If you’ve been doing data analysis for very long, you’ve certainly come across terms, concepts, and processes of matrix algebra. Not just matrices, but:
What are goodness of fit statistics? Is the definition the same for all types of statistical model? Do we run the same tests for all types of statistic model?
A Gentle Introduction to Random Slopes in Multilevel Modeling
…aka, how to look at cool interaction effects for nested data.
Do the words “random slopes model” or “random coefficients model” send shivers down your spine? These words don’t have to be so ominous. Journal editors are increasingly asking researchers to analyze their data using this particular approach, and for good reason.
Missing data causes a lot of problems in data analysis. Unfortunately, some of the “solutions” for missing data cause more problems than they solve.
Repeated measures is one of those terms in statistics that sounds like it could apply to many design situations. In fact, it describes only one.
A repeated measures design is one where each subject is measured repeatedly over time, space, or condition on the dependent variable.
These repeated measurements on the same subject are not independent of each other. They’re clustered. They are more correlated to each other than they are to responses from other subjects. Even if both subjects are in the same condition. (more…)
In this Stat’s Amore Training, Marc Diener will help you make sense of the strange terms and symbols that you find in studies that use multilevel modeling (MLM). You’ll learn about the basic ideas behind MLM, different MLM models, and a close look at one particular model, known as the random intercept model. A running example will be used to clarify the ideas and the meaning of the MLM results.