Yes, multilevel modeling is possible in SEM. See Muthén, B. (1994). Multilevel covariance structure analysis. In J. Hox & I. Kreft (eds.), Multilevel Modeling, a special issue of Sociological Methods & Research, 22, 376-398. You can request this paper from email@example.com.
See Examples 9.6 to 9.11 in the Mplus User's Guide which is available on the website.
robertav posted on Monday, September 03, 2007 - 9:29 am
Dear Authors, I'm carring on a SEM with both continuous and categorical(ordinal) indicators, with 5 continuous latent factors. I have 13428 observation. In order to save time I'm using the WLSMV estimator. How many observation I need to consider the WLSMV a good approximation of the ML estimator? Can you suggest me any reference?
And, as second step of my analysis, I’d like to add a multilevel structure. I have 5 continuous latent factors at the first level. Do you think it is feasible with Mplus? Any hints will be appreciate.
My data were collected for students nested within schools, but my primary interest is to do a single-level SEM model and not multilevel one. However, 7 out of 39 indicators showed significant school-level variance (which implies I need MLM). Since I am not interested in MLM I would like the dependence of the data to be taken into account, but not modeled.
1. Is type=complex appropriate analysis for this?
2. Does it produce the same info in the output as type=missing? I also have some missing data - how to account for that?
3. Do I have to grand-mean center those 7 variables first before using single-level SEM (and eliminate cluster-level variance)?
1. SEM models are typically not "aggregatable" in the sense of Muthen & Satorra (1995), which means that Type = Complex should not be used but instead Type = Twolevel. This happens if factor loadings are not the same on the two levels. I would recommend a simple random intercept twolevel SEM using the analysis steps of
Muthén, B. (1994). Multilevel covariance structure analysis. In J. Hox & I. Kreft (eds.), Multilevel Modeling, a special issue of Sociological Methods & Research, 22, 376-398. (#55)
2. The Mplus Twolevel analysis does do Type = Missing as the default, which uses the standard "MAR" under ML approach - using all available data (often called "FIML").
3. No. And grand-mean centering does not take care of clustering.
You will find a handout about Mplus multilevel factor analysis and SEM on our web site under Mplus Short Courses Topic 7, from the recent course at Johns Hopkins in March. The video of the course will soon be available for free watching on the web.
newuser posted on Monday, January 04, 2010 - 7:43 am
Hi, I am running a multi-level SEM mediation model (2-1-1), the M variable is a second order variable. I have successfully run similar (1-1(second order)-1) models with the same M and Y variables. However, running into problems when I try the (2-1-1) using a higher level X. I am wondering if the second order variable(M)is creating the problem, and if I should reduce it to a first order variable?
Here is how the 2-1-1 model looks %within% Y by y1 y2 y3 M by m1 m2 m3 (second order) M1 by ma mb mc (first order) M2 by md me mf (first order) M3by mg mh mi (first order) X by x1 x2 x3 Y on M %between% X by x1 x2 x3 Yb by y1 y2 y3 Mb by m1 m2 m3 (second order) M1b by ma mb mc (first order) M2b by md me mf (first order) M3b by mg mh mi (first order) Yb on Mb X Mb on X
Mplus can both impute data and analyze imputed data for two-level analysis.
For analyis, each imputed data set should be in a separate file and TYPE=IMPUTATION should be used. See Example 13.13 in the Version 6 user's guide.
SY Khan posted on Thursday, December 19, 2013 - 12:48 pm
Dear Dr. Muthen,
I am testing a 2-level SEM. The level 1 outcome variable is a continuous latent variable (JS). On level 2 I have another continuous latent variable (EMPPART).
I have categorical factor indicators at the WITHIN level. On the between level I have Binary factor indicators for level 2 factor and random intercept factor indicators. And no covariates on any level in the initial model! I am following example 9.9 of users guide 7.11.
I am using the following syntax:
Missing= all (999);
FJS BY JS1-9;
FJS2 BY JS1-9;
EMPPART BY BRGROUP QLTYCIRCL INFOSHAR;
FJS2 ON EMPPART;
1-Does this syntax suffice?
Also kindly explain:
2- why do we want to save data in this analysis? I.e. What can we explore from the saved data at a later stage?
3- as I can not use BOOTStRAP with type= TWOLEVEL what can I use instead? Is there a need to use BOOTSTRAP or other similar techniques to make analysis stronger?
It sounds like you are trying to write the output to a directory that does not have write privileges. Windows 7 does not allow any files to be write under the directory of Program Files. Try moving the input and data to c:/
SY Khan posted on Friday, January 10, 2014 - 7:33 am
Hi Thanks for your prompt reply.
I am running this input file in windows 8.1. Is that a problem? Is Mplus NOT supported in Windows 8.1?
Mplus is compatible with Windows 8. The problem is likely what I describe above for Windows 7.
SY Khan posted on Tuesday, January 21, 2014 - 6:25 am
Hi Dr. Muthen,
Sorry for coming back with the same problem over and over. I have tried moving my data and input files in C directory as suggested in your previous post above.
This time too the test ran for approx. five days and then aborted unexpectedly . The message was that " Mplus stopped working unexpectedly but no other error message was given that suggested any modifications to the input instructions etc.
I would be greatly thankful if you could suggest remedies as this is one of the main model in my analysis. How can I make this model run successfully?
Gizem Erdem posted on Tuesday, April 22, 2014 - 2:41 pm
Hi Dr. Muthen,
I am fitting a 1-1-1 multilevel SEM model with the following syntax:
Title: Multilevel SEM Data: file is MSEM.FORMPLUS.dat; Format is 3(F8.0) 25(F8.2); Variable: Names are ID agency t1weight ATS2 CWS2 MSE2 MRQ2; Usevariables agency ATS2 CWS2 MSE2 MRQ2; CLUSTER = agency; ANALYSIS: TYPE IS TWOLEVEL RANDOM;
MODEL: %WITHIN% MCQ2 ON MSE2(A); MSE2 ON ATS2(D); MSE2 ON CWS22(E); MRQ2 ON MSE2; MRQ2 ON CWS2;
%BETWEEN% AMTRNST2 ACWSPP2; MRQ2 ON MSE2(AA); MSE2 ON ATS2(DD); MSE2 ON CWS2(EE); MRQ2 ON ATS2; MRQ2 ON CWS2;
MODEL CONSTRAINT: NEW (indda indea indddaa indeeaa ); indda=d*a; indea=e*a; indddaa=dd*aa; indeeaa=ee*aa; Output: TECH1 TECH8 CINTERVAL; Savedata: file is MSEM.FORMPLUS.sav Save is fscores;
The model gives the following errors:
THE ESTIMATED WITHIN COVARIANCE MATRIX IS NOT POSITIVE DEFINITE AS IT SHOULD BE. COMPUTATION COULD NOT BE COMPLETED.
THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ERROR IN THE COMPUTATION. CHANGE YOUR MODEL AND/OR STARTING VALUES.
I want to test whether the effect of a time-invariant covariate "X" at L2 on a time-varying outcome "Y" varies over time in a 3-level model with random intercepts only. Can I test this by creating an interaction term between L2 predictor and time at L1? Synax is:
I ran a msem with two latent variables and got the following error. I didn't see anything in the results suggesting a problem, however.
THE MODEL ESTIMATION TERMINATED NORMALLY
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.