Albert Science International Organization

ARTICLE DESCRIPTION: 

Samuel Asiedu-Addo, Jonathan Annan, Yarhands Dissou Arthur, A COMPARATIVE ANALYSIS OF STATISTICAL MODELING TECHNIQUES: THE EFFECT OF LECTURER’S ATTRIBUTE ON TERTIARY STUDENT’S INTEREST IN STATISTICS, ASIO Journal of Chemistry, Physics, Mathematics & Applied Sciences (ASIO-JCPMAS), 2016, 1(3): 10-17.

ARTICLE TYPE: Research

dids no.: 03.2016-26169319, dids Link: http://dids.info/didslink/08.2016-97115428/ 

doi no.: 05.2016-11672519, doi Link: http://doi-ds.org/doilink/08.2016-53336117/

ABSTRACT:  

Relational effect of a lecturer’s personal attribute on students’ interest in statistics was the subject of investigation. Some lecturer’s personal attributes examined in this study include dynamism; communication strategies in the classroom, rapport created in the classroom and applied knowledge during lectures. The study used exploratory research design to establish the effect of lecturer’s personal attributes on student’s interest. Data was analyzed by means of confirmatory factor analysis and structural equation modeling (SEM) using the Smart PLS 3 program. In this study, 376 students were randomly selected from the Faculty of Technical and Vocational Education of the University of Education Winneba, Kumasi campus, and the Ghana Technology University College as well as the Kwame Nkrumah University of science and Technology. The results of this paper revealed that, the personal attributes of an effective lecturer namely lecturer’s dynamism, rapport, communication and applied knowledge contribute effectively (52.9%) in explaining students interest in statistics. Regression analysis and Structural equation modeling confirmed that lecturer’s personal attribute contribute effectively in predicting student’s interest i.e., 52.9% and 53.7% using regression and SEM, respectively. Our findings showed that the total effect of a lecturer’s attribute on a student’s interest was moderate and significant although lecturer’s communication and dynamism contribute positively but insignificantly to the prediction of a student’s interest. The results of the study further concluded that a lecturer’s personal attributes such as applied knowledge and rapport have positive and significant effect on a tertiary student’s interest in Statistics. The study finally found lecturer’s communication and dynamism to influence student’s interest in statistics positively but not significantly.

Key words: Student interest, Effective teacher, Personal attributes, Regression and SEM.

1. Carpenter, J.M., 2006. Effective teaching Methods for Large Classes. Journal of Family and Consumer Sciences Education, 24(2), pp.13–23.
2. Reid, D.J. & Johnston, M., 1999. Improving Teaching in Higher Education: Student and teacher perspectives. Educational Studies, 25(3), pp.269–281.
3. Pozo-Muñoz, C., Rebolloso-Pacheco, E. & Fernández-Ramírez, B., 2000. The “Ideal Teacher”. Implications for student evaluation of teacher effectiveness. Assessment & Evaluation in Higher Education, 25(3), pp.253–263.
4. Faranda, W.T., 2004. Student Observations of Outstanding Teaching: Implications for Marketing Educators. Journal of Marketing Education, 26(3), pp.271–281.
5. Clayson, D.E. & Haley, D.A., 1990. Student Evaluations in Marketing: What Is Actually Being Measured? Journal of Marketing Education, 12(3), p.9. Available at:http://search.ebscohost.com/login.aspx?direct=true&db=eue&AN=5381204&site=ehost-live&scope=site.
6. Kelley, C.A., Conant, J.S. & Smart, D.T., 1991. Master Teaching Revisited Pursuing Excellence from the Students’ Perspective. Journal of Marketing Education, 13(2), pp.1–10.
7. Patrick, J. & Smart, R.M., 1998. An empirical evaluation of teacher effectiveness: The emergence of three critical factors. Assessment and Evaluation in Higher Education, 23(2), pp.165-178. http://search.proquest.com.libraryproxy.griffith.edu.au/docview/203788898accountid=14543\nhttp://www.tandfonline.com.libraryproxy.griffith.edu.au/doi/pdf/10.1080/0260293980230205.
8. Young, S. & Shaw, D.G., 1999. Profiles of Effective College and University Teachers. The Journal of Higher Education, 70(6), pp.670–686. Available at:http://www.jstor.org/stable/2649170\nhttp://www.jstor.org.libraryproxy.griffith.edu.au/stable/pdfplus/2649170.pdf.
9. Pagani, L. & Seghieri, C., 2002. A statistical analysis of teaching effectiveness from students’ point of view. of: Developments in Statistics, pp.197–208.
10. Paswan, A.K. & Young, J.A., 2002. Student Evaluation of Instructor: A Nomological Investigation Using Structural Equation Modeling. Journal of Marketing Education, 24(3), pp.193–202. Available at: http://jmd.sagepub.com/cgi/content/abstract/24/3/193.
11. Heffernan, T., 2010. Personal attributes of effective lecturers: The importance of dynamism, communication, rapport and applied knowledge. International Journal of Management Education, 8(3), pp.13–27.
12. Bagozzi, R.P. & Yi, Y., 1988. On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(1), pp.74–94.
13. Bagozzi, R.P. & Yi, Y., 2012. Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), pp.8–34.
14. Ringle, C.M., Sarstedt, M. & Straub, D., 2012. A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly (MISQ), 36(1), pp.iii–xiv.
15. Seiler, V.L. & Seiler, M.J., 2002. Professors who make the grade. Review of Business, 23(2), p.39.
16. Smart, D.T., Kelley, C. a. & Conant, J.S., 2003. Mastering the Art of Teaching: Pursuing Excellence in a New Millennium. Journal of Marketing Education, 25(1), pp.71–78.
17. Wong, K.K., 2011. Book review: handbook of partial least squares: concepts, methods and applications.
18. Wong, K.K., 2010. Handling small survey sample size and skewed dataset with partial least square path modelling. Vue: The Magazine of the Marketing Research and Intelligence Association, November, pp.20–23.
19. Wong, K.K., 2013. Partial Least Squares Structural Equation Modelling (PLS-SEM) Techniques Using SmartPLS, Marketing Bulletin, 24, Technical Note 1: 1-32.
20. Fornell, C. & Larcker, D., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(3), pp.39–50. Available at:http://www.jstor.org/stable/3151312\nhttp://www.jstor.org/stable/10.2307/3151312.