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Hi, without knowing your model and your data, it is hard to evaluate the situation, but my experiences with artificially generated data suggest that you could be right with the assumption that there is no heterogeneity. Normally I got those messages when I had tried FIMIX on a single group without h...

She had overall 10 exogenous variables, which were either formative (7 of them) or reflective (3 of them).

java.version=1.4.2_15

You need a newer Java version. At least 5.0 or higher.

Hi,
there must be something strange with your model.
I just tried an example with about 3000 cases (9 LVs with 5 to 7 indicators); 2000 samples.
All samples went trough in less than an hour!

(btw. Java Memory assigned: 512MB)

You may use the search function in this Forum for the term "memory". There are a lot of topics concerning this fact.

My first suggestion is that you should try to increase the memory to at least 256MB or maybe even more.

I get SD (with N): 0,792148976 and SD (with N-1): 0,812727701 for your example.
If you use the first one you get exactly the same values as in the report.

First of all YES, the manifest variable scores (used) in the report are the standardized scores. As you stated yourself, the calculation of the standard deviation is probably not the same. You should be aware that you can calculate "the sample (or estimated) standard deviation" or "th...

You have an iterative two-step estimation process of the LV scores (outer/inner approximation) till convergence of the weights. Basically the process looks like this: Step 0: Initial outside approximation Step 1: Inner weights approximation Step 2: Inside approximation Step 3: Outer weights approxim...

Hi, Of course you probably get different results for different structural models. The scores of the LV’s are finally calculated as weighted mean of the indicators, the way Prof. Bido described. However the calculation of the weights depends also on the structural model and therefore the final calcul...