Linear, Logistic, Tobit, Cox, Poisson, Zero Inflated… The list of regression models goes on and on before you even get to things like ANCOVA or Linear Mixed Models.
In this webinar, we will explore types of regression models, how they differ, how they’re the same, and most importantly, when to use each one.
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 area in statistics where I see conflicting advice is how to analyze pre-post data. I’ve seen this myself in consulting. A few years 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. This advice led her to fear that she had grossly misunderstood a very basic tenet in her statistical training.
The Study Design
Nancy had measured a response variable at two time points for two groups. The intervention group received a treatment and a control group did not. Participants were randomly assigned to one of the two groups.
The researcher measured each participant before and after the intervention.
Analyzing the Pre-Post Data
Nancy was sure that this was a classic repeated measures experiment. It has (more…)
Every once in a while, I work with a client who is stuck between a particular statistical rock and hard place. It happens when they’re trying to run an analysis of covariance (ANCOVA) model because they have a categorical independent variable and a continuous covariate.
The problem arises when a coauthor, committee member, or reviewer insists that ANCOVA is inappropriate in this situation because one of the following ANCOVA assumptions are not met:
1. The independent variable and the covariate are independent of each other.
2. There is no interaction between independent variable and the covariate.
If you look them up in any design of experiments textbook, which is usually where you’ll find information about ANOVA and ANCOVA, you will indeed find these assumptions. So the critic has nice references.
However, this is a case where it’s important to stop and think about whether the assumptions apply to your situation, and how dealing with the assumption will affect the analysis and the conclusions you can draw. (more…)
Like some of the other terms in our list–level and beta–GLM has two different meanings.
It’s a little different than the others, though, because it’s an abbreviation for two different terms:
General Linear Model and Generalized Linear Model.
It’s extra confusing because their names are so similar on top of having the same abbreviation.
And, oh yeah, Generalized Linear Models are an extension of General Linear Models.
And neither should be confused with Generalized Linear Mixed Models, abbreviated GLMM.
Naturally. (more…)
I am reviewing your notes from your workshop on assumptions. You have made it very clear how to analyze normality for regressions, but I could not find how to determine normality for ANOVAs. Do I check for normality for each independent variable separately? Where do I get the residuals? What plots do I run? Thank you!
I received this great question this morning from a past participant in my Assumptions of Linear Models workshop.
It’s one of those quick questions without a quick answer. Or rather, without a quick and useful answer. The quick answer is:
Do it exactly the same way. All of it.
The longer, useful answer is this: (more…)
Factor is confusing much in the same way as hierarchical and beta, because it too has different meanings in different contexts. Factor might be a little worse, though, because its meanings are related.
In both meanings, a factor is a variable. But a factor has a completely different meaning and implications for use in two different contexts. (more…)
