http://spssx-discussion.165.s1.nabble.com/Re-posting-conditional-logistic-regression-for-agreement-study-tp3373568p3374559.html
> Jan,
>
> I'm sure there are others more knowledgable but here is my thinking. I think
> that what you'd like to be true is that any third variable has equal
> relationships with both DVs. Basically, a common cause model. You have three
> confounders: MD_Type, MDE, HxSA. MDE and HxSA might be used as predictors of
> current suicidality (ignore MD_type for the moment). Ideally, each and both
> would have equal relationships with both DVs. You can look at bivariate
> relationships easily enough. It'd be nice to compare the two resulting
> statistics much as you might compare two correlations. I don't know how to
> do that. By the way, note that these are dependent associations because they
> share a common variable. The best alternative, not available in spss, is to
> fit a simple three variable path model and constrain the path coefficients
> to equality. Mplus is the software to use although Lisrel or EQS could do
> this too. However, because the DVs are categorical, I think the process may
> be more complicated. I have little experience in this.
>
> MD_Type is a different problem. I assume that either a staff or a resident
> did the MD rating. Thus, MD_Type is a moderator variable. So the question is
> whether staff rate the same as residents. Again, bivariate techniques will
> give you insight but not a unified, i.e., single statistic, answer. Perhaps
> there are ways to compare two independent chi-squares or other categorical
> stats. I don't know them. The basic analysis here is just a multiple group
> problem. Again, an Mplus problem but now much more complicated because of
> the ordinal DV.
>
> I'd say the moderator question comes before the common cause question
> because if you can knock out MD_Type, you can legitimately combine your two
> MD_Type groups. Even though you could work this a two step problem, you
> could combine the everything into single path model. So, two groups defined
> by MD_type. In each group both DVs are predicted by MDE and HxSA. What you
> want is that the totally constrained model fits as well as the unconstrained
> model.
>
> Gene Maguin
>
>
>
>
> Hi,
> I'm re posting this since I didn't get any replies from my Jan 26th post. I
> hope this is the correct etiquette.
>
> Hi,
> I have 2 variables (veteran rating of suicidality and psychiatrist rating of
> veteran suicidality) with the same 5 level ordinal rating (n=482).
> I know that the survey results and the psychiatrist's rating are not
> independent since they are rating the same entity-the study participant.
> I am using a combination of methods to examine agreement including:
> looking at the crosstabs for patterns, calculating proportion of agreement,
> kappa, ICC, and McNemar.
> I understand that modeling (specifically conditional logistic regression)
> can be used and thus potential confounders can be examined.
> Can this be done in SPSS? If so, I'm confused as to what would be the DV and
> IV. I have 3 potential confounders.
>
> Here is a sample of my data
> Data List List/ ID Vet_Rate Psych_Rate MD_Type MDE HxSA
> 1 4 1 1 1 0
> 2 3 1 2 0 1
> 3 0 0 1 0 0
> 4 3 2 1 1 1
> 5 4 1 1 1 0
> etc.
> ID
> Vet_Rate= veteran rating of themselves (0-4)
> Psych_Rate= psychiatrist rating of veteran(0-4)
> MD_Type= type of psychiatrist (1=resident 2=staff)
> MDE=presence of depression 0=no 1=yes
> HxSA=history of suicide attempt 0=no 1=yes
>
> Thanks for any help,
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