-Code: DATA: FILE IS ...; listwise=on; VARIABLE: NAMES ARE pcid pcTRT ptid ptwaira2 ptkasra2 nucwai nucsdm; USEVARIABLES ARE pcTRT nucwai nucsdm; BETWEEN IS pcTRT; CLUSTER IS pcid; MISSING ARE ALL (-9999); ANALYSIS: TYPE IS TWOLEVEL RANDOM;
MODEL: %WITHIN% nucwai*72 nucsdm*124; sb | nucsdm ON nucwai;
%BETWEEN% pcTRT*.3 nucwai*26.0 nucsdm*52.0; nucwai ON pcTRT(a); nucsdm ON nucwai(bb); nucsdm ON pcTRT; [sb](bw); sb WITH pcTRT nucwai nucsdm;
-Problem: Without listwise deletion, I get: *** FATAL ERROR THIS MODEL CAN BE DONE ONLY WITH MONTECARLO INTEGRATION.. -After adding ALGORITHM = INT; Integration=MONTECARLO; to ANALYSIS section: *** ERROR in MODEL command Observed variable on the right-hand side of a between-level ON statement must be a BETWEEN variable. Problem with: NUCWAI *** ERROR The following MODEL statements are ignored: * Statements in the BETWEEN level: NUCSDM ON NUCWAI
If you have missing data on nucwai the model includes the product of two latent variables and there is no explicit likelihood expression - therefore you have to use numerical integration and a completely different algorithm, i.e., the missing data makes the algorithm quite a bit more complicated.
You can resolve your problem by using this equivalent model
%BETWEEN% pcTRT*.3 nucwai*26.0 nucsdm*52.0; nucwai ON pcTRT(a); nucsdm ON f(bb); nucsdm ON pcTRT; [sb](bw); sb WITH pcTRT nucwai nucsdm; f by nucwai@1; nucwai@0; f*1;
You should also consider using multiple imputation as an alternative estimation approach, that way you avoid numerical integration.
1 - I am trying to calculate a 2-1-1 multilevel mediation model where the mediator is binary. Will this input do this correctly and also calculate the indirect effect correctly? Conscie is the predictor x, poscons the mediator m, and success the outcome y.
… USEVARIABLES = poscons success conscie ID ; missing = poscons (-9) success (-9) conscie (-9) ID (-9) ; BETWEEN IS conscie ; CLUSTER IS ID; ANALYSIS: TYPE IS TWOLEVEL; ESTIMATOR = WLSMV; MODEL: %WITHIN% poscons success; success ON poscons(b); %BETWEEN% conscie poscons success; poscons ON conscie(a); success ON poscons(b); success ON conscie; MODEL CONSTRAINT: NEW(indb); indb=a*b; OUTPUT: TECH1 TECH8 CINTERVAL;
2 - I am using the WLMSV estimator. Is this correct? Or should I use ML or MLR?
3 - If I use WLMSV, how do I interpret the regression coefficients: Are they probit regression coefficients? Or can they be interpreted like regression coefficients from a linear regression?
I am trying to do something similar to the above. However, my X is measured at L2: middle managers (MMs) who are rating their superiors. Several MMs are rating the same superior. I also have covariates at each level. I have written the following syntax, but it will not run.
We need to see your full output - send to Support along with your license number.
Derek Boy posted on Wednesday, January 10, 2018 - 12:07 pm
Dear Dr. Muthen
I am considering a multilevel mediation analysis (while still pending the field data), with outcome EIP to be modeled at all three levels, mediator EEO also to be modeled at all three levels, and predictors EEL SGL CCT modeled at L1, L2, and L3 respectively. The codes that I wrote are as below. Would you please kindly let me know if I am doing it right or not? And, I am particularly nervous on my calculation of the indirect effect. Many many thanks.
WITHIN = EEL; BETWEEN = (S_ID) SGL (C_CN) CCT; CLUSTER = C_CN S_ID; ANALYSIS: TYPE = threelevel; MODEL: %WITHIN% EIP ON EEO (b1) EEL (c); EEO ON EEL (a); %BETWEEN S_ID% EIP ON SGL EEO (b2); EEO ON SGL; EEO; EIP; %BETWEEN C_CN% EIP ON EEO (b3); EEO ON CCT; EEO; MODEL CONSTRAINT: new(direct indirect); indirect = ((a*b1)+(a*b1*b2)+(a*b1*b2*b3)); direct = c; OUTPUT: sampstat tech1 tech8 cinterval;
I don't understand the model. What is (are) you mediator(s) and what is (are) your ultimate outcome(s)?
Why is there no regression of T_DRes on T_Ex on the Between Group level while you have it on Within? Why do you call the path from Agg_Asc to T_Ex "b" on Between Group when you call the path from T_Ex to T_DRes "c" on Within?
Is it possible to run a 1-1-1 MLM with 3 DVs simultaneously?
If so, would it consist in including all Y1, Y2 and Y3 together in the model?
%WITHIN% m ON x(aw); Y1 ON m(bw1); Y1 ON x; Y2 ON m(bw2); Y2 ON x; Y3 ON m(bw3); Y3 ON x; %BETWEEN% x m y1 y2 y3; m ON x(ab); y1 ON m(bb1); y1 ON x; y2 ON m(bb2); y2 ON x; y3 ON m(bb3); y3 ON x; MODEL CONSTRAINT: ! NEW(indb1 indb2 indb3 indw1 indw2 indw3); indw1=aw*bw1; indb1=ab*bb1; indw2=aw*bw2; indb2=ab*bb2; indw3=aw*bw3; indb3=ab*bb3;
I ran my model accordingly. However, I have trouble understanding what to do with model fit indexes.
Chi-Square Test of Model Fit
Value 0.000* Degrees of Freedom 0 P-Value 1.0000 Scaling Correction Factor 1.0000 for MLR
Therefore, all the indexes are "perfect" (RMSEA = 0.000; CFI = 1.000, etc.). I don't think my model is just identified (I have 600 observations, for a 1-1-1 model with 3 DVs; 35 free parameters).
I understand I cannot use these indexes to assess my model. I have checked the website for the chi-square difference test computation using MLR. But I don't understand which model should I compare this one against... Could you help me with this?
Finally, even if I get a "chi-square" value, I will still be missing the other indexes, is this correct?
The possibility to test model fit by SEM approaches is not related to the sample size but to the number of variances and covariances among the variables. Your model is just-identified (saturated; all paths are present in the model) and is therefore not testable by regular SEM approaches.
I am estimating a MSEM, including a 1-1-1 and a 1-2-1 mediation simultaneously.
Three questions: 1) Is this the correct way of estimating the model? 2) How should I interpret the between-level indirect effect of the 1-1-1 mediation? Is it the effect of the aggregate of X on the aggregate of Y via the aggregate of M. Or should I not interpret this indirect effect and is it only estimated to get an unbiased indirect effect at level 1? 3) Y is ordered categorical. Should I use the WLSMV estimator to take ordinality into account? If I do, Mplus asks me to cluster mean center the level 1 variables and add them on level 2 (unconflated MLM), instead of MSEM (which is preferable). Should I go for MLR anyway?
X = level 1 independent; M = level 1 mediator; Z = level 2 mediator; Y = level 1 dependent
NAMES ARE x m1 m2 z y district weight; USEVARIABLES ARE x m z y; WEIGHT IS weight; CLUSTER IS district; BETWEEN ARE Z;
ANALYSIS: TYPE = twolevel random; ESTIMATOR = MLR;
MODEL: %WITHIN% y x m; y ON x; m ON x (a); y ON m (b);
%BETWEEN% x m z y; y ON x; z on x (c); y on z (d); m ON x (e); y ON m (f);
MODEL CONSTRAINT: NEW(ind1 ind2 ind3); ind1=a*b; ind2=c*d; ind3=e*f;
I try to estimate a model similar to the one mentioned above from 2016 (with outcome at L1, mediator at L2, and predictor at L3) but with the mediator beeing a latent variable modelled on all three levels (similar to a doubly latent approach). Could you please let me know if the code is correct (particularly with regard to the model constraint)?