

Watson, J., & Callingham, R. (2003). Statistical literacy: A complex hierarchical construct. Statistics Education Research Journal. 2(2), 3–46. http://fehps.une.edu.au/serj

Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. W. (1977). Field dependent and Field-independent cognitive styles and their educational implicationn. Review of Educational Research 47(1), 1-64. https://doi.org/10.2307/1169967

Yotongyosa, M., Traiwichitkhun, D., & Kaemkate, W. (2015). Undergraduate Students’ Statistical Literacy: A Survey Study. Procedia - Social and Behavioral Sciences, 191, 2731-2734. https://doi.org/10.1016/j.sbspro.2015.04.328

Announcement

EDUPIJ Citation Metrics

EDUPIJ News!