The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
Learning statistics is difficult enough; throw in some especially confusing terminology and it can feel impossible! There are many ways that statistical language can be confusing.
Some terms mean one thing in the English language, but have another (usually more specific) meaning in statistics. [Read more…] about Member Training: Confusing Statistical Terms
Not too long ago, a client asked for help with using Spotlight Analysis to interpret an interaction in a regression model.
Spotlight Analysis? I had never heard of it.
As it turns out, it’s a (snazzy) new name for an old way of interpreting an interaction between a continuous and a categorical grouping variable in a regression model. [Read more…] about Spotlight Analysis for Interpreting Interactions
Not too long ago, I received a call from a distressed client. Let’s call her Nancy.
Nancy had asked for advice about how to run a repeated measures analysis. The advisor told Nancy that actually, a repeated measures analysis was inappropriate for her data.
Nancy was sure repeated measures was appropriate and the response led her to fear that she had grossly misunderstood a very basic tenet in her statistical training.
The Design
Nancy had measured a response variable at two time points for two groups: an intervention group, who received a treatment, and a control group, who did not.
Both groups were measured before and after the intervention.
The Analysis
Nancy was sure that this was a classic repeated measures experiment with [Read more…] about Analyzing Pre-Post Data with Repeated Measures or ANCOVA
One of the biggest challenges in learning statistics and data analysis is learning the lingo. It doesn’t help that half of the notation is in Greek (literally).
The terminology in statistics is particularly difficult to learn because often the same word or symbol is used to mean completely different concepts.
I know it feels that way, but it really isn’t a master plot by statisticians to keep researchers feeling ignorant.
Really.
It’s just that a lot of the methods in statistics were created by statisticians working in different fields–economics, psychology, and yes, straight statistics. Certain fields often have specific types of data that come up a lot and that require specific statistical methodologies to analyze.
Economics needs time series, psychology needs factor analysis. Et cetera, et cetera.
But separate fields developing statistics in isolation has some ugly effects.
Sometimes different fields develop the same technique, but use different names or notation.
Other times different fields use the same name or notation on different techniques they developed.
And of course, there are those terms with slightly different names, often used in similar contexts, but with different meanings. These are never used interchangeably, but they’re easy to confuse if you don’t use this stuff every day.
And sometimes, there are different terms for subtly different concepts, but people use them interchangeably. (I am guilty of this myself). It’s not a big deal if you understand those subtle differences. But if you don’t, it’s a mess.
And it’s not just fields–it’s software, too.
SPSS uses different names for the exact same thing in different procedures. In GLM, a continuous independent variable is called a Covariate. In Regression, it’s called an Independent Variable.
Likewise, SAS has a Repeated statement in its GLM, Genmod, and Mixed procedures. They all get at the same concept there (repeated measures), but they deal with it in drastically different ways.
So once the fields come together and realize they’re all doing the same thing, people in different fields or using different software procedures, are already used to using their terminology. So we’re stuck with different versions of the same word or method.
So anyway, I am beginning a series of blog posts to help clear this up. Hopefully it will be a good reference you can come back to when you get stuck.
We’ve expanded on this list with a member training, if you’re interested.
If you have good examples, please post them in the comments. I’ll do my best to clear things up.
I recently was asked whether to report means from descriptive statistics or from the Estimated Marginal Means with SPSS GLM.
The Estimated Marginal Means in SPSS GLM tell you the mean response for each factor, adjusted for any other variables in the model. They are found in the Options button. (These are the same as the LSMeans in SAS GLM).
If all factors (aka categorical predictors) were manipulated, these factors should be independent.
Or at least they will be if you randomly assigned subjects to conditions well.
In this situation only, the estimated marginal means will be the same as the [Read more…] about Why report estimated marginal means in SPSS GLM?
Part 1 outlined one issue in deciding whether to put a categorical predictor variable into Fixed Factors or Covariates in SPSS GLM. That issue dealt with how SPSS automatically creates dummy variables from any variable in Fixed Factors.
There is another key default to keep in mind. SPSS GLM will automatically create interactions between any and all variables you specify as Fixed Factors.
If you put 5 variables in Fixed Factors, you’ll get a lot of interactions. SPSS will automatically create all 2-way, 3-way, 4-way, and even a 5-way interaction among those 5 variables. [Read more…] about Dummy Coding in SPSS GLM–More on Fixed Factors, Covariates, and Reference Groups, Part 2