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 Justin Feeney posted on Wednesday, November 22, 2017 - 9:26 am
We collected mental health data on 4 continents and want to examine the invariance of 5 separate measures with a sample size of 1000. One obstacle that I've run into is that running the analysis with all 5 measures at once leads to more parameters than people, and I get a warning that the model is not positive definite. I've tried running it with fixed and it did not resolve the problem.


Would it make sense to run separate alignment analyses for each of the 5 measures? When I try this, the problem seems to be resolved.


Also, is there a method to use the values from alignment analysis for future analyses? We want to see how each measure below (P, D, R, S, and A) relates to a few other outcome variables.


Below is the syntax:

data:
file = CFA.dat;

variable:
names are cont P1-P8 D1-D24 R1-R5 S1-S4 A1-A30;
usevar = P1-P8 D1-D24 R1-R5 S1-S4 A1-A30;
MISSING ARE ALL (999);
classes = c(4);
knownclass = c(cont = 1 2 3 4);

ANALYSIS:
type = mixture;
estimator = ml;
alignment = free;

model:
%overall%
P by P1-P8;
D by D1-D24;
R by R1-R5;
S by S1-S4;
A by A1-A30;

output:
tech1 tech8 align SVALUES;

plot:
type = plot2;
 Bengt O. Muthen posted on Thursday, November 23, 2017 - 9:50 am
Yes, run it separately for each of the 5 factors.

Regarding relating the factors to other variables, see the extended alignment method described in

http://psycnet.apa.org/record/2017-01642-001
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