Common Method Bias / Common Method Variance

[Silburn Clarke]

One of the methodological issues that has repeatedly emerged and which seems to be of constant debate is common method bias. This is also referred to as common method variance by others although not strictly the same thing.

 

This is the idea that data collected with the same source (rater), same instrument and at the same time, is biased (ie self-reports in cross-sectional data collection)

 

On one side of the debate CMB/CMV is said to be not significant, on the other it is said to be an inflator of correlations, and still others say that its effect is one of attenuation.

 

On one side it is said to be best managed apriori by proper research design, on the other it is said to be controllable by post-collection data analytics, still others say that no amount of post-analysis can control and mitigate.

 

Recently both a PLS Latent Marker Method (Ronkko) as well as a Measured Latent Marker Variable Method (MLMVM) has been proffered as the best protocol for PLS'ers to employ in controlling for that bias.

 

Do you have any thoughts on the subject ?  

Is there a way to assess the effect in PLS ? 

Are there any seminars that are teaching mitigation strategies for that method ?

[Ned Kock]

I'd argue that one of the best tests of common method bias in the context of PLS is a full collinearity test, because this type of bias is invariably reflected in collinearity measures - see reference below.

Kock, N., & Lynn, G.S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580.

http://www.scriptwarp.com/warppls/pubs/Kock_Lynn_2012.pdf

When multi-group analyses are conducted with data segmentation, common method bias assessment becomes more complex; notably, measurement model invariance must be tested as well - see reference below.

Kock, N. (2014). Advanced mediating effects tests, multi-group analyses, and measurement model assessments in PLS-based SEM. International Journal of e-Collaboration, 10(3), 1-13.

http://www.scriptwarp.com/warppls/pubs/Kock_2014_UseSEsESsLoadsWeightsSEM.pdf

On Tue, Jun 3, 2014 at 8:01 AM, Silburn Clarke <survey...@gmail.com> wrote:

One of the methodological issues that has repeatedly emerged and which seems to be of constant debate is common method bias. This is also referred to as common method variance by others although not strictly the same thing. [...]

[Hsu, Maxwell K]

Dear Prof. Kock,

 

Is it possible for you to share with us the variance-covariance matrix of the observed variables related to your co-authored 2012  JAIS paper? “Kock, N., & Lynn, G.S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580. http://www.scriptwarp.com/warppls/pubs/Kock_Lynn_2012.pdf”? With the variance-covariance matrix, I hope to replicate your research findings with a learning-by-doing/replicating approach on my end. Thanks in advance for your kind consideration.

 

Best,
Maxwell

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[Ned Kock]

The entire dataset, together with other datasets, is available from:

http://warppls.com/

The one you are looking for is the "E-collaboration technologies study dataset".

The addition of error mentioned there should have minimal or no effect on the covariance-variance matrix, or any major result for that matter.

Ned

On Tue, Jun 3, 2014 at 10:46 AM, Hsu, Maxwell K <hs...@uww.edu> wrote:

Dear Prof. Kock,

 

Is it possible for you to share with us the variance-covariance matrix of the observed variables related to your co-authored 2012  JAIS paper? “Kock, N., & Lynn, G.S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580. http://www.scriptwarp.com/warppls/pubs/Kock_Lynn_2012.pdf”? [...]

[Hsu, Maxwell K]

Thanks a lot for your positive and prompt feedback J

 

Best,
Maxwell

 

From: pls...@googlegroups.com [mailto:pls...@googlegroups.com] On Behalf Of Ned Kock
Sent: Tuesday, June 03, 2014 11:20 AM
To: pls...@googlegroups.com
Subject: Re: [pls-sem] Common Method Bias / Common Method Variance

 

The entire dataset, together with other datasets, is available from:

--

[José Luis Roldán]

Dear Silburn,

Regarding your request, I would suggest the following references:

Chin, W. W., Thatcher, J. B., & Wright, R. T. (2012). Assessing Common Method Bias: Problems with the ULMC Technique. MIS Quarterly, 36(3).

Chin, W.W. et al., 2013. Controlling for Common Method Variance in PLS Analysis: The Measured Latent Marker Variable Approach. In H. Abdi et al., eds. Springer Proceedings in Mathematics & Statistics. New York, NY: Springer New York, pp. 231–239. Available at: http://link.springer.com/10.1007/978-1-4614-8283-3 

Best regards,

José L. Roldán

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Dr. José L. Roldán
Associate Professor of Business Administration

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[Rönkkö Mikko]

Hi

There is also a third alternative. You can use the partial correlation procedure to correct the observed covariance matrix and then calculate the PLS estimates using this covariance matrix.  This can be done with e.g. matrixpls that uses covariance data. 

Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114–121.

This approach has the advantage that you are breaking the problem into two distinct parts:

1) whether the partial correlation procedure works well with your data

2) whether PLS works well for your model and data under the assumption that the data did not contain CMV

Mikko

On 04 Jun 2014, at 10:48 , José Luis Roldán <jlro...@us.es> wrote:

Dear Silburn,

Regarding your request, I would suggest the following references:

Chin, W. W., Thatcher, J. B., & Wright, R. T. (2012). Assessing Common Method Bias: Problems with the ULMC Technique. MIS Quarterly, 36(3).

Chin, W.W. et al., 2013. Controlling for Common Method Variance in PLS Analysis: The Measured Latent Marker Variable Approach. In H. Abdi et al., eds. Springer Proceedings in Mathematics & Statistics. New York, NY: Springer New York, pp. 231–239. Available at: http://link.springer.com/10.1007/978-1-4614-8283-3 

Best regards,

José L. Roldán

El 03/06/2014, a las 15:01, Silburn Clarke <survey...@gmail.com> escribió: