Very thanks for the video. I wonder why does this model 2 provide conditional effects of the focal predictor considering both moderators as if they are also interacted as do in the model 3? ( in model 2, these two moderator independently moderate the effect of the predictor). I hope i can adress the issue. Best regards
The conditional effects don't assume an interaction between the two interaction terms. They just show the combined results of both independent moderations.
Hi, vielen Dank für deine super Videos! ich hätte eine Frage: Kann ich eine moderierte Mediation (Model 8) mit 2 unabhängigen Moderatoren (so wie Model 2) rechnen? Vielen Dank im Voraus und LG
I have a question. How do we interpret BOTH. In my case one of the moderators when taken alone is moderating (model 1) but when I use model 2 with another moderator, this moderator is not moderating while the second moderator is moderating. Yet, in the results section where it says BOTH this is significant moreover the effect size is larger than the moderation effect of the second moderator. How do I interpret this?
If BOTH is significant, then both interactions taken together explain significantly additional variance of the DV (beyond the other predictors in the model). Whereas the test for an individual interaction shows whether that interaction explains significantly additional variance of the DV beyond the other predictors including the other interaction. It is basically the same situation we know from multiple regression: You can have a significant correlation between a predictor and the criterion variable but at the same time not have a significant regression weight for that predictor in the context of a multiple regression. If one of the interactions is not significant, then that does not imply that its effect is zero; for that reason the effect size for BOTH will be larger thant the effect size for the other, significant interaction, in most cases.
@@RegorzStatistik thanks for your reply. If one interaction is not significant, and the other interaction is significant but both has a value greater than either square change for either oof the interactions can we talk about synergies between the moderators? I was trying to see when two moderators exist together (both are different HR practices) whether their moderation effect will produce a higher moderating effect or not
@@giuc100 If the R² for BOTH is smaller than the sum of the two individual R² values of the interactions, then there is some amount of overlap between the two interaction effects.
As long as gender is a binary variable it can be included as a covariate (with three genders you would need to construct two dummy variables instead). However, if I remember correctly, PROCESS for R does not work with factor variables so you would have to code the variable gender as a numeric variable.
Danke für das super Video! Sie sagten, dass diese Moderation für zwei unabhängige Moderatoren geeignet ist. Was, wenn ich nun zwei Moderatoren aufnehmen möchte, die korreliert sind (r = -.6)? Die Variablen gelten als zwei unterschiedliche Dimensionen (Subskalen) desselben Gesamtkonstrukts (Fragebogen). In allen Veröffentlichungen, die ich bisher dazu angeschaut habe, werden diese Dimensionen aber konsequent NICHT zu einer Gesamtskala zusammengefasst und sind von den ursprünglichen Autoren auch nicht dazu vorgesehen. Wenn man sie zu einem Konstrukt zusammenfassen wollen würde, müsste man eine der beiden Skalen invertieren, allerdings bin ich nicht sicher, ob ich das einfach machen kann. Ich bin sehr dankbar für Denkanstöße in die richtige Richtung. Liebe Grüße!
Das unabhängig ist in diesem Kontext anders gemeint. Das Modell 2 prüft zwei Moderatoren, die *unabhängig* voneinander den Effekt der UV auf die AV moderieren. Im Kontrast dazu ist der Moderationseinfluss im PROCESS Model 3 nicht unabhängig voneinander, sondern dort interagieren die beiden Moderatoren miteinander. Es müssen also nicht beide Moderatoren statistisch unabhängig voneinander sein.
Thank you so much!
Very thanks for the video. I wonder why does this model 2 provide conditional effects of the focal predictor considering both moderators as if they are also interacted as do in the model 3? ( in model 2, these two moderator independently moderate the effect of the predictor). I hope i can adress the issue. Best regards
The conditional effects don't assume an interaction between the two interaction terms. They just show the combined results of both independent moderations.
Hi, vielen Dank für deine super Videos! ich hätte eine Frage: Kann ich eine moderierte Mediation (Model 8) mit 2 unabhängigen Moderatoren (so wie Model 2) rechnen? Vielen Dank im Voraus und LG
Model 10 ist ein Modell mit zwei unabhängigen Moderatoren, die sowohl a-Pfad als auch c'-Pfad moderieren.
I have a question. How do we interpret BOTH. In my case one of the moderators when taken alone is moderating (model 1) but when I use model 2 with another moderator, this moderator is not moderating while the second moderator is moderating. Yet, in the results section where it says BOTH this is significant moreover the effect size is larger than the moderation effect of the second moderator. How do I interpret this?
If BOTH is significant, then both interactions taken together explain significantly additional variance of the DV (beyond the other predictors in the model).
Whereas the test for an individual interaction shows whether that interaction explains significantly additional variance of the DV beyond the other predictors including the other interaction.
It is basically the same situation we know from multiple regression: You can have a significant correlation between a predictor and the criterion variable but at the same time not have a significant regression weight for that predictor in the context of a multiple regression.
If one of the interactions is not significant, then that does not imply that its effect is zero; for that reason the effect size for BOTH will be larger thant the effect size for the other, significant interaction, in most cases.
@@RegorzStatistik thanks for your reply. If one interaction is not significant, and the other interaction is significant but both has a value greater than either square change for either oof the interactions can we talk about synergies between the moderators? I was trying to see when two moderators exist together (both are different HR practices) whether their moderation effect will produce a higher moderating effect or not
@@giuc100 If the R² for BOTH is smaller than the sum of the two individual R² values of the interactions, then there is some amount of overlap between the two interaction effects.
Can you use this code if one of your covariates is categorical? I am using gender as one of my covariates. Thanks!
As long as gender is a binary variable it can be included as a covariate (with three genders you would need to construct two dummy variables instead). However, if I remember correctly, PROCESS for R does not work with factor variables so you would have to code the variable gender as a numeric variable.
Danke für das super Video!
Sie sagten, dass diese Moderation für zwei unabhängige Moderatoren geeignet ist. Was, wenn ich nun zwei Moderatoren aufnehmen möchte, die korreliert sind (r = -.6)? Die Variablen gelten als zwei unterschiedliche Dimensionen (Subskalen) desselben Gesamtkonstrukts (Fragebogen). In allen Veröffentlichungen, die ich bisher dazu angeschaut habe, werden diese Dimensionen aber konsequent NICHT zu einer Gesamtskala zusammengefasst und sind von den ursprünglichen Autoren auch nicht dazu vorgesehen. Wenn man sie zu einem Konstrukt zusammenfassen wollen würde, müsste man eine der beiden Skalen invertieren, allerdings bin ich nicht sicher, ob ich das einfach machen kann.
Ich bin sehr dankbar für Denkanstöße in die richtige Richtung.
Liebe Grüße!
Das unabhängig ist in diesem Kontext anders gemeint. Das Modell 2 prüft zwei Moderatoren, die *unabhängig* voneinander den Effekt der UV auf die AV moderieren. Im Kontrast dazu ist der Moderationseinfluss im PROCESS Model 3 nicht unabhängig voneinander, sondern dort interagieren die beiden Moderatoren miteinander.
Es müssen also nicht beide Moderatoren statistisch unabhängig voneinander sein.