Graduate research (Ph.D.) students very often face the dilemma of whether to use EFA along with CFA (or CCA) while conducting measurement model analysis in SEM in basic business research with perception-based latent variables. Finally, there comes an article (reference given below) that discusses in detail about these construct measurement concepts from PLS-SEM and CB-SEM perspectives.
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in pls-sem using confirmatory composite analysis. Journal of Business Research, 109, 101-110. doi:10.1016/j.jbusres.2019.11.069
I hope this well-written paper would now serve as a primary reference for all those who seek emancipation from the never-ending (long-created) confusion about the use of EFA/CFA/CCA while conducting measurement model analysis, especially in SEM (PLS-SEM and CB-SEM).
I hope respected business research scholars would enlighten further our thought on the matter by providing their insights on the topic.
Best regards.
The AFE allows to generate structures of theoretical models and hypotheses that can be empirically tested (Gorsuch, 1983), without having previous specifications of the model or considering both the number of factors and the relationship between them (Gerbin & Hamilton, 1996).
The technique used by the AFE is to automatically extract the factors with a certain statistical criterion, obtaining the simplest factor structure in terms of its easiest and most significant interpretation (Thurstone, 1931 and 1947).
Confirmatory Factor Analysis (AFC)
The AFC allows to correct or corroborate, if any, the deficiencies of the AFE leading to a greater contrast of the specified hypotheses (Bollen, 1989), in the same way it analyzes the matrix of covariances instead of correlations, which helps establish whether the indicators are equivalent (Batista and Coenders, 1998).
The AFC is represented by flowcharts (path diagram), according to their particular specifications. The rectangles represent the items and ellipses, the common factors. Unidirectional arrows between common factors and items express saturation and bidirectional arrows indicate the correlation between common or unique factors (Jöreskog, 1969).
In conclusion, these models provide the appropriate statistical framework to assess the validity and reliability of each item instead of performing a global analysis (Batista, Coenders and Alonso, 2004), helping the researcher to optimize both the construction of a measuring instrument and The analysis of results.
In the AFC it is necessary to observe the factor loads that allow establishing the correlation between the variables and the factors (Garson, 2012). The closer they get to one, the greater the correlation will be. An empirical rule in the AFC states that the charges must be> = a 0.07. Although it is high and some factors and variables may be left out of the model, these should be taken at the discretion of the researcher, (Raubenheimer, 2004).
Taking the criteria of Garson (2012), Raubenheimer (2004) and Widaman (1993) in which they establish that, in order to eliminate collinearity between variables to the maximum, it is necessary to take the variables whose loads are> = 0.07, especially in the case of a Model proposal, in which case, its construction is based on the existing theory.
For me it is more appropriate to use EFA while testing the questionnaire for the first time whereas AMOS SEM measurement model is more tilted towards CFA with the already developed questionnaire.
I absolutely agree with you Mr Mukaram Ali Khan and thank you for sharing your knowledge. Regards
Hey
actually the article does not provide a lot of new insights, it simply provides existing evaluation criteria/rules of thumb that are known in PLS-SEM for a while, see e.g. Hair et al (2019), and names them CCA. Moreover, this article creates a lot of confusion as it names the evaluation steps of PLS-SEM after a method that already exists, namely confirmatory composite analysis (Schuberth et al., 2018), which is very different from the CCA introduced by Hair et al..
Best regards,
Florian
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review
Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). Confirmatory composite analysis. Frontiers in psychology, 9, 2541.
Thank you Dr. Florian Schuberth for putting light on the issue from a different angle. I would also try to find "confusions" in the paper mentioned above and would read Schuberth et al. as well. Best regards
Article PLS-SEM or CB-SEM: updated guidelines on which method to use
Article Estimation issues with PLS and CBSEM: Where the bias lies!
Dear all,
please see the following article who compares CCA as originally developed and the measurement evaluation step knwon from PLS-SEM:
https://link.springer.com/article/10.1007%2Fs11846-020-00405-0
Best regards,
Florian
Thank you Dear Dr. Florian Schuberth for sharing your article on CCA. It is a good comparative reference in this context.
Dr Florian Schuberth , in the item development process, if there is lacking of prior theory to support the dimension of the new measure, is it possible to use EFA for factor structure and followed by CCA at the later stage? Thanks.
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