Examining Construct Validity of the Student Online Learning Readiness (SOLR) Instrument Using Confirmatory Factor Analysis

Authors

  • Taeho Yu University of Virginia

DOI:

https://doi.org/10.24059/olj.v22i4.1297

Keywords:

Student readiness, online learning, factor analysis

Abstract

This study examines the construct validity of the Student Online Learning Readiness (SOLR) instrument. The SOLR instrument consists of 20 items to evaluate social competencies, communication competencies, and technical competencies in online learning. A large Midwestern university was selected to test the construct validity of the SOLR instrument. A total of 347 undergraduate students participated in this study. Confirmatory factor modeling approach was used to assess the construct validity of the SOLR instrument for this study. As a result of Confirmatory Factor Analysis (CFA), the hypothesized model of 20-item structure of the SOLR instrument was verified as a good fit for the data (χ2 (164, N=347)=1959.94, p<.001, IFI=.81, CFI=.81, GFI=.55, RMSEA=.016).

Author Biography

Taeho Yu, University of Virginia

TAEHO YU, Ph.D. Assistant Professor, Instructional Design & Technology, Department of Strategic Technologies and New Initiatives, School of Continuing and Professional Studies (SCPS), University of Virginia

References

Ali, R., & Leeds, E. (2009). The impact of classroom orientation in online student retention. Online Journal of Distance Learning Administration, 12(4). Retrieved from http://www.westga.edu/*distance/ojdla/winter124/ali124.html.

Angelina, L. M, Williams, F. K., & Natvig, D. (2007) Strategies to engage online students and reduce attrition. Journal of educators online, 4(2). 3-14.

Atchley, T., Wingenbach, G., & Akers, C. (2013). Comparison of course completion and student performance through online and traditional courses. The International Review of Research in Open and Distance Learning, 14(4). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1461

Author (2015).

Bernard, R.M., Brauer, A., Abrami, P.C., & Surkes, M. (2004). The development of a questionnaire for predicting online learning achievement. Distance Education, 25(1), 31-47.

Betermieux, S., & Heuel, E. (2009). Design and use of a web based support tool for students’ self-management in university and distance university settings. Paper presented at the World Conference on Online learning in Corporate, Government, Healthcare, and Higher Education 2009, Chesapeake, VA.

Brown, T.A. (2006). Confirmatory Factor Analysis for Applied Research, New York, NY: The Guilford Press.

Carey, J. M., (2011). Effective student outcomes: a comparison of online and face-to-face delivery modes. DEOSNEWS, 11(9). Retrieved from http://learningdesign.psu.edu/deos/deosnews11_9.pdf

Chen, X., Huang, X., Chang, L., Wang, L., & Li, D. (2010). Aggression, social competence, and academic achievement in Chinese children: a 5-year longitudinal study. Development and Psychopathology, 22(Special Issue 03), 583-592. doi:10.1017/S0954579410000295.

Cho, M.-H. (2012). Online student orientation in higher education: a development study. Educational Technology Research and Development, 60(6), 1051-1069.

Comrey, A. L. & Lee, H. B. (1992). A first course in factor analysis (2nd edition). Hillsdale, NJ: Erlbaum.

Dabbagh, N. (2007). The online learner: characteristics and pedagogical implications. Contemporary Issues in Technology and Teacher Education, 7(3), 217-226. Retrieved from http://www.citejournal.org/vol7/iss3/general/article1.cfm.

Dabbagh, N., & Bannan-Ritland, B. (2005). Online learning: concepts, strategies, and application. Upper Saddle River, NJ: Prentice Hall.

Dray, B.J., & Miszkiewicz, M. (2007). The intersection of learner characteristics and technology capabilities: implications for online learning. Paper presented at the 2007 AERA Annual Meeting, Chicago, IL.

Dray, B. J., Lowenthal, P. R., Miszkiewicz, M. J., Ruiz-Primo, M. A., & Marczynski, K. (2011). Developing an Instrument to Assess Student Readiness for Online Learning: A Validation Study. Distance Education, 32(1), 29-47.

Field, A. P. (2009). Discovering statistics using SPSS. London, England : SAGE.

Fornel, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement errors. Journal of Marketing Research, 18(2), 39–50.

Hatcher, L. (1994). A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. Cary, NC: SAS Institute, Inc.

Herrera, L., & Mendoza, N. (2011). Technological and pedagogical perceptions on b-learning from two opposite academic programs. Proceedings of the World Conference on Educational Multimedia, Hypermedia and Telecommunications 2011 (pp. 1078-1084). Chesapeake, VA: AACE.

Holder, B. (2007). An investigation of hope, academics, environment, and motivation as predictors of persistence in higher education online programs. Internet & Higher Education, 10(4), 245-260. doi:10.1016/j.iheduc.2007.08.002.

Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

Kerr, M.S., Rynearson, K., & Kerr, M.C. (2006). Student characteristics for online learning success. The Internet and Higher Education, 9(2), 91-105.

Lee, Y., & Choi, J. (2013). A structural equation model of predictors of online learning retention. The Internet and Higher Education, 16, 36-42.

Link, D. & Scholtz, S. (2000). Educational technology and faculty role: what you don’t know can hurt you. Nurse Educator, 25(6), 274-276.

Ma, W., & Yuen, A. (2010). Understanding online knowledge sharing: an exploratory theoretical framework. Proceedings of Third International Conference of Hybrid Learning (pp 239-248). Beijing, China: ICHL.

Mattice, N. J., & Dixon, P. S. (1999). Student preparedness for distance education (Research Report). Santa Clarita, CA: College of the Canyons.

McVay, M. (2001). How to be a successful distance education student: learning on the Internet. New York: Prentice Hall.

Netemeyer, R.G., Bearden, W.O., & Sharma, S. (2003). Scaling procedures: issues and applications. Thousand Oaks: Sage Publications.

Osika, E. R., & Sharp, D. P. (2002). Minimum technical competencies for distance learning students. Journal of Research on Technology in Education 34(3), 318-325.

Parker, J. D. A., Hogan, M. J., Eastabrook, J. M., Oke, A., & Wood, L. M. (2006). Emotional intelligence and student retention: predicting the successful transition from high school to university. Personality and Individual Differences, 41(7), 1329-1336.

Parnell, J.A., & Carraher, S. (2002). The role of effective resource utilization in strategy’s impact on performance. International Journal of Commerce and Management, 13(3), 1-34.

Reio, T. G., & Crim, S. J. (2006). The emergence of social presence as an overlooked factor in asynchronous online learning. Paper presented at the Academy of Human Resource Development International Conference (AHRD), Columbus, OH.

Poellhuber, B., Chomienne, M., & Karsenti, T. (2008). The effect of peer collaboration and collaborative learning on self-efficacy and persistence in a learner-paced continuous intake model. Journal of Distance Education, 22(3), 41-62.

Schreiber, J. A., Stage, F. K., King, J., Nora, A., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: a review. The Journal of Educational Research, 99, 323-337.

Selim, H. M. (2007). Critical success factors for online learning acceptance: confirmatory factor models. Computers & Education, 49, 396-413.

Shen, D., Cho, M., Tsai, C., & Marra, R. (2013). Unpacking online learning experiences: online learning self-efficacy and learning satisfaction. Internet and Higher Education, 19, 10-17.

Smith, T. C. (2005). Fifty-one competencies for online instruction. The Journal of Educators Online, 2(2), 1-18.

Tinto, V. (1975). Dropout from higher education: a theoretical synthesis of recent research. Review of Educational Research, 45(1), 89-125.

Tinto, V. (1988). Stages of student departure: reflections on the longitudinal character of student leaving. Journal of Higher Education, 59(4), 438-455.

Tinto, V. (2000). Taking retention seriously: rethinking the first year of college. NACADA Journal, 19(2), 5-10.

Tinto, V. (2005). Reflections on student retention and persistence: moving to a theory of institutional action on behalf of student success. Studies in Learning, Evaluation, Innovation and Development, 2(3), 89-97.

Tinto. V. (2006). Taking student retention seriously. Keynote presentation and paper at Maricopa Community College District, 6 January. Retrieved from http://www.umesgolf.com/assets/0/232/3812/4104/4110/bd28b4ae-e1cc-4575-9b37-535d2d2be5f1.pdf

Tinto. V. (2008). Access without support is not opportunity. Paper presented at the 36th Annual Institute for Chief Academic Officers, The Council of Independent Colleges, Seattle, Washington.

Volery, T., & Lord, D. (2000). Critical success factors in online education. The International Journal of Educational Management, 14(5), 216-223.

Watkins, R., Leigh, D., & Triner, D. (2004). Assessing Readiness for Online learning. Performance Improvement Quarterly, 17(4), 66-79.

Watulak, S. L. (2012). ‘I’m not a computer person’: negotiating participation in academic discourses. British Journal of Educational Technology, 43(1), 109-118.

Whale, D. (2006). Technology skills as a criterion in teacher evaluation. Journal of Technology and Teacher Education, 14(1), 61-74.

Williams, P. E. (2003). Roles and competencies of distance education programs in higher education institutions. The American Journal of Distance Education, 17(1), 45-57.

Wozney, L., Venkatesh, V., & Abrami, P. (2006). Implementing computer technologies: teachers’ perceptions and practices. Journal of Technology and Teacher Education, 14(1), 173-207.

Downloads

Published

2019-01-25

Issue

Section

Section II