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Thread: Multilevel analysis -SAS

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    Re: Multilevel analysis -SAS




    One thing that I find confusing about multilevel approaches is that at times it seems to suggest that some variables influence the DV indirectly through their impact on the group which then influences the first level DV. This is particularly obvious in say Raudenbush and Bryk who my training in HLM was based on. They have separate equations for 2nd and above level units (like school or hospital). Its true that they have an overall equation that combines these higher level equations eventually into an overall model, but at least in my classes that did not get a lot of emphasis
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Re: Multilevel analysis -SAS

    “Significance testing for variances in level 2 residual variance/covariance matrix G provides information about which level 1 slope coefficients are random. If the variance is not statistically significant you remove the random coefficient, …”

    This is testing if you should have a random component for a level 1 variable. How do you do this in sas, that is where do you find the results (and is there a specific test you need to specify).
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Re: Multilevel analysis -SAS

    There are two or three COV statement tests, did you buy the book I recommended.

    You can also use AICC comparisons along with -2loglikelihood tests during model building.
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    Re: Multilevel analysis -SAS

    I am not sure which book you mean. I order them from the state library. Not being a fabulously wealthy medical researcher I don't buy anything.

    I am reading Wang et el Multilevel models (which might be the book you recommended). He makes a fascinating point. SAS does distinguish between level 1 and 2 variables even though its not in the code (as it would be for example with the HLM software). SAS automatically classifies something as a level 2 variable (a predictor at level 2) if a variable varies across groups but remains constant within a group. That is one reason you have to specify a group and individual identifier.
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Re: Multilevel analysis -SAS

    Yeah I think it was the Wang book.
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    Re: Multilevel analysis -SAS

    Is there a SAS PROC called MMIX? I am not sure if this is a spelling mistake or what.

    By using the PLOTS option in the SAS PROC MMIXED procedure, several diagnostic plots will be produced. It is
    important to look at these diagnostic plots to ensure that the model is a good fit for the data.
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Re: Multilevel analysis -SAS

    They probably just meant "PROC MIXED", they have a continuous DV right?
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    Re: Multilevel analysis -SAS

    An assumption that is us usually made in ML regression is that the variance of the residual errors is the same in all groups. This can be assessed by computing a one way analysis of variance of the groups on the absolute values of the residuals which is the equivalent of the Levene test
    Hlsmith do you have any idea how that is done in SAS. None of the documentation I have seen even mentions it.
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Re: Multilevel analysis -SAS

    is there anyway in PROC MIXED to get the regression equation for each group (that is show the regression for group 1 than group 2 etc)
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Re: Multilevel analysis -SAS

    I am running my first real model using multilevel analysis and I had a question. As a first step I ran the following code to generate the ICC (unitid_pri is our groups var, DV is our interval level Dependent variable).

    Is this the correct way to generate the empty model to calculate that?

    proc mixed data= work.test2 covtest noclprint;
    class unitid_pri;
    model DV = /solution;
    random intercept /subject= unitid_pri;
    run;

    I calculated the ICC by Estimate /(estimate + residuals) as I found on line. Is this correct?

    My ICC is about 3.8 percent. Is that enough to see multilevel models as useful, or is it suggesting that group really has a limited effect?
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    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Re: Multilevel analysis -SAS

    noetsi,


    I believe further down in the SAS output there should be:


    "Null Model Likelihood Ratio Test"


    The related chisq test is for Ho: groups do not predict. So if that test is significant it provides support for using MLM.
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    Re: Multilevel analysis -SAS

    I understand that, but looking at the literature the point is made that substantively, as compared to statistical test, there is a point at which the ICC is so low its not worth considering the impact of groups. It is really that I was asking about. How low does the threshold go, that is how high does ICC have to be, to consider groups important and thus need multilevel models.
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Re: Multilevel analysis -SAS

    As a simplistic hypothetical, think about ICC as parital R^2 contributions. It is contextual, some may love 3% others may be used to double digits. Does accounting for 4% seem worthy given your context. What gets biased by ignoring group level besides precision?
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    Re: Multilevel analysis -SAS

    Which is pretty much what I thought. I am curious, I do not read the academic literature on this enough, what ICC's are commonly reported in multilevel studies.

    To me a 4 percent contribution seems minor.
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Re: Multilevel analysis -SAS


    In medicine I cant say they typically report the value. It depends on what you are clustering on, intra cluster variability, and probably the number of values in cluster. Jakes link in chatbox, the box plot, visualizes this.

    PS, I typically have pretty small value under ten I would say.
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