Posted by
Poes, Matthew Joseph on
Jul 05, 2012; 1:58pm
URL: http://spssx-discussion.165.s1.nabble.com/Generalized-Linear-Model-Moderation-Analysis-tp5714032p5714034.html
See my comments below:
Matthew J Poes
Research Data Specialist
Center for Prevention Research and Development
University of Illinois
510 Devonshire Dr.
Champaign, IL 61820
Phone: 217-265-4576
email:
[hidden email]
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
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Sent: Thursday, July 05, 2012 2:35 AM
To:
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Subject: Generalized Linear Model - Moderation Analysis
Hi all,
I'm really hoping that someone can help me with this as I'm reaching the end of my tether! I am conducting a moderation analysis on cross-sectional data. I have been advised to run a series of GZLM in order to achieve this.
This is as follows:
Model 1: All covariates
Model 2: Model 1 plus main effects *Main effects of what, are you talking about adding in the factors now? Keep in mind that the factors are simply the categorical variables in the model, which end up being dummy coded by the program when it runs. Covariates can be true covariates, or simply linear predictors. Main effects simply refers to the effect of either a covariate or a categorical factor variable, when all else in the model is equal to zero. However, you need to be certain that, in this scenario, zero reflects a relevant base case. In other words, to get meaningful main effects, you need to center all of your covariates, and utilize appropriate coding of your categorical factor variables.
Model 3: Model 2 plus resilience *What type of variable is resilience? If this is a linear variable, it will go into the covariate section, and to discuss its ability to explain additional variance in a meaningful way, you will want to show that the fit statistics went down when this was added, not just that it is a significant predictor in its own right.
Model 4: Model 3 plus interaction between risk factors and resilience.
Now to begin with - I can run the GZLM up to model 4 (I think, well I have done but have yet to interpret the results. Any tips??). The issues I am having is with creating the interaction. I know how to create an interaction between, say, tea*biscuits. However, I have several risk factors, so it is about creating an interaction term between - tea/coffee/water/soft drinks/green tea/black tea*biscuits. Does anyone know how to do this? I've searched but can not find anything to help direct me in regards to this.
*As for the interactions, they work the same as in any other model. Just like in other models, adding lots of levels to your interactions becomes very complicated and difficult to interpret. First, anything more than a 3-way interaction is going to be very complicated to interpret. I can't imagine it giving you useful information either. My recommendation is to first consider what research questions you are trying to answer, what theory underlies those questions, and then consider how the data can answer those questions. In terms of just creating an interaction with the term "biscuits", you simply create tea*biscuits, coffee*biscuits, water*biscuits, soft drinks*biscuits, green tea*biscuits, and black tea*biscuits. Include all of those in the model. Remember again though, the interpretation of the effect coefficient is for when everything else in the model is zero. In the example above, if you have tea, biscuits, and tea*biscuits in the model, then your interpretatio!
n is the effect for tea but no biscuits, the effect for biscuits but no tea, and the effect for tea when someone also has a biscuit. Here is where things get confusing, when all of those variables are in the model, it now becomes the effect for tea, but no biscuits, coffee, water, soft drinks, etc. Remember that the intercept is going to be the value for your referent group, and thus all the effects will be interpreted as the difference from the referent group, not their actual effect. With linear interactions you have two effects to consider, differences in intercept, and differences in slope.
Further, my data consists of factors and covariates, so I don't know whether this complicates things further. I have saved the XBPredicted and MeanPredicted within the model as well, I'm not sure if that will help.
*Not really, as long as you understand fundamentally how to interpret the model. Your saved values wont' really be useful for this.
I have conducted MI prior to running the analysis on the imputed data (though my supervisor thinks I need to re do the missing analysis now with EM and then run the GZLM - advice?).
*I'm not sure what you mean here by EM? As for the MI, just be certain that the MI was appropriate to begin with. I see a lot of people use it to handle missing data, even though the nature of the missing data is such as to suggest the MI increased bias of the estimates over list wise deletion. You don't want to do that. Also, if you have predictable missing data following a specific pattern (NMAR) I would suggest adding dummy variables for the cases which are missing that NMAR variable. You would do this for each of the variables which reflect NMAR properties, and include all of these dummy variables as covariates.
Any suggestions will be gratefully received.
Desperately yours...
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