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.
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?
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.
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.
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).
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?
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.
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...?
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.
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?