Kai Rödiger posted on Monday, September 30, 2013 - 2:29 am
Dear Linda and Bengt,
I'm quite new to MPlus and Multilevel Modelling but I figured out most of the basic stuff quite well. Now I have serious problems with a Cross-Level Interaction and need your help to know if it is possible at all to have a model like this.
So in short: I have LVL 2 measurements at the employee lvl (customer attitude) and LVL 1 measures at the customer LVL (perception of behavior).
I want this path to be moderated by the age difference between employee and customer modelled by (AE - AC). Each employee has up to 3 matched customers so there are up to 3 different values for Delta Age.
--------------------------Delta Age (I think this has to be a Within-Variable) -----------------------------| -----------------------------| -----------------------------| -----------------------------v Employee Behavior (L2) ----> Customer Perception (L1)
I tried various ways to define the slopes in the within and between part of the model but none of them worked out. Could you give me a hint if at all (and if yes how) such a relationship can be modelled?
Best regards and thank you very very much in advance, Kai
In multilevel modeling, you can't have a random slope on the highest level.
Kai Rödiger posted on Monday, November 25, 2013 - 3:02 am
thank you very much for your response. Do you have any suggestion how to solve this problem? Which method / approach might be best if cross-level interactions won't work here. Is a multilevel multigroup analysis a valid approach for this problem?
Yanxia WANG posted on Thursday, March 19, 2015 - 7:44 pm
I am new to Mplus, and recently met a similar problem which the level 2 moderator moderates the relationship between independent variable from level 2 and dependent variable from level 1. I did what Linda suggested, however, Mplus reported error with undefined zw (the interaction item). I really could not figure it out. Would you please help me to handle with this problem?
Bep Uink posted on Thursday, December 03, 2015 - 6:10 pm
Hello, I am running a 2 level model in the uni variate format, with experience sampling data. I am trying to predict an outcome at time 1, controlling for a co-variate a t-1. However, I do not want between-day lags (i.e. I do not want participants ratings in the morning to be predicted by their last rating on the previous day). However, I am not sure the syntax for this? I have thought of excluding observations the occur in the first time point of the following day, but these are are also used as t-1 covariates for the following time point. Any help would be very appreciated. Thank you.
You can consider three level modeling where the middle level = day. Alternatively and probably the easiest is to have 0 for that covariate in the data for the first observation in the day.
Bep Uink posted on Sunday, December 06, 2015 - 10:09 pm
Thank you, Tihomir. I am not clear on what covariate. To be more clear, I am trying to regress mood at time 1 onto event at time 1, controlling for mood at t-1. Because I do not want events from the previous nights' time point predicting the next mornings' mood, should I replace data for night time events with 0?
Ellen L. Hamaker, Conor V. Dolan, and Peter C. M. Molenaar (2002) On the Nature of SEM Estimates of ARMA Parameters, Structural Equation Modeling, 9(3), 347–368
Joran Jongerling, Jean-Philippe Laurenceau, Ellen L. Hamaker (2015) A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance, Multivariate Behavioral Research, 50:3, 334-349
Zhiyong Zhang, Ellen L. Hamaker, John R. Nesselroade (2008) Comparisons of Four Methods for Estimating a Dynamic Factor Model Structural Equation Modeling, 15:377–402,
hello, I am running a 2 level model with a cross-level interation. The question is I want get the coefficient of %within% level: s | Y on X. I am wondering if the code of %between% level:[s]; could get me the coefficient? If the intercept of s in the output is the coefficient? Here is my full code if you need: within=x; between=M; culster=group; analysis:type=twolevel random model: %within% s|Y on X; %between% [s]; s Y on M;
I'm trying to run a model with a cross-level interaction that I would normally run in a growth curve framework, but I need to use weighting within each wave and it's my understanding that I have to run the analysis in long form as a result.
Similar to a parallel analysis, I'd like to examine both the change in one variable and its intercept as predictors of change in another variable. I followed the example posted on here and in the user's manual for generating the random slope and examining it as a between-level outcome, but when I try to examine the random intercept as a predictor on the between-level, I get an error that says that I can't have a y-variable specified on the within level and also use it as an x-variable on the between level. I also have a between-level variable that I'd like to use as a moderator of the random intercept. So, I'm wondering if there is a way to do this in Mplus.
If not, is there any reason I couldn't examine moderation by generating the interaction terms with the define command and placing them as within level predictors similar to the code below? I realize it doesn't answer the exact same question statistically, but I think seems to test moderation in a way that is theoretically fairly similar. Thanks in advance!
a on time b INTtimeB INTtimeC INTtimeD BC BD timeBC timeBD ;
hello, I am running a 2 level model with a cross-level interation. The question is if the intercept of s in %between% level is the the effect of Y on X in %within% level? If not, can I get an exact coefficient of the effect of Y on X in %within% level? Here is my full code if you need: within=x; between=M; cluster=group; analysis:type=twolevel random model: %within% s|Y on X; %between% s Y on M;
Yes, but you shouldn't say "coefficient of the effect of X on Y" because there isn't just one effect - your random slope model says that the coefficient varies across clusters. So you should say "the mean of the effect of X on Y". The word "mean" is important here.