In the evaluation of my Strucutral Equation Model with the PLS-SEM approach I have path coefficients between two constructs that are significant (p-value < 0,000) but the effect size f² ist small and insignificant (Original Sample = 0,100; Bootstrap Sample Mean = 0,112, p-value = 0,135).
How can I interpret this result? I postulated the hypotheses, that the one construct has positive effects on the other.
Best regards,
Nikolas
I am a bit confused by your question because effect size indices do not have p-values (although reporting confidence intervals for them can be very useful).
Setting that aside, an effect can be both small AND have a small p-value when its standard error is very small, e.g. with a very large sample.
Dear Nikolas Lauer
Effect sizes should be interpreted as follows (Cohen’s, 1988):
f2 ≥ 0.02, f2≥ 0.15 and f2 ≥ 0.35 represent small, medium, and large effect sizes, respectively.
Regards
Even though it is significant, the effect is not large enough to be meaningful.
Thank you!
Have a look at our recent publications:
Article Validating a New TQM-Benchmarking Measurement Model in an In...
Article Total Quality Management Boosters and Blockers in a Humanita...
Article Political Advertising Effectiveness in Wartime Syria
Article Email is Evil! Behavioural Responses towards Permission-base...
Article When empathy hurts: Modelling university students’ word of m...
Thank you very much for your helpful answers.
I was confusing a couple of things in my interpretation.
I have the path coefficient and it's p-value. This p-value should determine whether the realtionsship between the two constructs is significant or not. The effect size f² (or q²) can be reported additionally, but their significance itself is often not reported, rather interpreted in the above mentioned intervals. In my research I found no papers reporting the p-value of the effect size.
Please note, that my work was only a master thesis. For an useful overview consider the following book.
Hair, J. F.; Hult, G. T. M.; Ringle, C. M.; Sarstedt, M. (2017): A primer on partial least squares structural equation modeling (PLS-SEM), Los Angeles.
- Badges
- Science method
- Similar topics
- Mathematics
- Statistics
- Statistical Techniques
- Structural Equation Modeling