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How to test Moderated Mediation in SEM (Structural Equation Modeling)
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- Опубліковано 13 лип 2022
- This video describes how to analyze a moderated mediation model in AMOS. A description of mediation through bootstrapping is explained along with the inclusion on an interaction variable. The index of moderated mediation is discussed as well. For more information about how to test a moderated mediation model, see my book "Applied Structural Equation Modeling using Amos". Amazon link: www.amazon.com...
Thank you very much!!!! I finally don't have to jump between software to run a moderated mediation model!!! You are awesome!!!
Thanks a lot. I loved to read the book because of your simple way of explanation
Thank you, simple and complete knowledge.
1. In the moderated mediation section, you described it as "Moderated Mediation With a Continuous Moderator". What would be the method if there was a model with latent variables with sub-dimensions?
2. When you constructed the model in your book, you were taking the interaction structure as CentreAdapt_X_Friend and the adapt and friend structures as normal. However, in Figure 7.40 I see that you take the structures adapt and friendly as comp_adapt and comp_friendly. Is this a specific exception to moderated mediation?
Trying to test moderated mediation with higher order constructs (with subdimensions) is extremely difficult. The reason why is you need to form an interaction term with the moderator and the independent variable. The most popular was to handle this is to create composite variables. To your point 2, the book talks about comp_friendly and comp_adapt. This is where the latent variables were created into composite scores for each construct. This is not unique to moderated mediation....it is just the most efficient way to handle a complex problem. Can you test moderated mediation with latent variable? Yes. I have another video on UA-cam called how to test moderation with Unobserved variables that explains how to do this. Hope that helps.
Hello, first of all, thank you very much for your reply.
I have improved my analysis by following your suggestions and your book. If you have time, I have a few more questions.
1. For example, considering figure 7.19 in the moderation section of your book, if "Adaptive Behaviour" has 3 sub-factors and "Customer Delight" has 2 sub-factors, will there be a change in the analysis direction of the model?
2. For the Modareted Mediation model, I arranged it as Path Model, Full Indicator Model, Mixed Model in accordance with the instructions in the book. Unlike yours, the Interaction variable gave slightly different results in all 3 techniques. Even the path model was meaningless. Other variables in the model were close to each other. Could the sample have an effect on this? Unfortunately, my sample is limited to 205 due to the subject. There are also 49 variables in the model.
3. When I created the "composite variable" for the path model, unlike yours, the variables had sub-dimensions. I first took the average of the sub-dimensions and then calculated the value of the variable by taking the average of the sub-dimensions. Is the path I followed correct?
I am grateful for your contribution in advance.
@@cihanuyank9749 So lets just clean up a little terminology first and that should help things. Adaptive behavior does not have "sub factors" those are the observed indicators or measurement items for the construct. To form a composite variable, one usually takes an average of all the observed variable scores and then you have a single score that represents to the construct. You will most likely have different results if you run this moderated mediation model as a path model and then as a full indicator or mixed model method. The reason why is you are accounting for measurement error in the full indicator and mixed model. You are not accounting for measurement error in the path model. Your sample is a little small for that much analysis and that might have something to do with it. With that small of a sample, I would encourage you to run it as a path model and have all your constructs as composite variables. It will make things easier for you.
Dear respected Dr. I would like to know if moderated mediation analysis could work with 5 iv's, 2 mediators, and 2 dv's. Should I run it separately? Thanks
Hi. Your video and book is amazing. why not use full model to test? I mean with the items errors included. in that case do I need to choose mixed method or match pairs method for the interaction term to test? will it have the same result?
It is always best to use a full model. When initially describing the concept I try to use a very simple example for beginners. Using matched pairs would be ideal.
@@joelcollier9387 thanks for your suggestion.
If we've two mediators and both of their (mediating and moderated mediating) effects are insignificant, then is it considered to be a wrong model? Do we MUST have a mediating and/or moderated mediating effect in our model?
I don't know that it it is a "wrong" model. It seems like it is more of a main effects model. A more interesting model would explain why those relationships take place (mediators and moderators) but it is not a "wrong" model.
Hi Joel, Thank you very much for this informative video and your great book, it helps me a lot in my master thesis!!. I am running a model with parallel and serial Mediation and I also have a moderator who moderates the effect between IV and the first parallel mediators. In your book it says that with serial mediations the paths are multiplied to get the indirect effect (path A*path B...). But I am a bit confused in the chapter on the moderated mediated model, where it says: "Initially, we are going to tell AMOS to calculate the labeled parameter "A_Path" by the "B_Path". This is going to determine the indirect effect in the presence of the moderator". Since my model now contains both moderator + seriel mediation, I am wondering how I can determine the individual indirect effects. Is the effect of the moderator directly included when I multiply the individual paths between my mediators, how do I get the indirect effects without the moderator then? I am a beginner and feel very helpless. I have already tried to get answer through litertaur but was not successful. I would be infinitely grateful for an answer. My model looks like this IV --> M1 -->M2-->DV and IV --> M3 --> M4-->DV (The moderator acts on M1 and M3).
Asiula, Kind of a complicated question to tackle here but here is my best try. With mediation you are looking for the indirect effect (A path X B path), moderated mediation is looking for the indirect effect in the presence of the moderator (and interaction term). For moderated mediation, your model will have moderator and interaction term included with paths to the dependent variable of choice. Thus, with moderated mediation you are looking calculating the indirect effect (A path X B path) in the presence of the moderator and interaction term (both included). If you are looking for simple mediation then no moderator or interaction term would be included. hope that helps.
@@joelcollier9387 Hi Joel, thank you very much for your message! This already helps me a lot. Do I understand it correctly that if I want to estimate the seriel mediation without the influence of the moderator and once with the influence of the moderator, I set up two models?
Hi Joe, great videos and book! I have a question about the placement of the moderator that I can't find an answer for in your resources. Can you also move the location of the moderator? For instance, instead of being between adapt and delight, moving it between delight and WOM (similar to moving from a process model 8 to a model 15). I'm trying to test a model where it is the second part of the mediation that I'd like to test. In my attempt to do this, I've so far created an interaction term for model's equivalent of delight*WOM. My problem comes when trying to create an index of moderated mediation. Since the interaction variable now has a direct path to the WOM DV, would an estimand of the interaction to DV path and mediatior variable to DV path still calculate the value I need? Thank you for your resources and any advice you may have on this!
Absolutely. It works the same way even if you move the moderator to different spots in the model. So if we were testing the moderator between delight and WOM, we would form the interaction term with delight and friendliness and then test the relationships to WOM. It is the same premise even though the moderator is now testing from the "mediator" to the ultimate DV. Hope that helps.
@@joelcollier9387 Perfect! Thanks for the quick response. Your book (and supplemental videos) are so helpful. This insight expands their usability even further for me. Many thanks!
In this video you are not creating any high group or low group for moderator to run the moderated mediation analysis. There are numbers of researchers using high group and low group techniques to test moderated mediation models. Please suggest to me how to cite your results and references to support your moderated mediation approach. Thank you
They are simply probing the interaction in a high and low level. I talk about this in detail in my book and even show a step by step process. I did not do this in the video because it would make the length pretty long. Anything that starts to approach 20 minutes and viewership starts to dip. You can cite my book if you need a reference. The book is titled "Applied Structural Equation Modeling using AMOS". You can find it at most major bookstores. Here is the link to Amazon if you are curious:
www.amazon.com/Applied-Structural-Equation-Modeling-using/dp/0367435268/ref=sr_1_1?crid=27ZACAG96YFS&keywords=joel+collier&qid=1662152266&sprefix=joel+collier%2Caps%2C93&sr=8-1&ufe=app_do%3Aamzn1.fos.d977788f-1483-4f76-90a3-786e4cdc8f10
Do you have similar video for Smart PLS. Thank you
I do not make videos for PLS. PLS-SEM has a bad reputation of having biased parameter estimates. I encourage everyone to move away from variance based SEM and use a covariance based SEM program.
Can we use binary mediator?
Unfortunately with AMOS you can not have a mediator that is binary. You can have binary variables as independent variables and moderators but not as mediators or constructs that will ultimately be treated as a dependent variable
Dummy variable is mediator