This kind of situation happens all the time, in which a colleague, a reviewer, or a statistical consultant insists that you need to do the analysis differently. Sometimes they're right, but sometimes, as was true here, the two analyses answer different research questions.
My goal all along has been to offer all my stat workshops with support for multiple software packages. I’ve gotten further along with some of them than others, but I’m working on it. So I was pretty happy during the last Repeated Measures workshop when one of the participants, Dan Neal, mentioned he was working out all the exercises in R.
The following is a TED talk by Arthur Benjamin, who is a math professor at Harvey Mudd College. Let me start by saying he is awesome. I already watched his Mathemagician TED talk with my kids*, so when I found this, I already expected it to be very good. I wasn't disappointed.
There are many designs with multiple observations in a cluster. Repeated measures data have multiple observations from the same subject. Randomized block studies have multiple plant measurements nested within a farm. An evaluation may have social workers clustered within an agency. Because of the clustering, there are a few issues that come up when conducting sample size calculations for multilevel models that don't usually come up when running calculations for…
Even experienced data analysts can get off track, especially with large data sets with many variables. It's just so easy to try different versions of models or get distracted by interesting, but irrelevant, relationships among variables. The lesson? Make a plan.
Remember all those Z-scores you had to calculate in Intro Stats? Converting a variable to a Z-score is standardizing. In other words, do these steps for Y, your outcome variable, and every X, your predictors: 1. Calculate the mean and standard deviation.
This is especially true when your audience is a clinical one who needs to make decisions based on your results. So you're also absolutely correct that presenting a table full of odds ratios is not the way to go here. To answer your first question, no. You cannot say for every one female who fails, X number of males will fail. You can, however, convey the odds ratios in a…
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 variables 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…
When the response variable for a regression model is categorical, linear models don’t work. Logistic regression is one type of model that does, and it’s relatively straightforward for binary responses. When the response variable is not just categorical, but ordered categories, the model needs to be able to handle the multiple categories, and ideally, account for the ordering.
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.
