You have probably noticed I’m not much into R (though I’m slowly coming around to it). It goes back to when I was in my graduate statistics program, where we were required to use SPlus (R’s parent language—as far as I can tell, it’s the same thing, but with customer support).
We were given a half hour tutorial and an incomprehensible text, and sent off to figure it out how to use SPlus on graduate level stats.
Not fun.
And since I was already fluent in SAS, SPSS, and BMDP (may it rest in peace), I resisted SPlus. A lot.
I actually wish R had been around, (more…)
by Annette Gerritsen, Ph.D.
In an earlier article I discussed how to do a cross-tabulation in SPSS. But what if you do not have a data set with the values of the two variables of interest?
For example, if you do a critical appraisal of a published study and only have proportions and denominators.
In this article it will be demonstrated how SPSS can come up with a cross table and do a Chi-square test in both situations. And you will see that the results are exactly the same.
‘Normal’ dataset
If you want to test if there is an association between two nominal variables, you do a Chi-square test.
In SPSS you just indicate that one variable (the independent one) should come in the row, (more…)
SPSS offers two choices under the recode command: Into Same Variable and Into Different Variables.
The command Into Same Variable replaces existing data with new values, but the command Into Different Variables adds a new variable to the data set.
In almost every situation, you want to use Into Different Variables. Recoding Into Same Variables replaces the values in the existing variable.
So if you notice a mistake after you’ve recoded, you can’t fix it.
But you may not even notice the mistake, because you can’t even test it.
And that’s just dangerous. (more…)
There are three main ways you can approach analyzing repeated measures data, assuming the dependent variable is measured 
continuously: repeated measures ANOVA, Mixed Models, and Marginal Models. Let’s take a look at how the three approaches differ and some of their advantages and disadvantages.
For a few, very specific designs, you can get the exact same results from all three approaches. This makes it difficult to figure out what each one is doing, and how to apply them to OTHER designs.
For the sake of the current discussion, I will define repeated measures data as repeated measurements of the same outcome variable on the same individual. The individual is often a person, but could just as easily be a plant, animal, colony, company, etc. For simplicity, I’ll use “individual.” (more…)
Can I use SPSS MIXED models for (a) ordinal logistic regression, and (b) multi-nomial logistic regression?
Every once in a while I get emailed a question that I think others will find helpful. This is definitely one of them.
My answer:
No.
(And by the way, this is all true in SAS as well. I’ll include the SAS versions in parentheses). (more…)
This free, one-hour webinar is part of our regular Craft of Statistical Analysis series. In it, we will introduce and demonstrate two of the core concepts of mixed modeling—the random intercept and the random slope.
Most scientific fields now recognize the extraordinary usefulness of mixed models, but they’re a tough nut to crack for someone who didn’t receive training in their methodology.
But it turns out that mixed models are actually an extension of linear models. If you have a good foundation in linear models, the extension to mixed models is more of a step than a leap. (Okay, a large step, but still).
You’ll learn what random intercepts and slopes mean, what they do, and how to decide if one or both are needed. It’s the first step in understanding mixed modeling.
This webinar has already taken place. You can gain free access to a video recording of the webinar by completing the form below.
Here’s what participants said about the webinar:
“Thank you. I was also impressed with the way of explaining and the selection of example chosen to explain the theory.”
– Joanna Konieczna-Salamatin
“Teriffic job! I learned a lot. Thanks. Way to reduce a challenging topic to managable bite-size pieces. The graphical representations of the models helped me understand the random slope and random intercept terminology in a way I never got before.”
– Rob Baer
“I found it a great example and clear explanation, an hour is much better spent watching this than reading through a text book as an intro to this form of modeling.”
– Matt Cooper
“It was my first webinar and I was apprehensive with my lack of experience with the tecnology but it was really easy, user friendly, and definitely an experience to be repeated! Thank you!”
– Vanda Roque
” Just terrific. Clear, at the right level for me, extremely helpful.”
– Amy D’Andrade
“The seminar was well presented. The speaking was clear and easily undersood. The presentation was paced well. I found many of the questions and answers at the end to be *very* useful.”
– Andrew McLachlan
