Repeated measures ANOVA doesn’t cut it for many repeated measures situations, but do you always need mixed models instead?
Repeated Measures
Member Training: Marginal Models for Repeated Measures Data
September 2nd, 2025 by TAF SupportMember Training: Introduction to Binary Logistic Regression
December 3rd, 2024 by TAF Support
Binary logistic regression is one of the most useful regression models. It allows you to predict, classify, or understand explanatory relationships between a set of predictors and a binary outcome.
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Member Training: Types of Longitudinal, Repeated Measures, and Time Series
October 2nd, 2024 by TAF SupportHow do you know when to use a time series and when to use a linear mixed model for longitudinal data?
What’s the difference between repeated measures data and longitudinal?
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An Introduction to Repeated Measures Designs
May 23rd, 2024 by Kim LoveThere are many designs that could be considered Repeated Measures design, and they all have one key feature: you measure the outcome variable
for each subject on several occasions, treatments, or locations.
Understanding this design is important for avoiding analysis mistakes. For example, you can’t treat multiple observations on the same subject as independent observations.
Example
Suppose that you recruit 10 subjects (more…)
The Difference Between Crossed and Nested Factors
December 18th, 2023 by Karen Grace-MartinOne of those tricky, but necessary, concepts in statistics is the difference between crossed and nested factors.
As a reminder, a factor is any categorical independent variable. In experiments, or any randomized designs, these factors are often manipulated. Experimental manipulations (like Treatment vs. Control) are factors.
Observational categorical predictors, such as gender, time point, poverty status, etc., are also factors. Whether the factor is observational or manipulated won’t affect the analysis, but it will affect the conclusions you draw from the results.
The Wide and Long Data Format for Repeated Measures Data
December 2nd, 2023 by Karen Grace-MartinOne issue in data analysis that feels like it should be obvious, but often isn’t, is setting up your data.
The kinds of issues involved include:
- What is a variable?
- What is a unit of observation?
- Which data should go in each row of the data matrix?
Answering these practical questions is one of those skills that comes with experience, especially in complicated data sets.
Even so, it’s extremely important. If the data isn’t set up right, the software won’t be able to run any of your analyses.
And in many data situations, you will need to set up the data different ways for different parts of the analyses. (more…)