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