Path coefficients > 1 HELP

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Marceau
PLS Junior User
Posts: 5
Joined: Wed Jul 18, 2018 3:12 pm
Real name and title: Carole Charbonnel

Path coefficients > 1 HELP

Hello,

I am a new user of PLS Equations Modelling. I have an existing model :
* 1 second order formative variable "CO-CREAT F." made of 2 first order formative variables "COPROD F" / "VALUE IN USE F."
* each of the first or second order formative variable is reflected in 3 reflective indicators CO-CREAT R. / VALUE IN USE R. / COPRODU R.)
* 3 reflective dependant variables (Valeur clients, Perf., Satisfact.)

I would like to calculate direct vs indirect effects, and for this I try to add direct links between first order formative variables and the 3 dependent variables.

The problem is : when I am adding those links, this leads to some coefficients becoming superior to 1, for instance 1.356 or -1.125. This is not normal, as path coefficients should be standardized figures.

Can you help me ?

CO-CREAT F. CO-CREAT. R. COPROD F. COPROD R. PERF. SATISFACT. VALUE IN USE F. VALUE IN USE R. VALEUR CLIENTS

CO-CREAT F. 0.293 0.710 1.376 1.376
CO-CREAT R.
COPROD F. 0.193 0.447 -0.234 -0.099 -0.091
COPROD R.
PERF
SATISFACT.
VALUE IN USE F. 0.871 -0.060 -0.591 0.268 -0.596
VALUE IN USE R.
Valeur clients

jmbecker
SmartPLS Developer
Posts: 932
Joined: Tue Mar 28, 2006 11:09 am
Real name and title: Dr. Jan-Michael Becker

Re: Path coefficients > 1 HELP

Path coefficients like standardized regression coefficients can be larger than 1.
Unlike a correlation coefficient they are not bound between -1 and 1.
Yet, you are right that values outside this range usually give rise to some concerns, especially about multicollinearity problems. The most likely reason for such large values are severe collinearity problems that should be avoided.

https://www.researchgate.net/post/Can_t ... 106e48e770

In your case, this is not a surprise as you have collinearity by design. You regress second-order and first-order constructs onto the same dependent variable, but the first-order construct is part of the second-order construct. In fact, if you regress all first-order plus second-order (which is the combination of all your first-order constructs) you should have nearly perfect collinearity (because the second-order construct is nearly perfectly predictable by your first-order construct). Hence, such a model is not feasible. You should also see that by having extreme VIF values.
Dr. Jan-Michael Becker, University of Cologne, SmartPLS Developer
Researchgate: https://www.researchgate.net/profile/Ja ... v=hdr_xprf

Marceau
PLS Junior User
Posts: 5
Joined: Wed Jul 18, 2018 3:12 pm
Real name and title: Carole Charbonnel

Re: Path coefficients > 1 HELP

Hi Jan-Michael,

thanks a lot for your reply, this was quite useful to understand the problem. The question now is : how can I deal with that ?

I use an existing formative index which as been developed formerly as Second order formative / formative measure :
12 indicators composing first order A / 11 indicators composing first order B / and second order C composed of A+B = 23 indicators.

I know that formative / formative measurmement models exist : in that case how can I deal with collinearity ? Do I have to integrate other independant variables ? Do I have to add more reflective measures ?

Kind regards

Carole

jmbecker
SmartPLS Developer
Posts: 932
Joined: Tue Mar 28, 2006 11:09 am
Real name and title: Dr. Jan-Michael Becker

Re: Path coefficients > 1 HELP

The problem are the direct links from the first-order constructs to the dependent variables if you include at the same time your second-order construct. You have to decide: Either second-order construct or first-order constructs. You cannot have an effect of all three on your DVs.
Dr. Jan-Michael Becker, University of Cologne, SmartPLS Developer
Researchgate: https://www.researchgate.net/profile/Ja ... v=hdr_xprf

Marceau
PLS Junior User
Posts: 5
Joined: Wed Jul 18, 2018 3:12 pm
Real name and title: Carole Charbonnel

Re: Path coefficients > 1 HELP

Hi,

this is quite clear. Thanks a lot for your help

Kind regards