Andy Cohen posted on Thursday, July 12, 2007 - 8:40 am
I am conducting a 2 level analysis in which I would like to include interactions between two main effect variables in the within portion of my analysis. I am defining the variables using the DEFINE command (e.g. IntA_B = A * B). The underlying variables for the interaction terms need to be group mean centered. I have already specified group mean centering for these variables in the VARIABLE command (as that is necessary for the use of the TWOLEVEL option in the ANALYSIS command, but am wondering if the DEFINE command will use the original or centered form of the variables.
Yes, the order of operations matters. Any transformations using DEFINE should be done first and the data saved. The centering should be done on the saved data.
C. Lechner posted on Friday, February 17, 2012 - 6:38 am
Ok, thank you very much for your answer! May I ask two additional questions:
#1: I suppose the same would apply to interactions between a latent variable and a manifest variable computed using the XWITH command? I would first compute the interaction, save it, and then center it along with the other variables in the model?
#2: Assume I have a multilevel model with two predictors and an interaction between the two on level 1. One of the two predictors that interact have a random effect, the other is treated as a fixed effect. The interaction thus has to be treated as a random effect as well. However, do BOTH predictors that are part of the interaction have to be treated as random, or will it suffice to treat one as random and the second one as fixed (as I would assume)? Technically, both works fine, because in a regression or path model, Mplus will treat these interactions as any other variables. But is it correct?
Just so that I'm clear, it seems like there is no way use the CENTER command to group mean center a set of variables and then use them in a DEFINE statement in the same procedure. For example:
variable: names = AgencyID Gender Age T employ enroll engage housegb incany totsup infsup formsup anysup anyinf anyform; cluster = AgencyID; missing are all .; usevar = Gender Age T incany formsup Intx; categorical are incany; within = T formsup Intx; between = Age Gender; center = grand mean (Age) group mean (T formsup);
Define: Intx = T*formsup;
This would compute the Intx variable before group mean centering T and formsup and this isn't what I want. Is there any way around this? The manual states that the CLUSTER_MEAN option also cannot be used with subsequent DEFINE statements. So I guess that leaves me with using the SAVEDATA command to save the group means, then running another procedure using those saved variables to compute the group mean centered values. Save that data for a final time, and run a third procedure calculating the interaction term with the saved, group mean centered values. Is that correct? Or did I add in an extra step somewhere. Thanks for all your help!
Hi, I am running a two-level model to test group differences before and after an intervention. I'm entering my own time variable to represent the number of days since baseline (see sample script below). I noticed that Mplus is automatically centering my time variable. Is there a way to not center it? I would like the baseline (T1) to = 0, as this is more meaningful. Thanks!
DATA WIDETOLONG: WIDE = DV_T1 DV_T2 DV_T3 | T1 T2 T3 ; LONG = DV | timeB ;
IDVARIABLE = person ; REPETITION = time ;
Variable: Names are ID group DV_T1 DV_T2 DV_T3 T1 T2 T3 ;
Usevariables are group DV timeB person ;
Cluster = person ; Within time timeB; Between = group ;
Fit statistics are fine except for L2 srmr (above1.0). When I add groupmean centering to x1 and x2 in a subsequent run, the L2 srmr improved substantially. What could be the reason for this? Should I center? I placed x1 and x2 at within as their ICCs were very low. Thanks..
I would feel more comfortable with other fit indices for two-level modeling. Stay with chi-square, RMSEA, and CFI.
Kirill Fayn posted on Thursday, May 30, 2013 - 12:45 am
i am trying to run my first MLM on mplus and am having difficulty centring my level one variables.
The model and the error is below:
USEVARIABLES ARE Interest Cope1 Nov1 ZOpen ZInt; WITHIN = Cope1 Nov1; BETWEEN = ZOpen ZInt; MISSING ARE all (-9999); CLUSTER = subject;
DEFINE: CENTER Cope1 Nov1(GROUPMEAN); ANALYSIS: TYPE = TWOLEVEL RANDOM; MODEL: %WITHIN% IntCop | Interest ON Cope1; !need to make these factors IntNov | Interest ON Nov1; %BETWEEN% Interest IntCop IntNov ON ZOpen ZInt OUTPUT: TECH8 SAMPSTAT;
*** ERROR in DEFINE command Error in assignment statement for CENTER
Could you please help. The syntax seems to be right so I am guessing I can't centre these variables for some reason.
What version of Mplus are you using? If it is earlier than Version 7, the CENTERING option was in the VARIABLE command. If it is Version 7 or later, please send the output and your license number to firstname.lastname@example.org.
Katerina Gk posted on Wednesday, October 09, 2013 - 4:42 am
Hi, I have 5-factor model(job sat.) and self-eff.( 3-factor model).I want to aggregate by school the observed variables of the job sat. and self-effi. in the between level. If I use CENTERING = GRANDMEAN (x) is enough to understand that I need to aggregate at between level?the observed variable are the same in two levels....
Missing are all (999); CLUSTER IS sxoleio; DEFINE: CENTER = e1..a1..(GRANDMEAN) ANALYSIS: TYPE IS TWOLEVEL ; ESTIMATOR = WLSMV; MODEL: %within% er1_w by e1@1... ; er2_w by e7@1... ;
If you want an aggregated variable on the between level, use the CLUSTER_MEAN option of the DEFINE command to create it. See Example 9.1 where using this variable versus a latent variable decomposition of the individual-level variable is discussed.
Katerina Gk posted on Wednesday, October 09, 2013 - 11:55 am
In relation to the issues of centering in multilevel models raised above, I have two questions:
1. If any transformations (e.g., interaction between observed variables) are made before centering, it means that the interaction term does not use standardized scores of the products, which violates a basic requirement for the computation of any interaction term. How can I bypass this problem? Or is it not a problem?
2. When using grandmean centering (or no centering) for my within-level predictors, the fit of the model is considerably higher than when using groupmean centering. What could be the reason? Is there a preferable centering method for within-level predictors?