Principal Component Analysis: A Revalidation of the Mathematics Persistence Scale

Authors

  • Vanessa A. Marmita Leyte Normal University
  • Melet B. Mariano De La Salle University, Manila
  • Eric M. Masela De La Salle University, Manila

Keywords:

persistence, mathematics persistence scale, reliability, validity, principal component analysis

Abstract

A reliable and valid instrument on mathematical persistence, appropriate for Filipino students, can address gaps in mathematics education especially that there is a relationship between persistence and problem solving. This paper addressed the purpose of providing additional data concerning the reliability and validity of Mathematics Persistence Scale (MPS) in the Philippine setting. An instrument used by Stoll in his study in 2015, which looked into different components relevant to mathematical persistence in education, was revalidated considering Filipino participants. The questionnaires were distributed to randomly selected 194 senior high school students. Factor structure extraction was done making use of the principal components method with varimax rotation. Four factors were extracted specifically labeled as; Effortful Math, Understanding Math Concepts, Innate Math Persistence and Math Confidence. MPS has good internal consistency with Cronbach’s alpha in the range of .508 to .860. Based on the findings, the same factor structure was extracted and is consistent with what this instrument is supposed to measure. Therefore, this MPS can also be administered among Filipino students in evaluating their level of persistence and can also be a reliable basis to further establish
interventions to improve mathematics learning.

References

Andaya, O. J. F. (2014). Factors that affect mathematics achievements of students of Philippine Normal University-Isabela Campus. Researchers World, 5(4), 83-91.

Barnett, E. A. (2011). Validation experiences and persistence among community college students. The Review of Higher Education, 34(2), 193-230.

Breen, S., Cleary, J. & O'Shea, A. (2010). Measuring students’ persistence on unfamiliar Mathematical tasks. Proceedings of the British Society for Research into Learning Mathematics, 30(3), 19-24.

Bro, R., & Smilde, A. K. (2014). Principal component analysis. Analytical Methods, 6(9), 2812-2831.

Constantin, T., Holman, A., & Hojbotă, M. A. (2011). Development and validation of a motivational persistence scale. Psihologija, 45(2), 99-120.

Cribbs, J. D., Hazari, Z., Sonnert, G., & Sadler, P. M. (2015). Establishing an explanatory model for mathematics identity. Child Development, 86(4), 1048–1062. doi:10.1111/cdev.12363

Dela Rosa, E. D., & Bernardo, A. B. I. (2013). Testing multiple goals theory in an Asian context: Filipino University students’ motivation and academic achievement. International Journal of School & Educational Psychology, 1(1), 47-57. doi:10.1080/21683603.2013.782594

Ellington, R. M., & Frederick, R. (2010). Black high achieving undergraduate Mathematics majors discuss success and persistence in mathematics. Negro Educational Review, 61(1-4), 61-84.

Fisher, C. M., Elrod, C. C., & Mehta, R. (2011). A replication to validate and improve a measurement instrument for Deming's 14 Points. International Journal of Quality & Reliability Management, 28(3), 328-358.

Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In S. Christenson, A. Reschly, C. Wylie, (Eds.), Handbook of Research on Student Engagement (pp. 763–782). Springer. doi:10.1007/978-1-4614-2018-7_37

Hair, J., Black, W., Babin, B., & Anderson, R. (2009). Exploratory factor analysis. In Hair, J., Black, W., Babin, B., & Anderson, R. (Eds.), Multivariate Data Analysis: A Global Perspective (7th Edition) (pp. 89-150). Upper Saddle River: Prentice Hall.

Kim, H., Ku, B., Kim, J.Y., Park, Y-J., & Park, Y-B. (2016). Confirmatory and exploratory factor analysis for validating the Phlegm Pattern Questionnaire for healthy subjects. Evidence-Based Complementary and Alternative Medicine, 2016, 1-8. http://dx.doi.org/10.1155/2016/2696019

Kim, Y.-M. (2009). Validation of psychometric research instruments: The case of information science. Journal of the American Society for Information Science and Technology, 60(6), 1178–1191. doi:10.1002/asi.21066

Klados, M. A., Pandria, N., Micheloyannis, S., Margulies, D., & Bamidis, P. D. (2015). Math anxiety: Brain cortical network changes in anticipation of doing mathematics. International Journal of Psychophysiology, 122, 24-31. doi.org/10.1016/j.ijpsycho.2017.05.003

Kooken, J., Welsh, M. E., McCoach, D. B., Johnston-Wilder, S., & Lee, C. (2016). Development and validation of the mathematical Resilience Scale. Measurement and Evaluation in Counseling and Development, 49(3), 217–242. doi:10.1177/0748175615596782

Lai P. (2013). Validating instruments of measure: Is it really necessary?. Malaysian Family Physician, 8(1), 2–4.

Leon, J., Medina-Garrido, E., & Núñez, J. L. (2017). Teaching quality in math class: The development of a scale and the analysis of its relationship with engagement and achievement. Frontiers in Psychology, 28. doi:10.3389/fpsyg.2017.00895

Liu, X., & Koirala, H. (2009). The effect of mathematics self-efficacy on mathematics achievement of high school students. NERA Conference Proceedings 2009. 30. Retrieved from: https://opencommons.uconn.edu/nera_2009/30.

Martin, D. B. (2009). Researching race in mathematics education. Teachers College Record, 111(2), 295-338.

Mohsenpour, M., Hejazi, E., & Kiamanesh, A. R. (2006). The role of self-efficacy, achievement goals, learning strategies and persistence in math achievement of 11 Grade high schools students in Tehran. Journal of Educational Innovations, 5(16), 9-35.

Montague, M., & Applegate, B. (2000). Middle school students’ perceptions, persistence, and performance in mathematical problem solving. Learning Disability Quarterly, 23(3), 215–227. doi:10.2307/1511165

Nagaoka, J., Farrington, C. A., Roderick, M., Allensworth, E., Keyes, T. S., Johnson, D. W., & Beechum, N. O. (2013). Readiness for college: The role of noncognitive factors and context. Voices in Urban Education, 38, 45-52.

New York City Transfer School Common Core Institute. (2016). Strengthening persistence in math and beyond: Retrieved from: http://www.eskolta.org/catalog/files/COPHS% 20NQCHS%2011-28-2016.pdf. Ngo, F. & Kosiewicz, H. (2017). How extending time in developmental math impacts student persistence and success: Evidence from a regression discontinuity in community colleges. The Review of Higher Education, 40(2), 267-306. doi.org/10.1353/rhe.2017.0004

Persistent. (2018) In Merriam-Webster’s online dictionary. Retrieved from: https://www.merriam-webster.com/dictionary/persistent.

Reason, R. D. (2009). An examination of persistence research through the lens of a comprehensive conceptual framework. Journal of College Student Development, 50(6), 659-682.

Republic Act No. 10533: The enhanced Basic Education Act of 2013. (2013, May 15). Philippines. Retrieved from: http://www.officialgazette.gov.ph/2013/05/15/republic-act-no-10533/

SEI-DOST & MATHTED, (2011). Mathematics framework for Philippine basic education. Manila: Sei-Dost & Mathted.

Stoll, D. (2015). The effects of constructs related to Mathematical persistence on Student performance during problem solving (Master’s theses). 627.

Usher, E. L., & Pajares, F. (2009). Sources of self-efficacy in mathematics: A validation study. Contemporary Educational Psychology, 34(1), 89-101.

Veenman, M. V. (2011). Alternative assessment of strategy use with self-report instruments: A discussion. Metacognition and Learning, 6(2), 205-211.

Wolfle, J. D. (2012). Success and persistence of developmental mathematics students based on age and ethnicity. The Community College Enterprise, 18(2), 39.

Zerpa, C., Hachey, K., van Barneveld, C., & Simon, M. (2011). Modeling student motivation and students’ ability estimates from a large-scale assessment of mathematics. Sage Open, 1(2), 1-9. doi: 10.1177/2158244011421803.

Published

12/01/2018

How to Cite

Marmita, V. A., Mariano, M. B., & Masela, E. M. (2018). Principal Component Analysis: A Revalidation of the Mathematics Persistence Scale. Journal of Education and Society, 2(1), 32–40. Retrieved from https://journals.lnu.edu.ph/index.php/jes/article/view/40

Issue

Section

Research Articles