A Categorical Confirmatory Factor Analysis for Validating the Turkish Version of the Self-Directed Online Learning Scale (SDOLS-T)
DOI:
https://doi.org/10.19173/irrodl.v26i1.7106Keywords:
self-directed learning, online teaching and learning, confirmatory factor analysis, ordered categorical data, measurement invarianceAbstract
This study developed and validated the Turkish version of the Self-Directed Online Learning Scale (SDOLS-T) for assessing students’ perceptions of their self-directed learning (SDL) ability in an online environment. Specifically, this study conducted in two stages multiple categorical confirmatory factor analyses factoring in the ordered categorical structure of the SDOLS-T data. The data in this study came from a parent study which utilized the SDOLS-T and other instruments for data collection. From among the three competing models the literature recommends examining to explain the shared variance of items in a survey, the results at stage 1 showed that the correlated, two-factor structure, originally proposed for the SDOLS, was also the best-fit model for the SDOLS-T. At stage 2, using the best-fit model from stage 1, measurement invariance analyses were conducted to examine the extent to which SDL under the SDOLS-T was understood and measured equivalently across the groups specified by four dichotomous demographic variables: gender, network connection, online learning experience, and grade. The stage 2 results indicate the SDOLS-T reached scalar invariance at least for gender and network connection, thus allowing the comparison of latent or manifest means, or any other scores (e.g., total scores, Rasch scores), across the groups by these two demographic variables. In the end, the findings support the SDOLS-T for use in facilitating educational practice (e.g., improving instructional design), advancing scholarly literature (e.g., investigating SDL measurement and content area issues), and informing policy/decision-making (e.g., increasing retention rates and reducing dropout) in online education in Turkey.
References
Abuhammad, S. (2020). Barriers to distance learning during the COVID-19 outbreak: A qualitative review from parents’ perspective. Heliyon, 6(11), Article e05482. https://doi.org/10.1016/j.heliyon.2020.e05482
Ahmad, B. E., & Majid, F. A. (2010). Self-directed learning and culture: A study on Malay adult learners. Procedia - Social and Behavioral Sciences, 7, 254–263. https://doi.org/10.1016/j.sbspro.2010.10.036
Ahmad, B. E., Ozturk, M., Baharum, M. A. A., & Majid, F. A. (2019). A comparative study on the relationship between self-directed learning and academic achievement among Malaysian and Turkish undergraduates. Gading Journal for Social Sciences, 21(1), 1–11. https://ir.uitm.edu.my/id/eprint/29240/1/29240.pdf
Aşkın, İ. (2015). An investigation of self-directed learning skills of undergraduate students [Unpublished doctoral dissertation]. Hacettepe University.
Aşkın Tekkol, İ., & Demirel, M. (2018). Self-Directed Learning Skills Scale: Validity and reliability study. Journal of Measurement and Evaluation in Education and Psychology, 9(2), 85–100. https://doi.org/10.21031/epod.389208
Ates Cobanoglu, A. & Cobanoglu, I. (2021). Do Turkish student teachers feel ready for online learning in post-COVID times? A study of online learning readiness. Turkish Online Journal of Distance Education, 22(3), 270–280. https://doi.org/10.17718/tojde.961847
Balcı, T., Temiz, C. N., & Sivrikaya, A. H. (2021). Transition to distance education in COVID-19 period: Turkish pre-service teachers’ e-learning attitudes and readiness levels. Journal of Educational Issues, 7(1), 296–323. https://doi.org/10.5296/jei.v7i1.18508
Bond, T. G., & Fox, C. M. (2015). Applying the Rasch model (3rd ed.). Routledge.
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
Byrne, B. M. (2010). Structural equation modeling with AMOS (2nd ed.). Routledge.
Cadorin, L., Bortoluzzi, G., & Palese, A. (2013). The Self-Rating Scale of Self-Directed Learning (SRSSDL): A factor analysis of the Italian version. Nurse Education Today, 33(12), 1511–1516. https://doi.org/10.1016/j.nedt.2013.04.010
Cadorin, L., Bressan, V., & Palese, A. (2017). Instruments evaluating the self-directed learning abilities among nursing students and nurses: A systematic review of psychometric properties. BMC Medical Education, 17, Article 229. https://doi.org/10.1186/s12909-017-1072-3
Caffarella, R. S. (1993). Self-directed learning. New Directions for Adult and Continuing Education, 57, 25–35. https://doi.org/10.1002/ace.36719935705
Çelik, K., & Arslan, S. (2016). Turkish adaptation and validation of Self-Directed Learning Inventory. International Journal of New Trends in Arts, Sports & Science Education (IJTASE), 5(1), 19–25. http://www.ijtase.net/index.php/ijtase/article/view/207
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834
Chen, F. F. (2008). What happens if we compare chopsticks with forks? The impact of making inappropriate comparisons in cross-cultural research. Journal of Personality and Social Psychology, 95(5), 1005–1018. https://doi.org/10.1037/a0013193
Cheng, S.-F., Kuo, C.-L., Lin, K.-C., & Lee-Hsieh, J. (2010). Development and preliminary testing of a self-rating instrument to measure self-directed learning ability of nursing students. International Journal of Nursing Studies, 47(9), 1152–1158. https://doi.org/10.1016/j.ijnurstu.2010.02.002
Cheung, G. W. & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5
Daily Sabah. (2021, March 20). Turkey’s remote education is project for future: Minister Selçuk. https://www.dailysabah.com/turkey/education/turkeys-remote-education-is-project-for-future-minister-selcuk
Demircioğlu, Z. I., Öge, B., Fuçular, E. E., Çevik, T., Nazligül, M. D., & Özçelik, E. (2018). Reliability, validity and Turkish adaptation of Self-Directed Learning Scale (SDLS). International Journal of Assessment Tools in Education, 5(2), 235–247. https://doi.org/10.21449/ijate.401069
Durnali, M. (2020). The effect of self-directed learning on the relationship between self-leadership and online learning among university students in Turkey. Tuning Journal for Higher Education, 8(1), 129–165. https://doi.org/10.18543/tjhe-8(1)-2020pp129-165
Edmondson, D. R., Boyer, S. L., & Artis, A. B. (2012). Self-directed learning: A meta-analytic review of adult learning constructs. International Journal of Educational Research, 7(1), 40–48. http://debdavis.pbworks.com/w/file/fetch/96898755/edmondson%20boyer%20artis%20--%20selfdirected%20learning%20a%20meta-analytic%20review.pdf
Ertuğ, N., & Faydali, S. (2018). Investigating the relationship between self-directed learning readiness and time management skills in Turkish undergraduate nursing students. Nursing Education Perspectives, 39(2), E2–E5. https://doi.org/10.1097/01.NEP.0000000000000279
Fisher, M., King, J., & Tague, G. (2001). Development of a self-directed learning readiness scale for nursing education. Nurse Education Today, 21(7), 516–525. https://doi.org/10.1054/nedt.2001.0589
Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly, 48(1), 18–33. https://doi.org/10.1177/074171369704800103
Gignac, G. E., & Kretzschmar, A. (2017). Evaluating dimensional distinctness with correlated-factor models: Limitations and suggestions. Intelligence, 62, 138–147. https://doi.org/10.1016/j.intell.2017.04.001
Guglielmino, L. M. (1977). Development of the Self-Directed Learning Readiness Scale [Unpublished doctoral dissertation]. University of Georgia.
Hiemstra, R. (1994). Self-directed learning. In T. Husen & T. N. Postlethwaite (Eds.), The International Encyclopedia of Education (2nd ed.). Pergamon Press.
Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157–168. https://doi.org/10.1016/j.compedu.2016.03.016
Hung, M.-L., Chou, C., Chen, C.-H., & Own, Z- Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 55(3), 1080–1090. https://doi.org/10.1016/j.compedu.2010.05.004
İlhan, M., & Çetin, B. (2013). Çevrimiçi Öğrenmeye Yönelik Hazır Bulunuşluk Ölçeği’nin (ÇÖHBÖ) Türkçe Formunun Geçerlik ve Güvenirlik Çalışması [The validity and reliability study of the Turkish version of an Online Learning Readiness Scale]. Eğitim Teknolojisi Kuram ve Uygulama, 3(2), 72–101. https://dergipark.org.tr/tr/pub/etku/issue/6269/84216
Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2022). semTools: Useful tools for structural equation modeling (R package version 0.5-6) [Computer software]. https://CRAN.R-project.org/package=semTools
Jung, O. B., Lim, J. H., Jung, S. H., Kim, L. G., & Yoon, J. E. (2012). The development and validation of a self-directed learning inventory for elementary school students. The Korean Journal of Human Development, 19(4), 227–245.
Kara, M. (2022) Revisiting online learner engagement: Exploring the role of learner characteristics in an emergency period. Journal of Research on Technology in Education, 54(sup1), S236–S252. https://doi.org/10.1080/15391523.2021.1891997
Karatas, K. & Arpaci, I. (2021). The role of self-directed learning, metacognition, and 21st century skills predicting the readiness for online learning. Contemporary Educational Technology, 13(3), Article ep300. https://doi.org/10.30935/cedtech/10786
Karagülle, S., & Berkant, H. G. (2022). Examining self-managed learning skills and thinking styles of university students. Gazi University Journal of Gazi Education Faculty, 42(1), 669–710. https://doi.org/10.17152/gefad.1005908
Khiat, H. (2015). Measuring self-directed learning: A diagnostic tool for adult learners. Journal of University Teaching & Learning Practice, 12(2), Article 2. https://doi.org/10.53761/1.12.2.2
Kidane, H. H., Roebertsen, H., & van der Vleuten, C. P. M. (2020). Students’ perceptions towards self-directed learning in Ethiopian medical schools with new innovative curriculum: A mixed-method study. BMC Medical Education, 20, Article 7. https://doi.org/10.1186/s12909-019-1924-0
Kim, E. S., & Yoon, M. (2011) Testing measurement invariance: A comparison of multiple-group categorical CFA and IRT. Structural Equation Modeling: A Multidisciplinary Journal, 18(2), 212–228. https://doi.org/10.1080/10705511.2011.557337
Kim, R., Olfman, L., Ryan, T., & Eryilmaz, E. (2014). Leveraging a personalized system to improve self-directed learning in online educational environments. Computers & Education, 70, 150–160. https://doi.org/10.1016/j.compedu.2013.08.006
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. Prentice Hall/Cambridge.
Kocaman, G., Dicle, A., Üstün, B., & Çimen, S. (2006). Kendi kendine ög˘ renmeye hazırolus¸ ölçeg˘ i: Geçerlilik güvenirlik çalıs¸ması [Self-Directed Learning Readiness Scale: Validity and reliability study; Paper presentation]. Dokuz Eylül University III. Active Education Congress, lzmir, Turkey.
Long, H. B. (1977). [Review of the book Self-directed learning: A guide for learners and teachers, by M. S. Knowles.]. Group & Organization Studies, 2(2), 256–257. https://doi.org/10.1177/105960117700200220
Lounsbury, J. W., & Gibson, L.W. (2006). Personal style inventory: A personality measurement system for work and school settings. Resource Associates.
Lounsbury, J. W., Levy, J. J., Park, S.-H., Gibson, L. W., & Smith, R. (2009). An investigation of the construct validity of the personality trait of self-directed learning. Learning and Individual Differences, 19(4), 411–418. https://doi.org/10.1016/j.lindif.2009.03.001
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130
Meade, A. W., Lautenschlager, G. J., & Johnson, E. C. (2007). A Monte Carlo examination of the sensitivity of the differential functioning of items and tests framework for tests of measurement invariance with Likert data. Applied Psychological Measurement, 31(5), 430–455. https://doi.org/10.1177/0146621606297316
Millsap, R. E. (2011). Statistical approaches to measurement invariance. Routledge.
Millsap, R. E., & Cham, H. (2013). Investigating factorial invariance in longitudinal data. In B. Laursen, T. D. Little, & N. A. Card (Eds.), Handbook of developmental research methods (pp. 109–148). Guilford Press.
Ozer, O., & Yukselir, C. (2021). “Am I aware of my roles as a learner?” The relationships of learner autonomy, self-direction and goal commitment to academic achievement among Turkish EFL learners. Language Awareness, 32(1), 19–38. https://doi.org/10.1080/09658416.2021.1936539
Peker Ünal, D. (2022). The predictive power of the problem-solving and emotional intelligence levels of prospective teachers on their self-directed learning skills. Psycho-Educational Research Reviews, 11(1), 46–58. https://doi.org/10.52963/PERR_Biruni_V11.N1.04
Polat, E., Hopcan, S., & Yahşi, Ömer. (2022). Are K–12 teachers ready for e-learning? The International Review of Research in Open and Distributed Learning, 23(2), 214–241. https://doi.org/10.19173/irrodl.v23i2.6082
Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G., & Hanson, J. (2016). Attitude, digital literacy and self-efficacy: Flow-on effects for online learning behavior. The Internet and Higher Education, 29, 91–97. https://doi.org/10.1016/j.iheduc.2016.01.001
Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004
Republic of Turkey Ministry of National Education. (n.d.). Turkey’s education vision 2023. https://planipolis.iiep.unesco.org/sites/default/files/ressources/turkey_education_vision_2023.pdf
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02
Saritepeci, M., & Orak, C. (2019). Lifelong learning tendencies of prospective teachers: Investigation of self-directed learning, thinking styles, ICT usage status and demographic variables as predictors. Bartın University Journal of Faculty of Education, 8(3), 904–927. https://doi.org/10.14686/buefad.555478
Sass, D. A. (2011). Testing measurement invariance and comparing latent factor means within a confirmatory factor analysis framework. Journal of Psychoeducational Assessment, 29(4), 347–363. https://doi.org/10.1177/0734282911406661
Sawatsky, A. (2017, May 22). Instruments for measuring self-directed learning and self-regulated learning in health professions education: A systematic review [Paper presentation]. Society of Directors of Research in Medical Education (SDRME) 2017 Annual Meeting, Minneapolis, MN, United States.
Schmitt, N., & Kuljanin, G. (2008). Measurement invariance: Review of practice and implications. Human Resource Management Review, 18(4), 210–222. https://doi.org/10.1016/j.hrmr.2008.03.003
Schulze, A. S. (2014). Massive open online courses (MOOCs) and completion rates: Are self-directed adult learners the most successful at MOOCs? [Doctoral dissertation, Pepperdine University]. Pepperdine Digital Commons. https://digitalcommons.pepperdine.edu/cgi/viewcontent.cgi?article=1441&context=etd
Song, L., & Hill, J. R. (2007). A conceptual model for understanding self-directed learning in online environments. Journal of Interactive Online Learning, 6(1), 27–42. https://www.ncolr.org/jiol/issues/pdf/6.1.3.pdf
Su, J. (2016). Successful graduate students’ perceptions of characteristics of online learning environments (Unpublished doctoral dissertation). The University of Tennessee, Knoxville, TN.
Suh, H. N., Wang, K. T., & Arterberry, B. J. (2015). Development and initial validation of the Self-Directed Learning Inventory with Korean college students. Journal of Psychoeducational Assessment, 33(7), 687–697. https://doi.org/10.1177/0734282914557728
Sun, W., Hong, J.-C., Dong, Y., Huang, Y., & Fu, Q. (2022). Self-directed learning predicts online learning engagement in higher education mediated by perceived value of knowing learning goals. The Asia-Pacific Education Researcher, 32, 307–316. https://doi.org/10.1007/s40299-022-00653-6
Svetina, D., Rutkowski, L., & Rutkowski, D. (2020). Multiple-group invariance with categorical outcomes using updated guidelines: An illustration using Mplus and the lavaan/semTools packages. Structural Equation Modeling: A Multidisciplinary Journal, 27(1), 111–130. https://doi.org/10.1080/10705511.2019.1602776
Tekkol, İ. A., & Demirel, M. (2018). An investigation of self-directed learning skills of undergraduate students. Frontiers in Psychology, 9, Article 2324. https://doi.org/10.3389/fpsyg.2018.02324
Thompson, M. S., & Green, S. B. (2013). Evaluating between-group differences in latent variable means. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 163–218). Information Age Publishing.
Ünsal Avdal, E. (2013). The effect of self-directed learning abilities of student nurses on success in Turkey. Nurse Education Today, 33(8), 838–841. https://doi.org/10.1016/j.nedt.2012.02.006
West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). Guilford Press.
Widaman, K. F., Ferrer, E., & Conger, R. D. (2010). Factorial invariance within longitudinal structural equation models: Measuring the same construct across time. Child Development Perspectives, 4(1), 10–18. https://doi.org/10.1111/j.1750-8606.2009.00110.x
Wu, H., & Estabrook, R. (2016). Identification of confirmatory factor analysis models of different levels of invariance for ordered categorical outcomes. Psychometrika, 81, 1014–1045. https://doi.org/10.1007/s11336-016-9506-0
Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Methods, 51, 409–428. https://doi.org/10.3758/s13428-018-1055-2
Xie, X., Siau, K., & Nah, F. F.-H. (2020). COVID-19 pandemic—Online education in the new normal and the next normal. Journal of Information Technology Case and Application Research, 22(3), 175–187. https://doi.org/10.1080/15228053.2020.1824884
Yang, H., Su, J., & Bradley, K. D. (2020). Applying the rasch model to evaluate the self-directed online learning scale (SDOLS) for graduate students. The International Review of Research in Open and Distributed Learning, 21(3), 99–120. https://doi.org/10.19173/irrodl.v21i3.4654
Yurdugül, H., & Alsancak Sırakaya, D. A. (2013). The scale of online learning readiness: A study of validity and reliability. Education and Science, 38(169), 391–406. https://www.proquest.com/openview/c6b0a56d9385205d7bf3759a9bedd68d/1
Yurdugül, H., & Demir, Ö. (2017). An investigation of pre-service teachers’ readiness for e-learning at undergraduate level teacher training programs: The case of Hacettepe University. Hacettepe University Journal of Education, 32(4), 896–915. https://doi.org/10.16986/HUJE.2016022763
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License. The copyright for all content published in IRRODL remains with the authors.
This copyright agreement and usage license ensure that the article is distributed as widely as possible and can be included in any scientific or scholarly archive.
You are free to
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms below:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
