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January 22, 2022
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December 26, 2018
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Abstract

As E-learning initiatives are increasingly being deployed in educational and corporate training settings to revamp work-place productivity through life-long learning, concerns related to instructional design quality among stakeholders are equally growing. Thus, the overriding objective of the study was to carry out initial screening and preliminary analysis of the data related to the causal influence of instructional design quality on learner satisfaction and continuance learning intention. Based on the survey design, the quantitative data were collected from 837 students across ten CISCO Networking academies in Uganda. Descriptive statistics, multiple regression and factor analysis techniques were employed to address the purpose of the study. Primary attention was paid to the assumptions of response rate, missing data, outliers, data normality, multicollinearity, homoscedasticity and common method bias. The results of the initial screening and preliminary data analysis revealed non violation of prerequisite multivariate assumptions. The findings have provided empirical evidence on the psychometric study of which the instrument can be further used for future research. The steps taken for the analysis have provided a benchmark of audit trail in the methodology and statistical analysis for the replication of the study.

Keywords

instructional design quality CISCO E-learning in Uganda learner satisfaction continuance learning intention data screening and preliminary analysis

Article Details

How to Cite
Kishabale, B., & Hassan, S. (2018). A Predictive Study on Instructional Design Quality, Learner Satisfaction and Continuance Learning Intention with E-learning Courses: Data Screening and Preliminary Analysis. Interdisciplinary Journal of Education, 1(2), 122–137. https://doi.org/10.53449/ije.v1i2.59
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References

  1. Abdulwahab, L., Dahalin, Z. M., & Galadima, M. . (2011). Data screening and preliminary analysis of the determinants of user acceptance of telecentre. Journal of Information Systems: New Paradigms, 1(1).
  2. Alanazi, A. A. (2016). Linking organisational culture, leadership styles, human resource management practices and organisational performance: Data screening and preliminary analysis. American Journal of Management, 16(1), 70–80.
  3. Aliyu, A. A., Rosmain, T., & Takala, J. (2014). Online banking and customer service delivery in Malaysia: Data screening and preliminary findings. Procedia - Social and Behavioral Sciences, 129, 562–570.
  4. Ally, M. (2004). Foundations of educational theory for online learning. In E. Terry, Anderson, Fathi (Ed.), Theory and practice of online learning (pp. 3–32). Athabasca University.
  5. ASTD. (2001). A vision of e-learning for America’s workforce: Report of the Commission on Technology and Adult Learning. American Society for Training and Development and National Governors’ Association.
  6. Bennett, D. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25(5), 464–469.
  7. Bhattacherjee, A. (2001). Understanding information systems continuance: An Expectation-Confirmaion model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
  8. Bhattacherjee, A., Perols, J., & Sanford, C. (2008). Information technology continuance: A theoretical extension and empirical test. Journal of Computer Information Systems, 49(1), 17–26. https://doi.org/Article
  9. Chatman, S. (2007). Overview of University of California Undergraduate Experience Survey ( UCUES ) response rates and bias issues. California: Center for Studies in Higher Education.
  10. Clawson, S. (2007). Does quality matter? Measuring whether online course quality standards are predictive of student satisfaction in higher education. Capella University. Retrieved from https://search.proquest.com/pqdtglobal/docview/304699277/fulltextPDF/DC894EB13A364C78PQ/1?accountid=44024
  11. Cruz, D. (2008). Application of data screening procedures in stress research. The New School Psychology Bulletin, 5(2), 41–45.
  12. Gaskin, J. (2017). Common Method Bias (CMB). Retrieved from http://statwiki.kolobkreations.com/index.php?title=Confirmatory_Factor_Analysis#Common_Method_Bias_.28CMB.29
  13. Georgiadou, E., Economides, A., Michailidou, A., & Mosha, A. (2001). Evaluation of educational software designed for the purpose of teaching programming. In Proceedings of the 9th SchoolNet 2001 International Conference on Computers in Education (pp. 745–752).
  14. Hair, J., Hult, T. G. ., Ringle, C., & Sarstedt, M. (2013). A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (1st Editio). London: Sage Publications.
  15. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). New York: Macmillan.
  16. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2016). Multivariate Data Analysis. New York: Macmillan.
  17. Ibrahim, M. A., & Mohd Noor, S. (2014). Strategic orientation, access to finance, business environment and SMEs performance in Nigeria : Data screening and preliminary analysis. European Journal of Business and Management ISSN, 6(35), 124–132.
  18. INACOL. (2011). National Standards for Quality Online Courses National Standards for Quality Online Courses (Version 2). National Association for K-12 Online Learning. Retrieved from https://www.inacol.org/wp-content/uploads/2015/02/national-standards-for-quality-online-courses-v2.pdf
  19. Karla, G. (2016). The golden principles of high-quality instructional design. Retrieved from https://www.shiftelearning.com/blog/bid/344888/the-golden-rules-of-high-quality-instructional-design
  20. Karuthan, C. (2016). Questionaire design and validation. Kuala Lumpur: Center of Regulatory Studies, University Malaya.
  21. Kim, H. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. The Korean Academy of Conservative Dentistry.
  22. Kline, R. (2016). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press.
  23. Kura, K. M., Faridahwati, M. S., & Chauhan, A. (2014). Influence of organisational formal control, group norms, self-regulatory efficacy on workplace deviance in the Nigerian universities: Data screening and preliminary analysis. 7th National Human Resource Management Conference, (May), 1–9.
  24. Maiyaki, A. A. (2012). Influence of service quality, corporate image and perceived value on customer behavioral responses : CFA and measurement model. International Journal of Academic Research in Business and Social Sciences, 2(1). Retrieved from http://www.hrmars.com/admin/pics/535.pdf
  25. Matsunaga, M. (2011). How to factor-analyze your data right: Do’s, don’ts, and how-to’s. International Journal of Psychological Research, 3(1), 97–110.
  26. Mu’azu, B. S., & Siti, Z. S. (2014). Internal Audit Effectiveness: Data Screening and Preliminary Analysis. Asian Social Science, 10(10), 76–85. https://doi.org/10.5539/ass.v10n10p76
  27. Nordin, M. S., Ahmad, T. B. T., Zubairi, A. M., Ismail, N. A. H., Rahman, A. H. A., Trayek, F. A. A., & Ibrahim, M. B. (2016). Psychometric properties of a digital citizenship questionnaire. International Education Studies, 9(3), 71.
  28. Pallant, J. (2007). SPSS survival manual. Sydney: Allen & Unwin.
  29. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.
  30. Pryce, G. (2002). Heteroscedasticity: Testing and correcting in SPSS. Glasgow: University of Glasgow.
  31. Quality Matters Program. (2013). Quality Matters Rubric Standards 2011 - 2013 edition. Maryland Online Inc. Retrieved from www.qmprogram.org
  32. Schafer, J. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8, 3–15.
  33. Sekaran, U., & Bougie, R. (2010). Research methods for business: A skill building approach. John Wiley & Sons. Inc.
  34. Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (5th ed.). New York: Harper Collins.
  35. Verardi, V., & Croux, C. (2009). Robust regression in Stata. The Stata Journal, 9(3), 439–453.
  36. Wang, Y.-S., Wang, H.-Y., & Shee, D. Y. (2007). Measuring e-learning systems success in an organizational context: Scale development and validation. Computers in Human Behavior, 23(4), 1792–1808. https://doi.org/10.1016/j.chb.2005.10.006
  37. Won, N. C., Wan, C. Y., & Sharif, M. Y. (2017). Effect of leadership styles, social capital, and social entrepreneurship on organizational effectiveness of social welfare organization in Malaysia: Data screening and preliminary analysis. International Review of Management and Marketing, 7(2), 117–122.
  38. Yong, A. G., & Pearce, S. (2013). A Beginner’s guide to factor analysis : Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology, 9(2), 79–94.