In my last blog post, I wrote about a mistake I once made when I didn’t realize the defaults for dummy coding were different in two SPSS procedures (Binary Logistic and GEE).
Ironically, about the same time I wrote it, I was having a conversation with Ann Maria de Mars on Twitter. She was trying to figure out why her logistic regression model fit results were identical in SAS Proc Logistic and SPSS Binary Logistic, but the coefficients in SAS were half those of SPSS.
It was ironic because I, of course, didn’t recognize it as the same issue and wasn’t much help.
But Ann Maria investigated and discovered that it came down to differences in the defaults for coding categorical predictors in SAS and SPSS that did it. Her detailed and humorous explanation is here.
Some takeaways for you, the researcher and data analyst:
1. Give yourself a break if you hit a snag. Even very experienced data analysts, statisticians who understand what they’re doing, get stumped sometimes. Don’t ever think that performing data analysis is an IQ test. You’re bringing together many skills and complex tools.
2. Learn thy software. In my last post, I phrased it “Know thy software”, but this is where you get to know it. Snags are good opportunities to investigate the details of your software, just like Ann Maria did. If you can think of it as a challenge to figure out–a puzzle–it can actually be fun.
Make friends with your syntax manuals.
3. Get help when you need it. Statistical software packages *are* complex tools. You don’t have to know everything to use them
Ask colleagues. Call customer support. Call a stat consultant. That’s what they’re there for.
4. A great way to check your work is to run your test two different ways. It’s another reason to be able to use at least two stat software packages. I’m not suggesting you have to run every analysis twice. But when a result looks strange, or you want to double-check a specific important model, this can be a good strategy for testing things out.
It may be that your results aren’t telling you what you think they are.
[Logistic_Regression_Workshop]
One of the tricky parts about dummy coded (0/1) variables is keeping track of what’s a 0 and what’s a 1.
This is made particularly tricky because sometimes your software switches them on you.
Here’s one example in a question I received recently. The context was a Linear Mixed Model, but this can happen in other procedures as well.
I dummy code my categorical variables “0” or “1” but for some reason in the (more…)
Here’s a little SPSS tip.
When you create new variables, whether it’s through the Recode, Compute, or some other command, you need to check that it worked the way you think it did.
(As an aside, I hope this goes without saying, but never, never, never, never use Recode into Same Variable. Always Recode into Different Variable so you don’t overwrite your data and then discover you made a mistake. Or worse, not discover. It happens).
And the easiest way to do that is to simply look at the data. (more…)
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…)
