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Thread: (SEM) Structural Modeling: identifying problems

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    (SEM) Structural Modeling: identifying problems




    Hello, I've received a homework at my UNI to resolve untill my exam (which is friday). It's about SEM. It is in form of a forum post and we need to find answers for this problem. Our prof also suggested seeking help on statistics forums. Can anyone help?

    "Hi All

    I've analysed some data (large sample, N~7000), doing crossed lagged regressions with categorical data, and using complex samples to correct for clustering.

    I originally analyzed it using WLSMV estimation, and I've switched to ML - I find that things are more likely to be statistically significant. One parameter goes from very slightly negative, and non-significant, to positive and highly significant.

    I don't recall seeing anything published that compares the two approaches - is there anything I've missed, or does anyone have any light to shed on this?"

    I'll be grateful for some answers.

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    Re: (SEM) Structural Modeling: identifying problems

    Please describe you variables (at least their format). Description leads reader to assume all variabbles are categorical (except maybe time).
    Stop cowardice, ban guns!

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    Re: (SEM) Structural Modeling: identifying problems


    we don't have variables specified. The answer has to be general and theoretical based only on the informations I mentioned above.

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