Indirect Effect

Frequently asked questions about PLS path modeling.
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Real name and title: Mr. Teoh Kok Ban

Indirect Effect

Post by Derick » Thu Apr 18, 2019 6:25 am


I have tested indirect effect in my study, where the t-value is significant at 0.05 level of significance but insignificant at 0.01 level of significance.

However, according to my 95% bootstrap confidence interval, it is (-0.083, 0.003) where it includes zero in between the confidence interval.

Hence, I would like to ask shall I consider this as insignificance due to the confidence interval or significance due to the p-value?

Thank you in advance for the prompt reply.

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Re: Indirect Effect

Post by jmbecker » Mon Apr 29, 2019 8:23 am

That is a good question.

1) Certainly, your effect is on the edge of being significant.
The problem with the concept of significance is that it is a dichotomous decision on a continuous phenomenon. Why 5% Because it is a conventional standard? But where is the difference between 4.9% and 5.1%?? There is none and still people count the one as significant and the other as non-significant.
Bayesians would say that there is some but not overwhelming evidence for the effect. They usually try to avoid making the dichotomous decision, but try to quantify the level of support. You will find that also in many modern discussions about frequentist approaches related to recent calls to abandon the concept of significance at all ( ... yfw-_ZKnTQ)

2) Another aspect is that different methods come to different conclusions.
You are likely using some bias-corrected percentile or bias-corrected and accelerated confidence intervals. That is something different as using a t-distribution based test, which underlies the p-value. If you would use studentized intervals you would get an interval that agrees with your p-value.
However, in the realm of indirect effects, researchers (mostly Preacher and Hayes) have found that the distribution of the indirect effect in mediation does not follow conventional distributions and therefore non-parametric bias-corrected percentile intervals should be used.

Thus, if you want to make a dichotomous decision you should prefer the confidence interval because that is the better method. However, you may also discuss that there are divergent findings and that your effect might be on the edge of being significant and that more research (additional studies) is needed to come to a final conclusion.
Dr. Jan-Michael Becker, University of Cologne, SmartPLS Developer
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