Dear all,
how can I interpret the path coefficients with a moderating variable. Example: The path coefficient from the predictor variable is a, the path coefficient from the moderator variable is b and the path coefficient of the interaction variable is c.
My moderator variable is 0/1. Does this mean that the path coefficient from the predictor to the dependent variable (DV) is = a in case the moderating variable is 0 and a+b in case the moderating variable is 1?
How do I know whether the relationship between the predictor and the DV is significant if the moderating variable is 1? I know that if I run the bootstrapping procedure I can infer whether there is a signifcant moderating effect. But it would be important for me to know whether the path coefficient from the predictor is significant in case the moderating variable = 0 and in case it equals 1.
Thanks for your help.
Markus
Moderating effect - Interpretation
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christian.nitzl
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Dear Prof. Arnold,
For checking a binary moderator variable (0/1) we typically use a group comparison approach in PLS. In this way your necessary interpretation would be very easy.
For that you have to calculate your model only for group ‘0’. There you can check if your path is significant for group ‘0’. You can do this also for group ‘1’ to check if the path is significant for group ‘1’.
Thereafter you could check the moderating effect if you calculate the difference between the path of group ‘0’ and ‘1’.
I hope this helps!
Best regards,
Christian
For checking a binary moderator variable (0/1) we typically use a group comparison approach in PLS. In this way your necessary interpretation would be very easy.
For that you have to calculate your model only for group ‘0’. There you can check if your path is significant for group ‘0’. You can do this also for group ‘1’ to check if the path is significant for group ‘1’.
Thereafter you could check the moderating effect if you calculate the difference between the path of group ‘0’ and ‘1’.
I hope this helps!
Best regards,
Christian
Thanks for the answer. It helps to a certain degree. I already calculated it for both groups separately before and found what I want.
However, in PLS there is the opportunity to introduce a moderator effect in a model including all data, like an interaction effect in a regression. Now, in a regression, I could check whether both coefficients together are different from zero. Is this also possible in PLS?
(And of course, the two path coefficients for the separate gruops are not exactly the same as if a moderator effect is included in a single model.)
However, in PLS there is the opportunity to introduce a moderator effect in a model including all data, like an interaction effect in a regression. Now, in a regression, I could check whether both coefficients together are different from zero. Is this also possible in PLS?
(And of course, the two path coefficients for the separate gruops are not exactly the same as if a moderator effect is included in a single model.)