DSEM-CFA and Factorial Invariance PreviousNext
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 Melissa Bond posted on Thursday, May 21, 2020 - 12:35 pm
I'm running a DSEM-CFA for my dissertation and trying to use Hamaker's syntax for examining differences in factor loadings across the between and within levels. However, I'm running into an issue with finding a consistently invariant item to use as the anchor item. I'm aware of a method for identifying invariant item sets by Cheung and Rensvold (1999), but given the more complex nature of DSEM, wanted to make sure that there are not better methods for identifying the best anchor item. Please advise!
 Tihomir Asparouhov posted on Friday, May 22, 2020 - 9:21 am
You don't need an anchor item. The hypothesis about equality of loadings is equivalent to the hypothesis of proportional loadings when you fix the factor variances to 1.

%within%
f by y1-y4 (w1-w4); f@1;
%between%
fb by y1-y4 (b1-b4); fb@1;

model test:
0=w1*b2-b1*w2;
0=w1*b3-b1*w3;
0=w1*b4-b1*w4;
 Tihomir Asparouhov posted on Tuesday, May 26, 2020 - 1:27 pm
Just a little correction on the above setup. The model should be specified as

%within%
f by y1-y4*1 (w1-w4); f@1;
%between%
fb by y1-y4*1 (b1-b4); fb@1;

to free up all the loadings.
 Ahnalee Brincks posted on Thursday, May 28, 2020 - 1:42 pm
I'm trying to run a DSEM-CFA model with three continuous indicators:

Usevariables are TSSID Y1 Y2 Y3 ;

CLUSTER = TSSID;

Define:
Y1 = Intensity1;
Y2 = Intensity2;
Y3 = Intensity3;
Y4 = Intensity4;

Analysis:
Type = twolevel random;
estimator = bayes;
processors = 2;
biteration = (2000);

Model:
%within%
f by y1-y3(&1);
s| f on f&1;
logv | f;


%between%
fb by y1-y3*;
fb@1;
fb s logv WITH fb s logv;

The error message I'm getting is this:
*** FATAL ERROR
LAG CAN NOT BE GREATER THAN THE NUMBER OF OBSERVATIONS FOR EACH CLUSTER.


Can you help me understand this error?
 Tihomir Asparouhov posted on Thursday, May 28, 2020 - 3:41 pm
To run a time-series model / DSEM model you must have more than one observation in each cluster, and in fact we would recommend at least 6 observations per person as you have 5 subject specific parameters.

As a first analysis I would recommend that you run a simpler model

%within%
f by y1-y3(&1);
f on f&1;
f;

and exclude all clusters with 5 observations or less. You should also run the standard two level as a very first step


Model:
%within%
f by y1-y3*1;
f@1;

%between%
fb by y1-y3*1;
fb@1;
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