There are two oft-cited assumptions for Analysis of Covariance (ANCOVA), which is used to assess the effect of a categorical independent variable on a numerical dependent variable while controlling for a numerical covariate:
1. The independent variable and the covariate are independent of each other.
2. There is no interaction between independent variable and the covariate.
In a previous post, I showed a detailed example for an observational study where the first assumption is irrelevant, but I have gotten a number of questions about the second.
So what does it mean, and what should you do, if you find an interaction between the categorical IV and the continuous covariate? (more…)
There is something about interactions that is incredibly confusing.
An interaction between two predictor variables means that one predictor variable affects a third variable differently at different values of the other predictor.
How you understand that interaction depends on many things, including:
- Whether one, or both, of the predictor variables is categorical or numerical
- How each of those variables is coded (specifically, whether each categorical variable is dummy or effect coded and whether numerical variables are centered)
- Whether it’s a two-way or three-way interaction
- Whether there is a directionality to the interaction (moderation) or not
Sometimes you need to get pretty sophisticated in your coding, in the output you ask for, and in writing out regression equations.
In this webinar, we’ll examine how to put together and break apart output to understand what your interaction is telling you.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
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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…)
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…)
A number of years ago when I was still working in the consulting office at Cornell, someone came in asking for help interpreting their ordinal logistic regression results.
The client was surprised because all the coefficients were backwards from what they expected, and they wanted to make sure they were interpreting them correctly.
It looked like the researcher had done everything correctly, but the results were definitely bizarre. They were using SPSS and the manual wasn’t clarifying anything for me, so I did the logical thing: I ran it in another software program. I wanted to make sure the problem was with interpretation, and not in some strange default or (more…)
One great thing about logistic regression, at least for those of us who are trying to learn how to use it, is that the predictor variables work exactly the same way as they do in linear regression.
Dummy coding, interactions, quadratic terms–they all work the same way.
Dummy Coding
In pretty much every regression procedure in every stat software, the default way to code categorical variables is with dummy coding.
All dummy coding means is recoding the original categorical variable into a set of binary variables that have values of one and zero. You may find it helpful to (more…)
