Multivariate analysis on performance in statistics, self-efficacy and attitudes of senior high school students

Mildin Jeminez Retutas(1*), Marilyn Torela Rubio(2)

(1) College of Teacher Education and Technology, University of Southeastern Philippines, Philippines
(2) College of Education, Bukidnon State University, Philippines
(*) Corresponding Author

Abstract

Over the past few years, teaching and learning of statistics have been influenced by the emergence of the reform movement in education such as the K-12 basic education curriculum. Those of statistics concepts have changed both elementary and secondary level. Considering the educational reform in the Philippines, the study was conducted to determine whether there are significant differences of the determinants such as gender, type of school, parent’s educational level, family monthly income, family size and Senior High School track preference to students’ self-efficacy beliefs, attitudes towards Statistics, and performance in Statistics. The causal-comparative research design was used for comparing two or more groups to find the differences or determine whether the independent variable influences the dependent variable. The data were gathered from 570 senior high school students of both public and private schools in Mindanao, Region XI. The study adopted the questionnaires on self-efficacy beliefs and attitude towards Statistics while it utilized a researcher-made questionnaire for performance in Statistics. Multivariate Analysis of Variance (MANOVA) was used to determine whether multiple levels of independent variables on their own or in combination with one another influence the dependent variables. The findings revealed that among the demographic factors, only type of school has a significant difference to the self-efficacy beliefs, attitudes towards Statistics, and performance of senior high students in Statistics. Implications from the findings of this study might suggest that improving of K-12 school facilities by the school public administrators and collaborative effort of teachers to enhance the students’ self-efficacy, attitudes towards statistics and teaching statistics reveals optimistic results.  Also, school administrators may provide opportunities for Statistics teachers to hone their pedagogical skills in promoting and building students’ self-confidence and interest in the subject.

Keywords

Demographic profiles, self-efficacy beliefs, attitudes towards Statistics, performance in statistics and probability, multivariate analysis

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References

Ajai, J. T., & Imoko, I. I. (2015). Gender differences in Statistics achievement and retention scores: A case of problem-based learning method. International Journal research in Education and Science (IJRES), I(1), 45-50.

Ali, H. O. (2013). Factors affecting students’ academic performance in Mathematical Sciences Department in tertiary institutions in Nigeria. US-China Education Review A, 3(12), 905-913.

Arghode, V. (2012). Qualitative and quantitative research: Paradigmatic differences. Global Education Journal, 2012(4), 155-163. Retrieved from https://eds.a.ebscohost.com.library.gcu.edu:2048/ehost/

Asante, K. O. (2012). Secondary students’ attitudes towards Statistics. IFE Psycologia: An International Journal 20(1), 121–133. Retrieved form https://journals.co.za/doi/abs/10.10520/EJC38916

Baksh ali, Z. S., Pasaha, E. & Rastegar, A. (2013). Statistics and modeling. Tehran: Iranina textbook Publishing Company.

Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, N.J. Prentice Hall.

Batanero, C. & Diaz, C. (2010). Training teachers to teach statistic:what can we learn from research? Statistique et enseignement, 1(1), 5-20.

Boone, H. N., & Boone, D. A. (2012). Analyzing likert data. Journal of extension, 50(2), 1 – 5.

Braza, M. r. &Guillo, Jr. R. M. (2015). Socio-demographic characteristics and career choice of private secondary school students. Asia pacific Journal of Multidisciplinary Research, Vol. 3, No. 4.

Byrne, M., Flood, B., & Griffin, J. (2014). Measuring the academic self-efficacy of first-year accounting students. Accounting Education, 23(5), 407-423. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/09639284.2014.931240

Callaman, R. A. & Itaas, E. C. (2020). Students’ Statistics achievement in Mindanao context: A meta-analysis. JRAMathEdu (Journal of Research and Advances in Statistics Education), 5(2), 148-159. Retrieved from https://files.eric.ed.gov/fulltext/EJ1267489.pdf

Chapagain, Y. (2021). School student academic performance in Nepal: An analysis using the School Education Exam (SEE) results. International Journal on Studies in Education, 3(1), 22-36. https://doi.org/10.46328/ijonse.34

Chiesi, F., Pirmi. C., & Morsanyi, K. (2010). Learning Probability and Statistics:Cognitive and Non-cognitive Factors Related to Psychology Students’ Achievement. International Association of Statistical Education (ISAE).

Chinn, S. (2012). Beliefs, anxiety and avoiding fear in Statistics. Child Development Research, 2012.

Department of Education Order No. 8s. (2015). Policy Guidelines on Classroom Assessment for the K to 12 Basic Education Program. Retrieved from PISA 2018 National Report of the Philippines.

Doyle, K. M., Dias, O., Kennis, J. R., Czarnocha, B. & Baker, W. (2015). The ratonal number subconstructs as a foundation for problem solving. Adults Learning Statistics: An International Journal, 11(1), 21-42. Retrieved from https://files.eric.ed.gov/fulltext/EJ1091996.pdf

Emmioglu, E., &Capa-Aydin, Y. (2012). Attitudes and achievement in statistics: A meta-analysis study. Statistics Education Research Journal, 11(2) 95 – 102.

Gherasim, L. R., Butnaru. S., &Mairean, C. (2013). Classroom environment, achievement goals and maths performance: gender differences. Educational Studies, 39(1),1 –12.

Gray, L. R., Mills, G. E., &Airasian, P. (2011). Educational research: Competencies for analysis and application. (10th ed.), Upper Saddle River, NJ: Pearson.

Hansen, R. & Myers, J. (2012). Rote versus conceptual emphases in teaching elementary probability. Journal for R (Placeholder1) Research Statistics Education, 16, 364-374. https://doi.org/10.5951/jresematheduc.16.5.0364

Herrera, F. (2011). Problem and activity-based approaches: Their influences to student’s achievement and retention scores in introductory probability and statistics. International Association of Multidisciplinary Research Journal, 2(1), 48-67. Retrieved from https://ejournals.ph/article,php?id=2425

Humlum, M. (2011). Timing of family income, borrowing constraints and child achievement. Journal of Population Economics, vol. 24, 3, 979-1004.

Huynh, M., Baglin, J., & Bedford, A. (2014). Improving the attitudes of high school students towards statistics: An island-based approach. In Sustainability in Statistics Education. Proceedingd of the Ninth International Conference on Teaching Statistics (ICOTS9), Flagstaff, Arizona, USA. Voorburg; International Association of Statistics Association> Retrieved from https://icots.info/9/proceedings/pdfs/ICOTS9_9G@_HUYNH.pdf

Hussain, Dr. (2019). Re: How does causal-comparative research designs fit to Information systems science?

Jamisola, N. B. (2014). Predictors of Statistics Performance of Fourth Year Students in Davao City, Unpublished Thesis. University of Southeastern Philippines, Davao City, Philippines.

Lacovou, M. (2010). Leaving home: Independence, togetherness and income. Institute for Social and Economic Research, Essex University, Colchester CO4 3SQ, UK.

Lemana, L. G. (2012). A Structural Model Predicting Performance of Freshmen Statistics Unpublished Thesis. University of Southeastern Philippines, Davao City, Philippines.

McGrath, A. L., Ferns, A., Greiner, L., Wanamaker, K., & Brown, S. (2015). Reducing anxiety and increasing self-efficacy within an advanced graduate psychology statistics course. Canadian Journal for the Scholarship of Teaching and Learning, 6(1), 5. Retrieved from https://eric.ed.gov/?id=EJ1057728

Murray, S. (2011). Declining participation in post-compulsoysecodnry school Statistics: students’ views and solutions to the problem. Research in Statistics Education, 13(3), 269-285. https://doi.org/10.1080/14794802.2011.624731

Orongan, R. C. (2007). Structural model of cognitive, affective and demographic factors on tertiary student’s performance in introductory statistical at Central Mindanao University, Bukidnon Philippines. Unpublished Dissertation. Central Mindanao Univeristy, Bukidnon Philippines.

Parker, P. D., Marsh, H. W., Ciarrochi, J., Marshall, S., & Abduljabbar, A. S. (2013). Juxtaposing math self-efficacy and self-concept as predictors of long-term achievement outcomes. Educational Psychology, 34(1), 29-48.

Peters, P., Smith, A., Middledorp, J., Karpin, A., Sin, S., & Kilgore, A. (2013). Learning essential terms and concepts in statistics and accounting. Higher Education Research & Development, 33(4), 742 – 756. https://doi.org/10.1080/07294360.2013.863838

Philias, O. Y., & Wanobi, W. C. (2011). Performance determinants of Kenya Certificate of Secondary Education (KCSE) in Statistics of secondary schools in Nyamaiya Division, Kenya. Asian Social Science, 7 (2), 107-112.

Prado, M. & Gravoso, R. (2011). Improving high school students’ statistical reasoning skills: A case of applying anchored instruction. The Asia-Pacific Education Researcher, 20(1), 61 – 72.

Reardon, S. F. (2013). The widening income achievement gap. Educational leadership, 70(8), 10-16.

Salvan, N. L. (2014). Determinants of Grade 7 Mathematical Achievement: Basis for Developing a Tool for Selecting Special Program of the Science, Technology and Engineering (STE) Students (Unpublished master’s thesis). University of Southeastern Philippines, Davao City, Philippines

Salvan, E. (2016). Demographic Characteristics, Socio-Psychological Attributes, And Performance Of College Students In Statistics. Unpublished Dissertation. Central Mindanao University, Bukidnon Philippines.

Schneider, W. R. (2011). The relationship between statistics self-efficacy, statistics anxiety, and performance in an introductory graduate statistics course. Retrieved from https://scholarcommons.usf.edu/etd/3335

Swift, J. (2012). Challenges for enriching the curriculum: Statistics and probability. Statistics Teachers, 76, 268 – 269. https://doi.org/10.5951/MT.76.4.0268

Thapa, A. (2015). Public and private school performance in Nepal: an analysis using the SLC examination. Education Economics, 23(1), 47-62. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/09645292.2012.738809

Vellymalay, S.K.N. (2010). Parental involvement in children’s education: Does parents’ education level really matter? European Journal of Social Sciences,16(3), 430-431.

Williams, A. (2014). An exploration of preference for numerical information in relation to math self-concept and statistics anxiety in a graduate statistics course. Journal of Statistics Education, 2(1), 1-16. Retrieved from https://eric.ed.gov/?id=EJ1031923

Yilmaz, K. (2013). Comparison of quantitative and qualitative research traditions: Epistemological, theoretical, and methodological differences. European Journal of Education, 48(2), pp. 311-325.

Zacal, S. G. (2019). Psycho-social Attributes, Reading Comprehension and Problem-Solving Skills of Junior High School: A Structural Model on Statistics Performance. Unpublished Dissertation. Bukidnon State University, Malaybalay City, Bukidnon, Philippines.

Zieffler, A., Garfield, J., Alt, S., Dupuis, D., Holleque, K., & Chang, B. (2008). What Does Research Suggest About the Teaching and Learning of Introductory Statistics at the College Level? A Review of the Literature. Journal of Statistics Education, 16(2). Retrieved from https://jse.amstat.org/v16n2/zieffler.pdf

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