How to test for Common Method Variance in PLS?

Frequently asked questions about PLS path modeling.
mauderoc
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How to test for Common Method Variance in PLS?

Post by mauderoc » Wed Feb 06, 2008 9:33 am

Dear all,

I am currently trying to test my dataset in PLS for common method variance (CMV) (possible bias in the data because both independent as dependent variables were collected at the sime time from the same source). Podsakoff 2003 gives several excellent methodological suggestions how one should deal with CMV in research.

I found an interesting article on the internet in which PLS is used to test for CMV (I also found similar published articles).
http://www.hfncenter.org/cms/book/export/html/243

However, this article contains the following CMS tess which I do not understand how to perform in PLS:

1) "Second, a partial correlation method was used (Podsakoff and Organ 1986). The highest factor from the principal component factor analysis was added to the PLS model as a control variable on all dependent variables. According to Podsakoff and Organ, this factor is assumed to contain the best approximation of the common method variance if is a general factor on which all variables load (p. 536). This factor did not produce a significant change in variance explained in any of the three dependent variables, again suggesting no substantial common method bias."


How does one add a factor from Principal component analysis to PLS? I know how to conduct a principal component analysis in SPSS, but I don't understand how you add the highest factor into PLS??

Thank you very much for your feedback in advance.

Kind regards,

Maurice


literature:
P. Podsakoff, S. MacKenzie, J. Lee, 2003, "Common Method Biases in Behavioral Research: A Critical Review of the
Literature and Recommended Remedies", Journal of Applied Psychology, Vol. 88, No. 5, 879–903
With kind regards,

Maurice de Rochemont

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Post by Diogenes » Wed Feb 06, 2008 9:12 pm

Hi Maurice,
The article by Podsakoff et al (2003) was the Best Paper in Industrial and Organizational Psychology in 2005 http://www.siop.org/tip/backissues/July ... 1to136.pdf

For using PLS, I recommend:
LIANG, H.; SARAF, N.; HU, Q.; XUE, Y. Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS Quarterly. v.31, n.1, p.59-87, mar/2007.
This article has a detailed explanation in its appendix E on how to use PLS to assess CMB, see pages 71 and 85-87.

About your question, if you want follow what was said in your item 1, you should run Principal Components in SPSS to identify the "highest factor" = first principal component before rotation, after that you could create a new model with this LV included.

Best regards
Bido

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Post by GDAVE » Sun Mar 22, 2009 5:02 am

Hello,

I have 3 independent variables and a dependent variable. As per theory, all IVs are +vely related (positive path coefficients) to the DV.

To assess the common method bias, when I add the highest factor from the principal component factor analysis to the PLS model as a control variable and run the model, all path coefficients between IVs and DV are changing to -ve. I am not sure why this is happenning, and how to interpret it.

I'll apprecite your help and suggestions.

Thanks
George

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Post by stephan.kramer » Tue Jan 19, 2010 9:47 am

Diogenes wrote:Hi Maurice,

For using PLS, I recommend:
LIANG, H.; SARAF, N.; HU, Q.; XUE, Y. Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS Quarterly. v.31, n.1, p.59-87, mar/2007.
This article has a detailed explanation in its appendix E on how to use PLS to assess CMB, see pages 71 and 85-87.
Dear Bido,

I checked the article you referred to regarding CMB. If I understood their procedure correctly, they converted each indicator to a first-order-construct which is measured by the corresponding indicator. The first-order-constructs are then linked to a second-order-construct (= the first-order-construct before transformation).

Wouldn't it be problematic to model this approach in Smart-PLS because the 2nd-order-construct then lacks indicators for measurement? What would be a feasible approach there? Maybe using the repeated indicator approach?

Any comments would be highly appreciated.

Thanks,

Stephan

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Post by khauschildt » Thu Jan 21, 2010 7:27 pm

Hello,

I am also curious whether anyone has tried this approach, as I have tried to build a model and indeed the latent variables without indicators are a problem (PLS doesn't run).

Thanks!

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Post by Diogenes » Fri Jan 22, 2010 11:57 am

Yes,

For the new 2nd order LV use the repeated indicator approach.


One way to do this faster is:

1) Copy the original model in the SmartPLS
2) For each 1st order LV:
- Hide its indicators (it will be a 2nd order LV in the final model)
- Create one new LV for each indicator (one LV / indicator)
- Connect the 2nd order LV with these new LV (that has just one indicator)
3) The “method” LV
- Create a new LV
- Use all indicators (repeated indicator approach)
- Connect this LV to all first order LV (that was created for each indicator)
4) Run and compute AVE


Best regards,

Bido

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Post by stephan.kramer » Fri Jan 22, 2010 12:32 pm

Thanks a lot Bido! This is very helpful!

One last question: Do you know whether this CMB-approach is only feasible for pure reflective measurement models or is there a possibility to compute formativ and 'mixed' models (in a way that some constructs are measured reflective and some formative) as well? At least the 'mixed' mode would lead to problems when assigning the indicators to the method factor...Using LV-scores would not be a proper workaround, would it?

Best regards, Stephan

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Post by khauschildt » Wed Jan 27, 2010 8:05 pm

Thank you for your reply! However, I now have another question to which I couldn't find an answer on previous threads. It would be great if someone could shed light on this issue:

Liang et al. state that to assess the extent of common method bias, "the squared values of the method factor loadings were interpreted as the percent of indicator variance caused by the method, whereas the squared loadings of substantive constructs were interpreted as the percent of indicator variance caused by substantive constructs" (p.87).

I am unsure about which numbers to compare. In the Liang et al. paper, I conclude from Figure E3 that what they actually used are the path coefficients (connecting the latent first-order variables to Method and Second order variables). However, using the repeated indicator (superblock) approach, there is a choice between

a) these path coefficients
b) loadings of the "superblock" indicators on the latent method and second order variables

Which is the appropriate number? One of these? Something else entirely?

Thank you so much in advance!!

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Post by khauschildt » Fri Jan 29, 2010 8:56 am

I am unsure about which numbers to compare. In the Liang et al. paper, I conclude from Figure E3 that what they actually used are the path coefficients (connecting the latent first-order variables to Method and Second order variables). However, using the repeated indicator (superblock) approach, there is a choice between

a) these path coefficients
b) loadings of the "superblock" indicators on the latent method and second order variables

Which is the appropriate number? One of these? Something else entirely?
OK, thinking about it again, I decided to go with a)- the path coefficients of the "latent indicator variables".These seem appropriate to me as they are really the indicators of the second-order variables.

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Post by jarhead01e » Sat Jun 12, 2010 6:42 am

Can someone tell me how to apply the "repeated indicator approach" approach? I'm having the same problem. My second order "original" LVs remain red because they have no direct indicators. I'm not seeing any options to employ a repeated indicator....

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Post by christian.nitzl » Sat Jun 12, 2010 8:57 pm

Hey Criston,

It is easy to produce a LV with "repeated indicator”. You have only to take a second time the indicators which you already used (drag and drop).

How it works you can see here: http://www.youtube.com/watch?v=7XDDTofpboM

Furthermore there is an working paper that could be interesting for the discussion her:

Ylitalo (2009): Controlling for Common Method Variance with Partial Least
Squares Path modeling: A Monte Carlo Study; http://www.sal.tkk.fi/Opinnot/Mat-2.108 ... yli09b.pdf

Best regards

Christian
Last edited by christian.nitzl on Sat Jun 19, 2010 9:40 am, edited 1 time in total.

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Post by jarhead01e » Sun Jun 13, 2010 6:24 am

AHHHH! Got it! Thanks so much Christian!

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Post by jarhead01e » Sun Jun 13, 2010 10:47 pm

Hi Christian,
Thanks for the link. It helped me get all 2nd order LVs loaded with indicators, but what about the METHOD LV? Do I load it with all of my Indicators?

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Post by christian.nitzl » Mon Jun 14, 2010 8:26 am

Your welcome!

Yes, you right. You load your “Method LV” with all of your Indicators.

See the comment of Professor Diogenes Bido at point 3 above.

Best regards

Christian

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Post by Diogenes » Mon Jun 14, 2010 11:38 am

Hi,

1) To compute the variances we used the path coefficients, as Kristina had said.

See viewtopic.php?t=640

2) Common method variance (one assumption is that there is a common cause, what is connected with the reflective way of modeling).

This view was used in Ylitalo (2009):
p.8) Classical test theory
p.9) Harman´s single factor test
p.11) In these papers, a proxy for common method variance was formed by conducting an explanatory factor analysis on all items in the model, and using the first emerging unrotated factor as a control variable in the inner model.
p.15) reflective indicators

For these reasons, formative indicators were not used in the CMV calculations.

Best regards,

Bido

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