Most statistical software packages use a spreadsheet format for viewing the data. This helps you get a feeling for what you will be working with, especially if the data set is small.
But what if your data set contains numerous variables and hundreds or thousands of observations? There is no way you can get warm and fuzzy by browsing through a large data set.
To help you get a good feel for your data you will need to use your software’s command or syntax editor to write a series of code for reviewing your data. Sounds complicated.
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I love working with my clients.
I love working with my clients for many reasons, but one of them is I learn so much from them.
Just this week, one of my clients showed me how to get SPSS GENLINMIXED results without the Model Viewer.
She’s my new hero.
If you’ve ever used GENLINMIXED, the procedure for Generalized Linear Mixed Models, you know that the results automatically appear in this new Model Viewer. (more…)
How should I build my model?
I get this question a lot, and it’s difficult to answer at first glance–it depends too much on your particular situation.
There are really three parts to the approach to building a model: the strategy, the technique to implement that strategy, and the decision criteria used within the technique. (more…)
Like many people with graduate degrees, I have used a number of statistical software packages over the years.
Through work and school I have used Eviews, SAS, SPSS, R, and Stata.
Some were more difficult to use than others but if you used them often enough you would become proficient to take on the task at hand (though some packages required greater usage of George Carlin’s 7 dirty words).
There was always one caveat which determined which package I used. (more…)
One question I always get in my Repeated Measures Workshop is:
“Okay, now that I understand how to run a linear mixed model for my study, how do I write up the results?”
This is a great question.
There are many pieces of the linear mixed models output that are identical to those of any linear model–regression coefficients, F tests, means.
But there is also a lot that is new, like intraclass correlations and (more…)
Repeated measures ANOVA is the approach most of us learned in stats classes for repeated measures and longitudinal data. It works very well in certain designs.
But it’s limited in what it can do. Sometimes trying to fit a data set into a repeated measures ANOVA requires too much data gymnastics. (more…)
