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 Jindow Joseph posted on Monday, January 21, 2013 - 7:03 am
Hello Experts,

I have a predictive model as below :

X,Y,Z are the independent variables
P,Q are moderator variables
D is the dependent variable

P & Q moderates the relation X --> D

Q also moderates Y --> D

Q also moderates Z --> D


All variables are measured by 7 point Likert scale.

X has 8 questions;(X1-X8)
Y has 9 questions;(Y1-Y9)
Z has 6 sub-categories -- each sub category has 3-6 questions each (Za1-Za6, Zb1-Zb3,Zc1-Zc3,Zd1-Zd4,Ze1-Ze3,Zf1-Zf5)
D has 5 questions (D1-D5)
P has 12 questions (P1-P12)
Q has 4 sub-categories -- each sub category has 4 questions each (Qa1-Qa4, Qb1-Qb4,Qc1-Qc4,Qd1-Qd4)

Please tell me the best way to analyze this model.
 Bengt O. Muthen posted on Tuesday, January 22, 2013 - 2:04 am
Perhaps you want to explore using factor models for your multi-question constructs. This general modeling question is better discussed on SEMNET.
 Jindow Joseph posted on Wednesday, January 23, 2013 - 4:10 pm
Hello Sir,

Thanks a lot for the guidance. Sorry for not being specific. Is LMS the best option to analyze this model ? Or are there better approaches in Mplus ? Thanks..
 Bengt O. Muthen posted on Wednesday, January 23, 2013 - 9:23 pm
If you have latent variable interactions, the LMS method is available in Mplus. It is the only automatic method for this in Mplus (there are many other methods).
 Jindow Joseph posted on Thursday, January 24, 2013 - 4:33 pm
Thanks a lot,Sir..
 Jindow Joseph posted on Sunday, February 03, 2013 - 12:44 pm
On the below syntax:
Model:
C by C1* C2* C3*;
T by T1* T2* T3*;
M by M1* M2* M3*;
U by U1* U2* U3*;
S by S1* S2* S3*;
I by I1* I2* I3*;
R by R1* R2* R3*;
O by O1* O2* O3*;
F by F1* F2* F3*;
W by W1* W2* W3*;
SE by SE1* SE2* SE3* SE4*;
OE by OE1* OE2* OE3* OE4*;
UE by UE1* UE2* UE3* UE4*;
RE by RE1* RE2* RE3* RE4*;

JD by S* I* R* O* F*;
E by SE* OE* UE* RE*;

JDE | JD XWITH E;
WE | W XWITH E;
CE | C XWITH E;
ME | M XWITH E;
MU | M XWITH U;
T ON JD W C M E U JDE WE CE ME MU;
JD@1;W@1;C@1;M@1;E@1;U@1;

Analysis:
TYPE = RANDOM;
ALGORITHM = INTEGRATION

I am getting a "NOT ENOUGH MEMORY SPACE" error.Kindly help..Should I shorten the model?
 Bengt O. Muthen posted on Sunday, February 03, 2013 - 5:38 pm
That's a very complex model. You could introduce one XWITH at a time to see if that interaction is needed.
 Jindow Joseph posted on Wednesday, February 06, 2013 - 4:46 am
Thanks.. Do you mean to say that I could 'split' the model into different parts and analyze them separately as below:

Model:

T by T1-T3*;
S by S1-S3*;
I by I1-I3*;
R by R1-R3*;
O by O1-O3*;
F by F1-F3*;
SE by SE1-SE4*;
OE by OE1-OE4*;
UE by UE1-UE4*;
RE by RE1-RE4*;
JD by S* I* R* O* F*;
EI by SE* OE* UE* RE*;

JDEI | JD XWITH EI;
T ON JD EI JDEI;
JD@1;EI@1;

Analysis:
TYPE = RANDOM;
ALGORITHM = INTEGRATION

Here I have taken only those variables relevant for this XWITH relation. Hope that is good enough.

As the model is 'split' into different parts , does it defeat the purpose of SEM?
 Linda K. Muthen posted on Wednesday, February 06, 2013 - 12:05 pm
Leave your model as it was except for the following terms:

JDE | JD XWITH E;
WE | W XWITH E;
CE | C XWITH E;
ME | M XWITH E;
MU | M XWITH U;

Run the model with each of the above statements separately. Don't change anything else. You will likely find that all of the interactions are not significant. As a last step add the ones that are significant.
 Jindow Joseph posted on Wednesday, February 06, 2013 - 3:22 pm
Thanks... If more than 2 interactions are found to be significant,there will be a road block (with 3 interactions , the total integration points are around 50,000).

So is it fine if I avoid the last step of including all interactions in one shot.Of course then analysis have to be based on the individual run of each interaction(May be I could also select pairs of significant interactions and run again--not sure whether that's correct)

Or is there an alternate approach in MPlus that I should check out.
 Linda K. Muthen posted on Wednesday, February 06, 2013 - 4:33 pm
You should do them one at a time. Do the first one alone; do the second one alone; etc. Then include all of the ones that are significant alone in the last analysis. Use INTEGRATION = MONTECARLO (5000).
 Jindow Joseph posted on Thursday, February 07, 2013 - 2:15 pm
Thanks a lot, Ma'am...
 Jindow Joseph posted on Friday, March 01, 2013 - 5:03 pm
Hello Dr.Muthen,

How can the model fit be best analyzed for LMS in Mplus ? If the factor loading of the interaction term is more than the factor loading of individual items , can I say that the interaction is significant and it exists?

Or should I run the model first without interaction term and then run with interaction term and compute 'Difference Testing Using the Loglikelihood' (TRd).How significant should this value be so that I can say that the model with interaction effect has a better fit? Should I as well compare the AIC/BIC values of these models?
 Linda K. Muthen posted on Friday, March 01, 2013 - 6:13 pm
The z-test for the interaction term is the same as doing a difference test between the model without the interaction and the model with the interaction. You need to decide the level of significance that is required.
 Jindow Joseph posted on Monday, March 04, 2013 - 6:02 pm
Thanks Ma'am..
 Jindow Joseph posted on Friday, April 19, 2013 - 5:35 am
Hello Dr.Muthen,

As multigroup analysis is not available with integration what option would be the best to study the difference between the groups men and women.My total sample size is 341-- so can I split the file into two -- one for men and another for women and then run the LMS codes separately.As the sample size would be low for men and women separately, is it advisable to do so...?
 Linda K. Muthen posted on Friday, April 19, 2013 - 4:06 pm
I think you can do multiple group analysis using TYPE=MIXTURE with the KNOWNCLASS option. The issue of small sample size is the same whether you analyze the groups separately or do a multiple group analysis. The issue is the number of parameters versus the sample size.
 Jindow Joseph posted on Sunday, April 21, 2013 - 10:50 am
Thanks Ma'am...

Sorry to bother again, but one question on interpreting the result with Moderation:

D is the dependent variable ; V is the independent variable and M is the moderator

If I get the following result:

Est S.E. Est./S.E. P-Value(2 tailed)
D ON

V -0.304 0.098 -3.118 0.002
M 0.234 0.090 2.594 0.009
VxM -0.359 0.111 -3.228 0.001

There is a moderation effect as the Est./S.E value is greater than 1.96.But can I know the direction of moderation from this --- like if V remains constant , increase in M decreases D or (if V remains constant, increase in M increases D..) Also, is it problem that Est./S.E for M is significant?
 Linda K. Muthen posted on Sunday, April 21, 2013 - 3:37 pm
Your moderation effect is negative. If V remains constant, an increase in M decreases D. A significant main effect is not a problem.
 Jindow Joseph posted on Sunday, April 21, 2013 - 4:42 pm
Thanks a lot , Ma'am...
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