How can I detect common method bias in smartpls? Can you suggest some guidelines?
For PLS-SEM, common method bias (CMB) is detected through a full Collinearity assessment approach (Kock, 2015). VIF values should be lower than the 3.3 threshold (Hair et al., 2017, Kock, 2015). This is indicative that the model is free from common method bias. Any value greater than 3.3 means the model is affected by CMB.
smartpls do not provide common method bias method..so, you can use Harman single factor using spss for that particular method
Dear Izdwan,
You can refer this ref. : Sattler, H., Volckner, F., riediger, C. & Ringle, C. M. (2010). The Impact of Brand Extension Success Factors on Brand Extension Price Premium. International Journal of Research in Marketing, 27(4) 319-328, as a guide...
Tq
Hi Mohd,
You might want to take a look on answers shared in this RG post:
https://www.researchgate.net/post/Does_anyone_know_what_is_the_purpose_of_common_method_bias_in_structural_equation_modeling
Regards,
Fung
Hi folks,
unfortunately, neither the Harman test nor the common factor method allows to eliminate common method bias if it is present, see
Conway, J. M., & Lance, C. E. (2010). What reviewers should expect from authors regarding common method bias in organizational research. Journal of Business Psychology, DOI 10.100.
Richardson, H. A., Simmering, M. J., & Sturman, M. C. (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods, 12(4), 762–800.
A possible solution is to use instrumental variable for which the assumption that they are not affected by the "method" (or better "response style"), see
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21, 1086–1120.
There is a new (not yet published) paper by Williams and O'Boyle who investigated the "marker technique" with some perhaps promising results:
Williams, L. J., & O’Boyle, E. H. (n.d.). Ideal, nonideal, and no-marker variables: The confirmatory factor analysis (CFA) marker technique works when it matters.
The abstract from this paper is
"A persistent concern in the management and applied psychology literature is the effect of common method variance on observed relations among variables. Recent work (i.e., Richardson, Simmering, & Sturman, 2009) evaluated 3 analytical approaches to controlling for common method variance, including the confirmatory factor analysis (CFA) marker technique. Their findings indicated significant problems with this technique, especially with nonideal marker variables (those with theoretical relations with substantive variables). Based on their simulation results, Richardson et al. concluded that not correcting for method variance provides more accurate estimates than using the CFA marker technique. We reexamined the effects of using marker variables in a simulation study and found the degree of error in estimates of a substantive factor correlation was relatively small in most cases, and much smaller than error associated with making no correction. Further, in instances in which the error was large, the correlations between the marker and substantive scales were higher than that found in organizational research with marker variables. We conclude that in most practical settings, the CFA marker technique yields parameter estimates close to their true values, and the criticisms made by Richardson et al. are overstated."
HTH
Holger
Thanks a lot sir...I find out Lindell technique in using marker variable is more useful...
For PLS-SEM, common method bias (CMB) is detected through a full Collinearity assessment approach (Kock, 2015). VIF values should be lower than the 3.3 threshold (Hair et al., 2017, Kock, 2015). This is indicative that the model is free from common method bias. Any value greater than 3.3 means the model is affected by CMB.
Check this out:
https://www.smartpls.com/resources/ebook_on_pls-sem.pdf
Normally VIF we check in SmartPLS when use formative constructs rather then reflective. Or to check CMB we can report VIF even if our constructs are reflective in nature. Please researchers and scholars clarify this point. Thanking in advance for replies.
According to Kock (2015) see the reference above the VIF for reflective and formative constructs can be utilized to assess common method bias. A higher tolerance level of 5.0 is suggested, but the more stringent threshold of 3.3 is used more often.
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