COND 1=family, 0=group I'm thinking that the mean of change for group = 7.606; family = 7.606+(-.444) = 7.162. As age goes up, change in anxiety goes down (i.e., anxiety changes less). I am not sure how to interpret the product term. The reason I am asking this question here is because I have an additional question about missing data. I got this finding using FIML in MPlus. However, when I imputed the data in SPSS and used other tools to test the interaction I got a non-significant result. I am not sure if it is okay to use FIML when testing the above. Sorry if the questions sound basic, but whoever I asked in my program did not know and I need to figure it out for my dissertation. Thank you very much.
FIML and imputation are asymptotically equivalent but may differ in practice with small samples. You may not have the same sample size in Mplus and SPSS. Mplus may delete observations with missing on x.
The interaction between age and cond is signficant in the prediction of anxiety. This means that treatment behaves differently with respect to age. For cond=1, the coefficient is -.880 + .938, a small positive value. For cond=0, it is -.880, a negative value.
Thank you. I get the full sample in MPlus by bringing in the covariates x-y*; as advised here. I also get the full sample in SPSS when I impute the values, but I don't get the same results. Could clustering be the reason? MPlus accounts for clustering whereas SPSS doesn't. It would be interesting to know in what way, if any, clustering makes a difference in moderation analyses.
Based on the above -- would it be accurate if I say that for cond = 1 -- for every one unit that age goes up, anxiety goes up by .06. This is a small positive value.
for cond = 0 -- for every one unit that age goes up, anxiety goes down by -.880 units.
I'm not sure if the way I wrote this makes sense. I should say that I used
Anxiety Post on Anxiety Pre Anxiety Post on M_Age Anxiety Post ON M_AgeXCond as the syntax.
And one more thing -- I'm running some analyses with categorical moderators. How can I get the simple main effects and the means so I put them in a 2X2 table? It would make it easier for me to interpret the findings. I will then be able to report simple main effect contrasts and interaction contrasts.
Thanks very much! This helps me a lot toward my dissertation.
If you do Type = Complex in your Mplus run to deal with clustering, the parameter estimates are not affected, only their SEs. So that wouldn't be the source of the difference visavis SPSS.
Yes, your statements about how age affects anxiety are correct.
You should also have Cond on the right-hand side of your ON statements, which you did in your initial post.
You ask about means in a 2 x 2 table, so you must be asking about a binary moderator with your binary Cond variable. This is regression with two binary covariates. It is straightforward to get the 4 means by plugging in the 0's/1's for the two variables as in regular regression.
Anke Schmitz posted on Wednesday, February 19, 2014 - 10:17 am
I have a signifikant moderation between two observed variables (one dichotomous and one continous). Where can I see how large the effect is for students with mean values, -1SD and +1 SD? Regards Anke
Handouts for New Developments in Mplus Version 7 The handouts for the Mplus Version 7 workshops at Utrecht University on August 27-29, 2012 are posted here in 4-per-page format and in regular format:
Part 1: 4-per-page Regular Part 2: 4-per-page Regular Part 3: 4-per-page Regular
You also find a video of that there.
Anke Schmitz posted on Saturday, February 22, 2014 - 6:35 am
Bengt, thank you very much! It worked quite well. I have one more question: How do I compare a model without interaction (just with main effects) with a model with interaction term? There are no model criteria in the output. Just those information: MODEL FIT INFORMATION
Number of Free Parameters 36
H0 Value -6874.044 H0 Scaling Correction Factor 1.0009 for MLR
Thanks for your replys on feb, 22. It worked quite good. Is there any chance to compare moderationsmodels between two observed groups (1 versus 2)? I just want to know if the interaction effects differ between two groups of students or if they are similar. I tried the "grouping is" command, but it did not work. Do I have to split the data file and generate two separate models? Regards Anke
I have a question. I am doing moderation analysis with latent variables using Mplus. If my main predictor and outcome is not signification but my interaction term is, can I still interpret the significant interaction term?
Thank you, Dr. Muthen. I have a quick question to ask:
I am reading an article by Maslowsky et al. In this article, it is indicated that when working with latent interaction terms, one must estimate a structural model in two steps: (1). A structural model without the latent interaction term (model 0) & (2). a structural model with latent interaction term (model 1).
Doing this in two steps allows for testing of model fit (Klein & Moosebrugger, 2002; Muthen, 2012).
My main question is that if I am looking at B1 as my moderator in the relationship between X and Y, do I include B1 as a predictor along with X in model 0 (that is the model that does not include the interaction term)?
Thank you, Dr. Muthen. Does the new version of Mplus produce standardized path coefficients when including an interaction term in a structural equation model with latent variables?
Sara Namazi posted on Wednesday, June 06, 2018 - 11:06 am
Hi Dr. Muthen,
I wanted to follow-up with you. In my reading about latent interaction terms, I learned that Mplus does not produce standardized beta coefficient when including latent interaction terms in your model. However, I am able to get standardized beta coefficient using the following command in Mplus:
OUTPUT: STAND TECH1 tech4 sampstat PATTERNS RESIDUAL modindices ;
Not sure If I am missing something or whether my understanding is correct.
Dear Dr Muthen, I have a question regarding the interpretation of a moderation model output. I have 2 indipendent latent factor: a, b and a dipendent measured variable c. I ran a simple model: c on a b; and I had in the output a negative estimate for both: c on a -0.300 c on b -0.100 I decided to run a moderation model because I assumed that variable a could interact with b and produce a stronger effect on c: (high a and high b --> lesser c) On the contrary I had in the output: c on a -0.348 c on b -0.108 c on ab +0.250 In the interaction a and b the estimated relation with c is reversed. That does not make any sense in theory. Am I interpreting the output in a wrong way?
I have a question about latent moderated structural questions. So to test interaction terms in Mplus, Maslowsky et al. 2015 article indicates that to estimate latent moderated structural equations, you should run a model without the interaction term (called Model 0) and then add a model with the interaction term (called Model 1) to compare model fit using the XWITH Command in Mplus.
So if I am looking at the relationship between a --> M --> b and a--b so a simple mediation, would that mean that my model 0 is:
M on a b on M b on a
and model 1 (v is my moderator pointing to the path going from a to M):
Dear Dr Muthen, I'm running a moderation model and the moderation variable is a measured variable. When I run the model it takes 10 minutes to complete. When I used a moderation variable that is a latent variable in the same model it takes 3 minutes to complete. Is that normal or am I doing in the wrong way?
I write you the model with the measured moderation variable below: