A Construct Revalidation of the Community of Inquiry Survey: Empirical Evidence for a General Factor Under a Bifactor Structure

  • Hongwei Yang, Ph.D. University of West Florida
  • Jian Su, Ph.D. University of Tennessee
Keywords: community of inquiry, teaching presence, social presence, cognitive presence, bifactor model, construct revalidation

Abstract

The study revisited the community of inquiry (CoI) instrument for construct revalidation. To that end, the study used confirmatory factor analysis (CFA) to examine four competing models (unidimensional, correlated-factor, second-order factor, and bifactor models) on model fit statistics computed using parameter estimates from a statistical estimator for ordinal categorical data. The CFA identified as the optimal structure the bifactor model where all items loaded on their intended domains and the existence of the general factor was supported, essentially evidence of construct validity for the instrument. The study further examined the bifactor model using mostly model-based reliability measures. The findings confirmed the contributions of the general factor to the reliability of instrument scores. The study concluded with validity and reliability evidence for the bifactor model, supported the model as a valid and reliable representation of the CoI instrument and a fuller representation of the CoI theoretical framework, and recommended its use in CoI-related research and practice in online education.

Author Biographies

Hongwei Yang, Ph.D., University of West Florida

Hongwei Yang, Ph.D. is an assistant professor in the School of Education, College of Education and Professional Studies at the University of West Florida. His scholarship is found in online teaching and learning, family science, human and animal medical care, survey research, quantitative methodology, and psychometrics.

Jian Su, Ph.D., University of Tennessee

Jian Su, Ph.D. is an Instructional Designer/Developer in the Office of Online Learning and Academic Programs at the University of Tennessee. Her research interests include online teaching and learning, Open Educational Resources, and digital distractions.

References

Arbaugh, B., Cleveland-Innes, M., Diaz, S., Garrison, D. R., Ice, P., Richardson, J. C., & Swan, K. P. (2008). Developing a community of inquiry instrument: Testing a measure of the community of inquiry framework using a multi-institutional sample. The Internet and Higher Education, 11(3–4), 133–136. https://doi.org/10.1016/j.iheduc.2008.06.003

Bangert, A. W. (2009). Building a validity argument for the community of inquiry survey instrument. The Internet and Higher Education, 12(2), 104–111. https://doi.org/10.1016/j.iheduc.2009.06.001.

Byrne, B. M. (2010). Structural equation modeling with AMOS (2nd ed.). Routledge.

Caskurlu, S. (2018). Confirming the subdimensions of teaching, social, and cognitive presences: A construct validity study. The Internet and Higher Education, 39, 1–12. https://doi.org/10.1016/j.iheduc.2018.05.002

Chen, F. F., Hayes, A., Carver, C. S., Laurenceau, J. P., & Zhang, Z. (2012). Modeling general and specific variance in multifaceted constructs: A comparison of the bifactor model to other approaches. Journal of Personality, 80(1), 219–251. https://doi.org/10.1111/j.1467-6494.2011.00739.x

DeMars, C. E. (2013). A tutorial on interpreting bifactor model scores. International Journal of Testing, 13, 354–378. https://doi.org/10.1080/15305058.2013.799067

Dempsey, P. R., & Zhang, J. (2019). Re-examining the construct validity and causal relationships of teaching, cognitive, and social presence in community of inquiry framework. Online Learning, 23(1), 62–79. http://dx.doi.org/10.24059/olj.v23i1.1419

Díaz, S. R., Swan, K., Ice, P., & Kupczynski, L. (2010). Student ratings of the importance of survey items, multiplicative factor analysis, and the validity of the community of inquiry survey. The Internet and Higher Education, 13(1–2), 22–30. https://doi.org/10.1016/j.iheduc.2009.11.004

DiStefano, C., & Hess, B. (2005). Using confirmatory factor analysis for construct validation: An empirical review. Journal of Psychoeducational Assessment, 23(3), 225–241. https://doi.org/10.1177/073428290502300303

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2–3), 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6

Garrison, D. R., & Arbaugh, J. B. (2007). Researching the community of inquiry framework: Review, issues, and future directions. The Internet and Higher Education, 10, 157–172. https://doi.org/10.1016/j.iheduc.2007.04.001

Garrison, D. R., Cleveland-Innes, M., & Fung, T. S. (2010). Exploring causal relationships among teaching, cognitive and social presence: Student perceptions of the community of inquiry framework. The Internet and Higher Education, 13(1-2), 31–36. https://doi.org/10.1016/j.iheduc.2009.10.002

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

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

Kovanović, V., Joksimović, S., Poquet, O., Hennis, T., Čukić, I., de Vries, P., Hatala, M., Dawson, S., Siemens, G., & Gašević, D. (2018). Exploring communities of inquiry in massive open online courses. Computers & Education, 119, 44–58. https://doi.org/10.1016/j.compedu.2017.11.010

Kozan, K. (2016). The incremental predictive validity of teaching, cognitive and social presence on cognitive load. The Internet and Higher Education, 31, 11–19. https://doi.org/10.1016/j.iheduc.2016.05.003

Kozan, K., & Caskurlu, S. (2018). On the nth presence for the community of inquiry framework. Computers & Education, 122, 104–118. https://doi.org/10.1016/j.compedu.2018.03.010

Kozan, K., & Richardson, J. C. (2014). New exploratory and confirmatory factor analysis insights into the community of inquiry survey. The Internet and Higher Education, 23, 39–47. https://doi.org/10.1016/j.iheduc.2014.06.002

Ma, Z., Wang, J., Wang, Q., Kong, L., Wu, Y., & Yang, H. (2017). Verifying causal relationships among the presences of the community of inquiry framework in the Chinese context. International Review of Research in Open and Distributed Learning, 18(6). https://doi.org/10.19173/irrodl.v18i6.3197

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

Mundfrom, D. J., Shaw, D. G., & Ke, T. L. (2005). Minimum sample size recommendations for conducting factor analyses. International Journal of Testing, 5(2), 159–168. https://doi.org/10.1207/s15327574ijt0502_4

Olpak, Y. Z., & Kiliç Çakmak, E. (2016). Examining the reliability and validity of a Turkish version of the community of inquiry survey. Online Learning, 22(1), 147–161. http://dx.doi.org/10.24059/olj.v22i1.990

Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696. https://doi.org/10.1080/00273171.2012.715555

Reise, S. P., Moore, T. M., & Haviland, M. G. (2010). Bifactor models and rotations: Exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment, 92(6), 544–559. https://doi.org/10.1080/00223891.2010.496477

Reise, S. P., Morizot, J., & Hays, R. D. (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research, 16(1), 19–31. https://doi.org/10.1007/s11136-007-9183-7

Rockinson-Szapkiw, A., Wendt, J., Whighting, M., & Nisbet, D. (2016). The predictive relationship among the community of inquiry framework, perceived learning and online, and graduate students’ course grades in online synchronous and asynchronous courses. International Review of Research in Open and Distributed Learning, 17(3). https://doi.org/10.19173/irrodl.v17i3.2203

Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016a). Applying bifactor statistical indices in the evaluation of psychological measures. Journal of Personality Assessment, 98(3), 223–237. https://doi.org/10.1080/00223891.2015.1089249

Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016b). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21(2), 137–150. https://doi.org/10.1037/met0000045

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

Shea, P., & Bidjerano, T. (2009). Community of inquiry as a theoretical framework to foster “epistemic engagement” and “cognitive presence” in online education. Computers & Education, 52, 543–553. https://doi.org/10.1016/j.compedu.2008.10.007

Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55(4), 1721–1731. https://doi.org/10.1016/j.compedu.2010.07.017

Stenbom, S. (2018). A systematic review of the community of inquiry survey. The Internet and Higher Education, 39, 22–32. https://doi.org/10.1016/j.iheduc.2018.06.001

Swan, K., & Ice, P. (2010). The community of inquiry framework 10 years later: Introduction to the special issue. The Internet and Higher Education, 13(1–2), 1–4. http://dx.doi.org/10.1016/j.iheduc.2009.11.003

Swan, K. P., Richardson, J. C., Ice, P., Garrison, D. R., Cleveland-Innes, M., & Arbaugh, J. B. (2008). Validating a measurement tool of presence in online communities of inquiry. E-mentor, 2(24), 1–12. http://www.e-mentor.edu.pl/_xml/wydania/24/543.pdf

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.

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(1), 409–428. https://doi.org/10.3758/s13428-018-1055-2

Yu, T., & Richardson, J. C. (2015). Examining reliability and validity of a Korean version of the community of inquiry instrument using exploratory and confirmatory factor analysis. The Internet and Higher Education, 25, 45–52. https://doi.org/10.1016/j.iheduc.2014.12.004

Published
2021-11-22
How to Cite
Yang, Ph.D., H., & Su, Ph.D., J. (2021). A Construct Revalidation of the Community of Inquiry Survey: Empirical Evidence for a General Factor Under a Bifactor Structure. The International Review of Research in Open and Distributed Learning, 22(4), 22-40. https://doi.org/10.19173/irrodl.v22i4.5587
Section
Research Articles