Thank you so, so much for this video! After stumbling upon a 3-way interaction and trying to figure it out on my own with little success, this video pretty much single-handedly saved my sanity.
My homophobic dissertation adviser kept asking for 1 interaction....but using your video I was able to do my own work to prove him wrong! Thank you (again) so much! Can you come teach our stats classes?!
This is what i did. Entered DV in y (Outcome variable), IV in X , the mediating variables (3) in M variables, and each Moderating variables in proposed moderator y and Z
No clue if it's an OS thing or just the version of PROCESS, but you could also get a graphing syntax along with the visualization data, to save you some hassle.
Thanks so much for this helpful tutorial Dr. Buchanan! I'm using three-moderation for my thesis and this was so helpful. Do know where I could find any examples about presenting the results of a three-way interaction? I can find plenty of examples for 2 way interactions but am struggling to work out how to present the three- way.
Thanks for your helpful explanation. Is it possible to add a third moderator to the additive multiple moderation model (Model 2) or multiplicative moderation model (Model 3)? I tried using the "z" variable name for the third moderator for both types of models, but the program did not recognize it.
This video is not new, but I am still wondering, if it´s possible for anyone to name any good literature for the outlier analysis at the beginning of this video. If I do it that way, I will need to give some "source" and I can´t sadly mention this great video:-) Thanks
I love your videos. Saved me more than once. I noticed you didn't do power analysis (G*power) with this Moderation in PROCESS example. is it not appropriate to do so?
Hi Erin! This video - and all of your other videos - is great and has helped a lot. I have a quick question, if that's ok. You say below that in a model where X is continuous variable and M and W are dichotomous variables that you shouldn't mean centre. My model has X as a dichotomous variable and M as a continuous. Would you recommend mean centring any continuous variables (either X or M, but in my case M) by hand prior to PROCESS analysis and using them in the PROCESS model instead of the variable not mean centred? I hope that makes sense and thanks in advance for your help!
Not in process that I'm aware of ... but it's been a while since I looked at it. You'd have to break it down into the smaller interactions and rerun it would be my guess.
Thank you for the video. it is what I want to find. However, I cannot hear clearly about the formula (4/N-K-1) and (2k+2)/N, I dont understand why we need to compute this.
Hi Erin! First, loved your videos! Thank you so much- I learned more in a couple of hours listening to you than I did in an entire semester of stats. Question: I am currently using PROCESS model 9 for my dissertation (my supervisor's idea...) and interpreting the output is a little beyond my understanding. Can you recommend any other videos or papers that could help walk me through it?
Hi Erin, great video- I am just wondering, for every Dependent variable I want to examine using PROCESS model number three should I check for outliers separately for each? i.e do I change the DV in the Analyse --> regression--> linear --> DV box and click Mahalanobis, cooks and leverage again for each? Hope that makes sense.
Firstly great video i encountered a small issue: When i run the model, i get the error: You have specified an M variable in a model that does not use it. Is there any way this can be resloved im running SPSS 28 and Process_v4.1. Thank you for your time.
Dr. Buchanan, thank you for your video! I have a question regarding the outliner procedure. You seems to only did the multivariate outliers. Should you also exam univariate outliers as well?
This is so helpful Erin, thanks a lot for this easy to follow video :) Thanks a lot. I was just looking for something like this , just is my DV a bivariate. Would you know if I could also proceed with Process on this?
Thanks so much for this helpful video. So, can we tell that the moderated moderation model is still significant since interaction 4 is significant (X*W*Z), even if interaction 1 (X*W) appears to be not significant in the entire model? I'm now a bit confused with my data; because the simple moderation model appears to be significant when I put the first moderator (W) only, but it appears to be not when I put the second moderator (Z) together.
That will happen since there are different variables when you only do simple versus 3 way. If the three way is significant, then you would say there’s moderation.
Erin, thanks for your video, it helps a lot! I have two questions. First, in my model X is continuous variable and M and W are dichotomous variables. Would you tick mean center option in that case as well? Second, can I interpret the effect shown in conditional effect table when a whole three way interaction in non significant?
Hello. In my model, i just got one significant maineffect and one significant Interaktion. All the other interactions and maineffects are not significant. Nevertheless, am i allowed to interpret the simple slopes or not? i don´t get it, thank you :)
Hi, Erin. I have become a big fan of your videos lately. Lifesaving. I noticed on this and another video the section about data screening and cleaning, not much is said about how to report what you find. What do I need to say in a manuscript about histograms and scatterplots if they look ok? perhaps you have resources for this kind of question available somewhere?
Hello Erin. I have been following your videos and I found them very useful. I had a question though: I have 3 moderators, 2 DVs and 1 IV; with this configuration, is it possible to analyse using PROCESS? In my model the 3 moderators are affecting the link between the IV and the 2 DVs, and not each other or anything crazy like that. Any advice?
The new version of process does interactions with categorical variables (and dummy codes them for you), so you could use that (then just stick the variable in X or M).
Is there a link for a how-to? I’ve tried looking. I have a continuous variable that I am splitting up into 3 groups to check differences between the 33, 66, and 100 percentiles.
@@eaterrr Splitting a continuous variable always makes me nervous :| I believe you can just denote that it's categorical with the categorical button options that are available in process, but you would want to create fake category column if you are splitting it from a continuous column. I can't quite remember if the categorical options work for a three-way interaction model, but I think so.
I’m nervous about it, too! I’m looking at whether two continuous variables (one is a moderator) will predict performance on a task - and I want to see if there are differences with the moderator variable between high, middle, and low groups split by those percentiles, but I’m trying to figure out the best way to do so. Thank you!
@@eaterrr Ok here's what I might tell you to consider instead. Figure out the score that a person would have on that variable at those points - like what is 33%, 66%, etc. Then shift the data around that point - normal simple slopes are 1 SD above and below the mean, and these are created by adding and subtracting a SD from the data, rather than creating subgroups of people who are only 1 above or below. Therefore, you could apply the same idea to your data by manually creating columns that mimic a 33% group, 66%, etc. So, if the 33% group scores a 4 and the mean is 8, you need to shift them up 4 points to be centered around the mean (assuming mean centered regression for easier interpretation). If the 66% group scores a 12 and the mean is 8, you should shift them down 4 points to see their effect. Then you would run the analysis again with those columns (one at a time, instead of the original column for that variable) and check out what happens to the other variables in the analysis for interpreting simple slopes.
Thank you so much, you are supper very easy steps and detailed explanation as well, but I wonder what if the moderator is a dichotomous moderator such as gender or nationality? because I have tried this but I got this note "The Johnson-Neyman method cannot be used with a dichotomous moderator". Do I have to run the normal regression? or I can use PROCESS as well?
Hello Erin, do you have a guide on reporting results for moderated moderation? I have two conditioning variables (W) acting separately on my moderator (M) which I intend to analyse separately. So my model is as per the conceptual diagram, except I'm running it twice (once for each (W) variable. Thanks for any help you can offer
No. You would need to use logistic regression for categorical outcomes, which requires less assumptions. You could still use cooks, leverage, and standardized residuals though.
Could you make a video on checking threeway interaction with logistic regression? Including checking the assumptions and all? I could not find this anywhere. Thanks a lot!
Hi Dr. Buchanan, This is a terrific video! I've referenced it a number of times in a project I recently submitted. We just got the reviews back, and the reviewers are wondering if we can depict confidence intervals for the figures we created. Is there anyway to ask PROCESS for 95% CIs along with the plotting data that it already generates so helpfully? Thanks!
I kind of wanted to know, so I found this calculator: www.danielsoper.com/statcalc/calculator.aspx?id=90 which will let you calculate the CI values around the estimated scores (standard error of the estimate = mean square error / residual). However, it's still not totally clear to me how you'd add them into graph builder in SPSS - it seems to only want to calculate them ... you might be able to make the chart in Excel, which will let you add error bars manually.
Well that's quite helpful as I ended up making the original graph in excel anyways! One follow-up, if you don't mind: I'm assuming the MSE comes from the model summary portion of the process output. For the "residual" would I simply use the standard error of the three-way interaction term in the regression output?
So, you don't get this output in the version I'm using in the video (2.15) but if you have the newer process (2.16) you get the mean square error of the model (like the overall model not the predictor) under MSE. Different example below - but you'd use that number. Model Summary R R-sq MSE F df1 df2 p .1849 .0342 .2046 1364.2116 7.0000 269720.000 .0000
Hello Dr. Buchanan, I also included three covariates into PROCESS model 3. One of the covariates ended up with p < .05 within the model. Is there anything that I should be cautious about? Could you give an example on how I should report it? Much Thanks. Will
I would just denote that the covariate was a significant adjustor of the DV - and then interpret the direction of the coefficient (i.e., positive or negative). If you want to interpret the model, you could say the adjusted Y is mediated by M etc.
Thank you. I have a question though. Can W be a categorical variable? I'm testing a model in which W is a 3-level categorical variable. But based on my output, it seems that PROCESS treated it as a continuous variable.
Hi Erin, thank you very much for your video. I have a question about calculating the cut off point for cook distances. Do you have any references for that kind of formula? Unfortunately, I can't find it.... thank you ahead!! :)
Hi! First of all; thank you very much for all the SPSS PROCESS tutorials! Have been of great help so far! I only have one question (I'm using model 4 and model 16 as an extension to that); I'm using Schwartz' PVQ as moderator (a prerequisite of this scale is that you mean center these values in a certain way before entering them in the analysis). To ensure that the other variables (i.e. the mediators) are also mean-cantered, I've done so manually. The outcome shows that the interaction coefficients are quite high (all above 0) but the probabilities are in-significant (all above the 0.05 threshold). Does this mean I'm doing something wrong or is this a suitable outcome?! Hope you can help me!
To me, that implies that there's a lot of error that the values are not significantly different from zero. So, the output you are describing is possible yes.
I watched this awesome youtube video on three-way interaction to run the analysis for my data. I am looking at the moderating role of adaptive Emotion regulation in the relationship between stress and well-being. I also plan to see if years of experience moderates this association. I mean adaptive emotion regulation and years of experience are moderators. I select model three and put the X, Y, W, and Z in the correct boxes. I am using Process v3.0. In the output, I do not get the information for the conditional effect of X on Y at values of the moderators. However, when I enter only one moderator (adaptive emotion regulation) and select model one, it gives me this information. I would appreciate if you guide me as to why this happens.
Hey Erin, Thank you for this video. I am trying to find where my interaction is significant/nonsignificant at low and high values of my 3 way interaction. Since the conditional effects of X on Y at different levels of both moderators are only arbitrary points (and mine are not significant), I am trying to find out where exactly these points are both significant. I am confused as to how to interpret this using the JN. Since JN shows the effect of the interaction of X*M at different levels of W, how would I know exactly whether M is high when W is significant. For example, if my JN output shows that everything is significant below values of 3 for W, how would I know if M is high or low below values of 3? I would like to discuss what you did in the video- a chart with low and high values of both moderators interacting- but I can't figure out how to detect whether M is high or low at significant levels of W. Do you have any suggestions?
I mean, you wouldn't know if M is high or low, you would get the b value for M at that level of W. So you would say, as M goes up/down at less than 3 of W, then Y does blah blah blah. Or am I misunderstanding your question?
Hi. Thanks as always. Question. Suppose I've looked at the effects of gender (M,F) and learning context (University or college) at levels of Math Efficacy (-1,0,+1) on Math GPA. So now i've plotted the data for visualizing conditional effects. But Im also interested in the plot for gender and learning context on Math GPA without levels of Math Efficacy (eg with the 3 lines for ME colapsed). Would that be interpretable? If so, is it like having covaried Math Efficacy? How would one talk about it? I've looked. The pattern is similar to that using the raw data, but cleaner.
Sure - you could create the two way interaction instead - it wouldn't be covaried, so much as averaged across Math Efficacy. You would say at average levels of ME (so 0 in your scale), here's what context by gender looks like for GPA.
Thank you very much for the video. You did a great job! I wonder if I have two categorical variables and one continuous variable( as the independent variables in the regression model, what model should I use? The 1SD above average for categorical variable just doesn't make sense here.
Thank you. But PROCESS did not seem to differentiate categorical vs. continuous variables here when I use Model 3. How would you be able to let PROCESS work on categorical variables and simple slopes for each category？ Many many thanks.
Using the Process Plug in + SPSS, my research model is 9, consist of 1 IV, 2 Moderating , 3 Mediating and 1 VD. My question is is there a certain process to conduct the analysis using model 9? i mean after inserting the variables in the system, what are the other options that i need to select to complete the analysis
Could i use the PROCESS method with a 4-way interaction? What I'm looking at is Work Sector with 4 levels, with Embeddedness as the moderator and OCB as the independent. or am i only able to use the manual method?
Thank you for this great video it helped me a lot with understanding three way interactions. I do have one question though. When I run my regression in SPSS (without macro) and just insert all two and three way interactions through product termes (I only have three independent variables) this leads to a significant three way interaction. When running the macro I don't get a three way interaction. Is there any possible explanation for this difference and should I, in your opinion, use the regular regression or use the MACRO in my dissertation? Maybe it is important to state that nothing was hypothesized on three way interactions (the work I'm doing is primarily focussed on main effects and two way interactions) and in the other two models (I'm testing the effect on attitude towards the brand, attitude towards the ad and Purchase intention in three different regressions) I don't get a significant three way interaction no matter what method I use.
+Erin Buchanan yes that is what I was thinking as well (that I should not see a difference). And yes I did center the variables (by subtracting the mean) before making my product terms. Do I have to use the centered variables for the single variables as well or only to form the product terms? Because for the main variables I did not use the centered variables. Could this explain the difference?
Hi Erin!! Can I interpret the output for the three way interaction to report main effects for my dissertation? When I run the analysis without the interaction the main effect is not significant, but when I put it in process it is significant. I don't really care about the main effect, just the three-way interaction. Please advise :-)
I will like to opened this discussion. I was told to never interpret the main effect when interaction terms are put in the equation, since they are tainted by that matter which render them non-interpretable. Should we interpret main effect at step 1 only? What about simple interaction in step 2 before the triple interaction? Can we interpret the two-way interaction in the final model when the triple interaction is added to the model? Any thought?
I will like to opened this discussion. I was told to never interpret the main effect when interaction terms are put in the equation, since they are tainted by that matter which render them non-interpretable. Should we interpret main effect at step 1 only? What about simple interaction in step 2 before the triple interaction? Can we interpret the two-way interaction in the final model when the triple interaction is added to the model? Any thought?
Thank you very much, I follow your work and find all your videos extremely useful. I also want to ask you, what would be the best way to analize the data if one of the moderator is a categorical variable with three levels (two experimental and one control condition) and the other one is continuous. Should I use dummy variables like in a regression model, moderating by one dummy and controlling for the other one? or should I simply use dichotomous variable comparing one experimental condition with the control gruop? Thank you in advance. Regards, EGS
+Efraín García Hi Efraín, may I ask which way you finally chose for your data? I am curious because me too I am trying to run a 3way interaction with a categorical variable with three levels as moderator. Are u on linkedin? Maybe we can connect there. I would highly appreciate an exchange on that matter with you. Thank you so much in advance!
Svenja Hi Svenja, sorry for the delayed answer. I run the model suggested by Erin, and also did the moderated moderation with the SPSS Macro Process (Hayes). I found both ways quite useful to conduct the analysis. I'm on LinkedIn with this same name, so we could exchange ideas there.
+Efraín García Hi Efraín, sorry, I guess I wasn't able to find you on linkedin. My last name is ohlemann. Maybe this way around it might be easier. :-)
Hii! I'm working with Model 2 in PROCESS on SPSS and I'm wondering in which boxes to move my moderator variables into? I tried putting one of the moderators in Proposed Moderator W and the other in Proposed Moderator Z, but the dialog box won't let me run the analysis when I do that. Does this mean that one of the two moderators has to go under "M Variable(s)" - and if so, why?
If you look at the template documents provided with process, the variables used much match the variables in the template. Therefore, you must have an M and W if you use model 2, otherwise the macro will not know what to run to calculate the analysis.
One more question: If I use Model 2 and only one of the interactions was significant, am I supposed to re-run the moderation analysis but with only the significant moderator?
In some papers I have read they have "dropped" the non-significant moderator, but they seemed to be using regression analyses rather than the PROCESS plug-in
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Thank you so, so much for this video! After stumbling upon a 3-way interaction and trying to figure it out on my own with little success, this video pretty much single-handedly saved my sanity.
Glad to be of help!
Erin Buchanan, you're awesome! Thank you so much for making our lives easy!
My homophobic dissertation adviser kept asking for 1 interaction....but using your video I was able to do my own work to prove him wrong! Thank you (again) so much! Can you come teach our stats classes?!
An excellent video, very comprehensive. Thank you, Erin!
This is what i did. Entered DV in y (Outcome variable), IV in X , the mediating variables (3) in M variables, and each Moderating variables in proposed moderator y and Z
Super useful video! really good and always right to the point!!! Great!
Thank you very much for this video and your explanation :)
Glad it was helpful!
Thank you! I had so many doubts, cleared most on seeing your video. Would appreciate an interpretation of Moderated mediation in PROCESS.
Thank you Erin. Is there model 7 video (moderated mediation model) as you noted above. Also in this example, what would be the sample size. thanks
Great video! It's extremely clear.
Thank you sooo much for this great explanation!
No clue if it's an OS thing or just the version of PROCESS, but you could also get a graphing syntax along with the visualization data, to save you some hassle.
Thanks so much for this helpful tutorial Dr. Buchanan! I'm using three-moderation for my thesis and this was so helpful. Do know where I could find any examples about presenting the results of a three-way interaction? I can find plenty of examples for 2 way interactions but am struggling to work out how to present the three- way.
Here's an example of a paper we wrote with some crazy interactions: osf.io/k7dx5
Hi Erin,
Could you make one example for Model 2? The analysis and the interpretation of the interaction effect for model 2.
Thank you.
Thank you.
Thanks for your helpful explanation. Is it possible to add a third moderator to the additive multiple moderation model (Model 2) or multiplicative moderation model (Model 3)? I tried using the "z" variable name for the third moderator for both types of models, but the program did not recognize it.
This video is not new, but I am still wondering, if it´s possible for anyone to name any good literature for the outlier analysis at the beginning of this video. If I do it that way, I will need to give some "source" and I can´t sadly mention this great video:-) Thanks
The outlier ideas are from Cohen Cohen Aiken and West regression book, along with Tabachnick and Fidell multivariate book.
Hi Erin, this video is so helpful. thank you! Can you advise on power analysis for three-way moderation analysis?
I would probably try to power the highest interaction or the overall R2 using gpower in that case if you aren't familiar with simulation.
I love your videos. Saved me more than once. I noticed you didn't do power analysis (G*power) with this Moderation in PROCESS example. is it not appropriate to do so?
Hi Erin! This video - and all of your other videos - is great and has helped a lot. I have a quick question, if that's ok. You say below that in a model where X is continuous variable and M and W are dichotomous variables that you shouldn't mean centre. My model has X as a dichotomous variable and M as a continuous. Would you recommend mean centring any continuous variables (either X or M, but in my case M) by hand prior to PROCESS analysis and using them in the PROCESS model instead of the variable not mean centred? I hope that makes sense and thanks in advance for your help!
Thank you so much for this video. Are there any ways to use the johnson neyman technique when examining the three way interaction?
Not in process that I'm aware of ... but it's been a while since I looked at it. You'd have to break it down into the smaller interactions and rerun it would be my guess.
Thank you for the video. it is what I want to find. However, I cannot hear clearly about the formula (4/N-K-1) and (2k+2)/N, I dont understand why we need to compute this.
These are for outlier cutoffs for leverage and cooks. It's how you know what scores are beyond what normal variation in the sample might produce.
Hi Erin! First, loved your videos! Thank you so much- I learned more in a couple of hours listening to you than I did in an entire semester of stats. Question: I am currently using PROCESS model 9 for my dissertation (my supervisor's idea...) and interpreting the output is a little beyond my understanding. Can you recommend any other videos or papers that could help walk me through it?
Hi Erin,
great video- I am just wondering, for every Dependent variable I want to examine using PROCESS model number three should I check for outliers separately for each? i.e do I change the DV in the Analyse --> regression--> linear --> DV box and click Mahalanobis, cooks and leverage again for each?
Hope that makes sense.
Do you have a video for 3 way interactions in logistic regression using SPSS ?
I don't! I will add it to the request list. I do think they finally worked out my license, so I can make videos again!
Firstly great video i encountered a small issue:
When i run the model, i get the error: You have specified an M variable in a model that does not use it. Is there any way this can be resloved im running SPSS 28 and Process_v4.1.
Thank you for your time.
For that version of process, you'll need to check out the templates - they may not be the same numbers as the one I am using in this video.
Hi, i have the same problem. Did you figure out how to do it?
Hi Erin, I am trying to find an example of model 2 with interpretation. Do you have one of those? Thanks for your help.
I don't at the moment, but it is a popular request, so I will add it to my to do list.
Dr. Buchanan, thank you for your video! I have a question regarding the outliner procedure. You seems to only did the multivariate outliers. Should you also exam univariate outliers as well?
This is so helpful Erin, thanks a lot for this easy to follow video :) Thanks a lot. I was just looking for something like this , just is my DV a bivariate. Would you know if I could also proceed with Process on this?
Thanks so much for this helpful video. So, can we tell that the moderated moderation model is still significant since interaction 4 is significant (X*W*Z), even if interaction 1 (X*W) appears to be not significant in the entire model? I'm now a bit confused with my data; because the simple moderation model appears to be significant when I put the first moderator (W) only, but it appears to be not when I put the second moderator (Z) together.
That will happen since there are different variables when you only do simple versus 3 way. If the three way is significant, then you would say there’s moderation.
@@StatisticsofDOOM Thank you so much for your help. I really wanna appreciate you. Hope you have a wonderful day!
Erin, thanks for your video, it helps a lot! I have two questions. First, in my model X is continuous variable and M and W are dichotomous variables. Would you tick mean center option in that case as well? Second, can I interpret the effect shown in conditional effect table when a whole three way interaction in non significant?
Hello. In my model, i just got one significant maineffect and one significant Interaktion. All the other interactions and maineffects are not significant. Nevertheless, am i allowed to interpret the simple slopes or not? i don´t get it, thank you :)
Also how did you assume there is no multicollinearity issue based on correlation coefficient? Is there some kind of boundaries?
Generally most people consider above .9 as multicollinear. (Cohen Cohen Aiken and West).
Hi, Erin. I have become a big fan of your videos lately. Lifesaving. I noticed on this and another video the section about data screening and cleaning, not much is said about how to report what you find. What do I need to say in a manuscript about histograms and scatterplots if they look ok? perhaps you have resources for this kind of question available somewhere?
yes! even though it took e a while to notice you had responded.
Thanks for the video. Could I use PROCESS when I have 2 continous IVs and 1 catergorial IV (with four levels). Thank you very much.
I don't think so? Maybe? I think only categorical IVs can be used in Model 1 and 4? I would just try it and see what happens.
I have a quick questions. One of my lines actually bent. What does that mean?
A number typed wrong maybe?
Hello Erin. I have been following your videos and I found them very useful. I had a question though: I have 3 moderators, 2 DVs and 1 IV; with this configuration, is it possible to analyse using PROCESS? In my model the 3 moderators are affecting the link between the IV and the 2 DVs, and not each other or anything crazy like that. Any advice?
Would it be considered okay to analyse each moderator to the 1 DV & 1 IV link; and run PROCESS for each moderator and the two DVs?
I have a categorical variable with three groups - where would I place the two dummy variables in PROCESS?
The new version of process does interactions with categorical variables (and dummy codes them for you), so you could use that (then just stick the variable in X or M).
Is there a link for a how-to? I’ve tried looking. I have a continuous variable that I am splitting up into 3 groups to check differences between the 33, 66, and 100 percentiles.
@@eaterrr Splitting a continuous variable always makes me nervous :| I believe you can just denote that it's categorical with the categorical button options that are available in process, but you would want to create fake category column if you are splitting it from a continuous column. I can't quite remember if the categorical options work for a three-way interaction model, but I think so.
I’m nervous about it, too! I’m looking at whether two continuous variables (one is a moderator) will predict performance on a task - and I want to see if there are differences with the moderator variable between high, middle, and low groups split by those percentiles, but I’m trying to figure out the best way to do so. Thank you!
@@eaterrr Ok here's what I might tell you to consider instead. Figure out the score that a person would have on that variable at those points - like what is 33%, 66%, etc. Then shift the data around that point - normal simple slopes are 1 SD above and below the mean, and these are created by adding and subtracting a SD from the data, rather than creating subgroups of people who are only 1 above or below. Therefore, you could apply the same idea to your data by manually creating columns that mimic a 33% group, 66%, etc. So, if the 33% group scores a 4 and the mean is 8, you need to shift them up 4 points to be centered around the mean (assuming mean centered regression for easier interpretation). If the 66% group scores a 12 and the mean is 8, you should shift them down 4 points to see their effect. Then you would run the analysis again with those columns (one at a time, instead of the original column for that variable) and check out what happens to the other variables in the analysis for interpreting simple slopes.
Thank you so much, you are supper very easy steps and detailed explanation as well, but I wonder what if the moderator is a dichotomous moderator such as gender or nationality? because I have tried this but I got this note "The Johnson-Neyman method cannot be used with a dichotomous moderator". Do I have to run the normal regression? or I can use PROCESS as well?
Thank you you are an amazing professor
Hello Erin, do you have a guide on reporting results for moderated moderation?
I have two conditioning variables (W) acting separately on my moderator (M) which I intend to analyse separately. So my model is as per the conceptual diagram, except I'm running it twice (once for each (W) variable.
Thanks for any help you can offer
I'd mostly just check out guides for moderation on reporting - same idea, but with 3 ways interactions.
Do you identify outliers the same way when the dependent variable is dichotomous?
No. You would need to use logistic regression for categorical outcomes, which requires less assumptions. You could still use cooks, leverage, and standardized residuals though.
Could you make a video on checking threeway interaction with logistic regression? Including checking the assumptions and all? I could not find this anywhere. Thanks a lot!
@@iliagugenishvili7976 I have not - could add it to the list (things are very busy right now though). Three way interactions are always tough!
Is there any guidance that i can follow to analyse it
Are you able to use a control variable in Process Macro?
Yes in the covariates box (re-adding my answer back from when changed over youtube accounts, and they were deleted).
Hi Dr. Buchanan,
This is a terrific video! I've referenced it a number of times in a project I recently submitted. We just got the reviews back, and the reviewers are wondering if we can depict confidence intervals for the figures we created. Is there anyway to ask PROCESS for 95% CIs along with the plotting data that it already generates so helpfully?
Thanks!
:( not that I'm aware of. You might be able to use the SEs from the B values? I'm not 100% sure how you'd do it honestly.
No worries! I appreciate you taking the time to weigh in, and thanks again for the helpful video!
I kind of wanted to know, so I found this calculator:
www.danielsoper.com/statcalc/calculator.aspx?id=90
which will let you calculate the CI values around the estimated scores (standard error of the estimate = mean square error / residual).
However, it's still not totally clear to me how you'd add them into graph builder in SPSS - it seems to only want to calculate them ... you might be able to make the chart in Excel, which will let you add error bars manually.
Well that's quite helpful as I ended up making the original graph in excel anyways! One follow-up, if you don't mind: I'm assuming the MSE comes from the model summary portion of the process output. For the "residual" would I simply use the standard error of the three-way interaction term in the regression output?
So, you don't get this output in the version I'm using in the video (2.15) but if you have the newer process (2.16) you get the mean square error of the model (like the overall model not the predictor) under MSE. Different example below - but you'd use that number.
Model Summary
R R-sq MSE F df1 df2 p
.1849 .0342 .2046 1364.2116 7.0000 269720.000 .0000
Hello Dr. Buchanan, I also included three covariates into PROCESS model 3. One of the covariates ended up with p < .05 within the model. Is there anything that I should be cautious about? Could you give an example on how I should report it? Much Thanks. Will
I would just denote that the covariate was a significant adjustor of the DV - and then interpret the direction of the coefficient (i.e., positive or negative). If you want to interpret the model, you could say the adjusted Y is mediated by M etc.
Thank you. I have a question though. Can W be a categorical variable? I'm testing a model in which W is a 3-level categorical variable. But based on my output, it seems that PROCESS treated it as a continuous variable.
Yes it can, but I'm pretty sure PROCESS cannot handle that yet. You would have to do it manually.
Hi Erin, thank you very much for your video. I have a question about calculating the cut off point for cook distances. Do you have any references for that kind of formula? Unfortunately, I can't find it....
thank you ahead!! :)
Cohen, Cohen, Aiken, and West: www.amazon.com/Multiple-Regression-Correlation-Analysis-Behavioral/dp/0805822232
Hi!
First of all; thank you very much for all the SPSS PROCESS tutorials! Have been of great help so far!
I only have one question (I'm using model 4 and model 16 as an extension to that); I'm using Schwartz' PVQ as moderator (a prerequisite of this scale is that you mean center these values in a certain way before entering them in the analysis). To ensure that the other variables (i.e. the mediators) are also mean-cantered, I've done so manually.
The outcome shows that the interaction coefficients are quite high (all above 0) but the probabilities are in-significant (all above the 0.05 threshold). Does this mean I'm doing something wrong or is this a suitable outcome?!
Hope you can help me!
To me, that implies that there's a lot of error that the values are not significantly different from zero. So, the output you are describing is possible yes.
Yes, thanks again ! :D
I watched this awesome youtube video on three-way interaction to run the analysis for my data.
I am looking at the moderating role of adaptive Emotion regulation in the relationship between stress and well-being. I also plan to see if years of experience moderates this association. I mean adaptive emotion regulation and years of experience are moderators.
I select model three and put the X, Y, W, and Z in the correct boxes.
I am using Process v3.0.
In the output, I do not get the information for the conditional effect of X on Y at values of the moderators.
However, when I enter only one moderator (adaptive emotion regulation) and select model one, it gives me this information.
I would appreciate if you guide me as to why this happens.
You will not see the moderator simple slopes if the interaction is not significant. Try changing the options to always show the simple slopes.
What do you do if your IVs are highly correlated?
If they are basically the same variable (i.e. super correlated), then consider dropping one of them.
Should i select any other option?
Hey Erin, Thank you for this video. I am trying to find where my interaction is significant/nonsignificant at low and high values of my 3 way interaction. Since the conditional effects of X on Y at different levels of both moderators are only arbitrary points (and mine are not significant), I am trying to find out where exactly these points are both significant. I am confused as to how to interpret this using the JN. Since JN shows the effect of the interaction of X*M at different levels of W, how would I know exactly whether M is high when W is significant. For example, if my JN output shows that everything is significant below values of 3 for W, how would I know if M is high or low below values of 3? I would like to discuss what you did in the video- a chart with low and high values of both moderators interacting- but I can't figure out how to detect whether M is high or low at significant levels of W. Do you have any suggestions?
I mean, you wouldn't know if M is high or low, you would get the b value for M at that level of W. So you would say, as M goes up/down at less than 3 of W, then Y does blah blah blah. Or am I misunderstanding your question?
Hi. Thanks as always. Question. Suppose I've looked at the effects of gender (M,F) and learning context (University or college) at levels of Math Efficacy (-1,0,+1) on Math GPA.
So now i've plotted the data for visualizing conditional effects. But Im also interested in the plot for gender and learning context on Math GPA without levels of Math Efficacy (eg with the 3 lines for ME colapsed).
Would that be interpretable? If so, is it like having covaried Math Efficacy? How would one talk about it?
I've looked. The pattern is similar to that using the raw data, but cleaner.
Sure - you could create the two way interaction instead - it wouldn't be covaried, so much as averaged across Math Efficacy. You would say at average levels of ME (so 0 in your scale), here's what context by gender looks like for GPA.
Thank you very much for the video. You did a great job! I wonder if I have two categorical variables and one continuous variable( as the independent variables in the regression model, what model should I use? The 1SD above average for categorical variable just doesn't make sense here.
Thank you. But PROCESS did not seem to differentiate categorical vs. continuous variables here when I use Model 3. How would you be able to let PROCESS work on categorical variables and simple slopes for each category？ Many many thanks.
And what if one of the IV is a categorical variable with three levels?
Using the Process Plug in + SPSS, my research model is 9, consist of 1 IV, 2 Moderating , 3 Mediating and 1 VD. My question is is there a certain process to conduct the analysis using model 9?
i mean after inserting the variables in the system, what are the other options that i need to select to complete the analysis
+Erin Buchanan Thank you for your prompt reply and advice.
Could i use the PROCESS method with a 4-way interaction? What I'm looking at is Work Sector with 4 levels, with Embeddedness as the moderator and OCB as the independent. or am i only able to use the manual method?
You can check out the templates.pdf that comes with process - it appears to me that only three way interactions are supported at this time.
Thank you for this great video it helped me a lot with understanding three way interactions. I do have one question though. When I run my regression in SPSS (without macro) and just insert all two and three way interactions through product termes (I only have three independent variables) this leads to a significant three way interaction. When running the macro I don't get a three way interaction. Is there any possible explanation for this difference and should I, in your opinion, use the regular regression or use the MACRO in my dissertation? Maybe it is important to state that nothing was hypothesized on three way interactions (the work I'm doing is primarily focussed on main effects and two way interactions) and in the other two models (I'm testing the effect on attitude towards the brand, attitude towards the ad and Purchase intention in three different regressions) I don't get a significant three way interaction no matter what method I use.
+Erin Buchanan yes that is what I was thinking as well (that I should not see a difference). And yes I did center the variables (by subtracting the mean) before making my product terms. Do I have to use the centered variables for the single variables as well or only to form the product terms? Because for the main variables I did not use the centered variables. Could this explain the difference?
+Erin Buchanan great I will try that. And Thank you for your very fast reply! This helped me a lot.
I have a model similar to model 17. One Independent, Two Moderating, Three Mediating and One Dependent
Thank you for your kind help. i will review and hope to learn how to use it
Would this still work if all the variables were continuous?
Yes? I believe all these are treated continuously, but either way yes.
@@StatisticsofDOOM thank you for your super fast response Statistics of DOOM, I really appreciate it
Hi Erin!! Can I interpret the output for the three way interaction to report main effects for my dissertation? When I run the analysis without the interaction the main effect is not significant, but when I put it in process it is significant. I don't really care about the main effect, just the three-way interaction. Please advise :-)
+Erin Buchanan Thank you, Erin!
I will like to opened this discussion. I was told to never interpret the main effect when interaction terms are put in the equation, since they are tainted by that matter which render them non-interpretable. Should we interpret main effect at step 1 only? What about simple interaction in step 2 before the triple interaction? Can we interpret the two-way interaction in the final model when the triple interaction is added to the model? Any thought?
I will like to opened this discussion. I was told to never interpret the main effect when interaction terms are put in the equation, since they are tainted by that matter which render them non-interpretable. Should we interpret main effect at step 1 only? What about simple interaction in step 2 before the triple interaction? Can we interpret the two-way interaction in the final model when the triple interaction is added to the model? Any thought?
selected model 9, bootstrap samples 5000
Thank you very much, I follow your work and find all your videos extremely useful. I also want to ask you, what would be the best way to analize the data if one of the moderator is a categorical variable with three levels (two experimental and one control condition) and the other one is continuous. Should I use dummy variables like in a regression model, moderating by one dummy and controlling for the other one? or should I simply use dichotomous variable comparing one experimental condition with the control gruop? Thank you in advance. Regards, EGS
Thank you very much, it had been quite useful!!! Happy new year :)
+Efraín García Hi Efraín, may I ask which way you finally chose for your data? I am curious because me too I am trying to run a 3way interaction with a categorical variable with three levels as moderator. Are u on linkedin? Maybe we can connect there. I would highly appreciate an exchange on that matter with you. Thank you so much in advance!
Svenja Hi Svenja, sorry for the delayed answer. I run the model suggested by Erin, and also did the moderated moderation with the SPSS Macro Process (Hayes). I found both ways quite useful to conduct the analysis. I'm on LinkedIn with this same name, so we could exchange ideas there.
+Efraín García Hi Efraín, sorry, I guess I wasn't able to find you on linkedin. My last name is ohlemann. Maybe this way around it might be easier. :-)
Hii! I'm working with Model 2 in PROCESS on SPSS and I'm wondering in which boxes to move my moderator variables into? I tried putting one of the moderators in Proposed Moderator W and the other in Proposed Moderator Z, but the dialog box won't let me run the analysis when I do that. Does this mean that one of the two moderators has to go under "M Variable(s)" - and if so, why?
If you look at the template documents provided with process, the variables used much match the variables in the template. Therefore, you must have an M and W if you use model 2, otherwise the macro will not know what to run to calculate the analysis.
Thank you!
One more question: If I use Model 2 and only one of the interactions was significant, am I supposed to re-run the moderation analysis but with only the significant moderator?
In some papers I have read they have "dropped" the non-significant moderator, but they seemed to be using regression analyses rather than the PROCESS plug-in
I wouldn't drop variables if they weren't significant, as you had them in there for a reason. People might consider that "cheating".
I am confused, I think it is model 8
Hi, do you have any video on "Model 4"?
Yes - they are listed under mediation with process on the channel, there are a couple of them.
Hi, any video on "Model 2"?
No not yet, but you could treat it much like two model ones! I can add model 2 to the list.
the options are different in the new process! eek.
They are a little different yes - I have several videos on the new version, search Process 3 on my channel.