I sometimes get asked questions that many people need the answer to. Here’s one about non-parametric ANOVA in SPSS.
Question:
Is there a non-parametric 3 way ANOVA out there and does SPSS have a way of doing a non-parametric anova sort of thing with one main independent variable and 2 highly influential cofactors?
Quick Answer:
No.
Detailed Answer:
There is a non-parametric one-way ANOVA: Kruskal-Wallis, and it’s available in SPSS under non-parametric tests. There is even a non-paramteric two-way ANOVA, but it doesn’t include interactions (and for the life of me, I can’t remember its name, but I remember learning it in grad school).
But there is no non-parametric factorial ANOVA, and it’s because of the nature of interactions and most non-parametrics.
What it basically comes down to is that most non-parametric tests are rank-based. In other words, (more…)
In Part 6, let’s look at basic plotting in R. Try entering the following three commands together (the semi-colon allows you to place several commands on the same line).
x <- seq(-4, 4, 0.2) ; y <- 2*x^2 + 4*x - 7
plot(x, y) (more…)
Every so often I point out to a client who exclusively uses menus in SPSS that they can (and should) hit the Paste button instead of OK. Many times, the client never realized it was there.
I am here today to tell you that it is there, and it is wonderful. For a few reasons.
When you use the menus in SPSS, you’re really taking a shortcut. You’re telling SPSS which syntax commands, along with which options, you want to run.
Clicking OK at the end of a dialog box will run the menu options you just picked. You may never see the underlying commands that SPSS just ran.
If instead you hit Paste, those command won’t automatically be run, but will instead the code to run those commands will be (more…)
One of the things I love about MIXED in SPSS is that the syntax is very similar to GLM. So anyone who is used to the GLM syntax has just a short jump to learn writing MIXED.
Which is a good thing, because many of the concepts are a big jump.
And because the MIXED dialogue menus are seriously unintuitive, I’ve concluded you’re much better off using syntax.
I was very happy a few years ago when, with version 19, SPSS finally introduced generalized linear mixed models so SPSS users could finally run logistic regression or count models on clustered data.
But then I tried it, and the menus are even less intuitive than in MIXED.
And the syntax isn’t much better. In this case, the syntax structure is quite different than for MIXED. (more…)
The Intraclass Correlation Coefficient, or ICC, can be very useful in many statistical situations, but especially so in Linear Mixed Models.
Linear Mixed Models are used when there is some sort of clustering in the data.
Two common examples of clustered data include:
- individuals sampled within sites (hospitals, companies, community centers, schools, etc.). The site is the cluster.
- repeated measures or longitudinal data where you collect multiple observations from the same individual. The individual is the cluster in which multiple observations are (more…)
I received the following email from a reader after sending out the last article: Opposite Results in Ordinal Logistic Regression—Solving a Statistical Mystery.
And I agreed I’d answer it here in case anyone else was confused.
Karen’s explanations always make the bulb light up in my brain, but not this time.
With either output,
The odds of 1 vs > 1 is exp[-2.635] = 0.07 ie unlikely to be 1, much more likely (14.3x) to be >1
The odds of £2 vs > 2 exp[-0.812] =0.44 ie somewhat unlikely to be £2, more likely (2.3x) to be >2
SAS – using the usual regression equation
If NAES increases by 1 these odds become (more…)
