When to use repeated measures ANOVA

Are you wondering when you should use a repeated measures ANOVA? Or maybe you are wondering how to decide between a mixed model and a repeated measures ANOVA? Well either way, you are in the right place! In this article we tell you everything you need to know to determine whether a repeated measures ANOVA is right for you.

We start out with a discussion of what types of outcome variables can be handled using a repeated measures ANOVA model. After that, we discuss some of the main advantages and disadvantages of repeated measures ANOVA models. Finally, we provide specific examples of situations where you should and should not use repeated measures ANOVA.

Outcome variables for repeated measures ANOVA

Are you wondering what types of outcome variables you can use repeated measures ANOVA models for? In general, you should use a repeated measures ANOVA model when you have a simple continuous outcome variable.

If you have an outcome variable that does not take on a simple continuous value, such as a binary outcome or a count outcome, you should look into using mixed models instead. Generalized linear mixed models can be used to handle a variety of different types of outcome variables.

Advantages and disadvantages of repeated measures ANOVA

What are the main advantages and disadvantages of a repeated measures ANOVA? Here are some of the main advantages and disadvantages you should keep in mind when deciding whether to use a repeated measures ANOVA.

Advantages of repeated measures ANOVA

• Accounts for repeated measurements on the same subject. The main advantage that a repeated measures ANOVA model has over similar methodologies like a standard ANOVA model is that the repeated measures ANOVA can be used in situations where you have multiple measurements taken on the same subject. These types of situations cannot be handled using a standard ANOVA model because the standard ANOVA model assumes that all observations in the data are independent of one another. If you have multiple measurements that were taken on the same subject, your data certainly do not meet this assumption.
• Interpretable coefficients. Another advantage of the repeated measures ANOVA model is that it has interpretable coefficients that come along with error measurements and statistical tests. That means that it is useful in cases where inference is one of your primary goals.
• Simple model. Another advantage of the repeated measures ANOVA model is that it is a relatively simple model with fewer parameters that need to be estimated than similar models like mixed models. That means that you do not need as much data to estimate your parameters and you are less likely to run into situations where you cannot estimate the parameters.

Disadvantages of repeated measures ANOVA

• Does not account for hierarchical structure in data. Repeated measures ANOVA models are specifically designed to handle situations where data points are not independent because multiple measurements are taken on the same subject. This is great if you find yourself in this specific situation, but there are other types of situations that might lead to observations not being independent of one another and repeated measures ANOVA models cannot handle these situations. For example, repeated measures ANOVA models cannot handle situations where there is a natural nested or hierarchical structure in the data such observations that are in similar areas of the hierarchy have similarities.
• Expects the same number of measurements for each subject. Another disadvantage of repeated measures ANOVA models is that they assume that you have the same exact number of measurements for each subject in your study. This means that you will have to pre-process your data to handle situations where a measurement is missing for a given subject.
• Expects measurements to be taken at the same time. In addition to assuming that the same number of measurements are taken for each subject, the repeated measures ANOVA model also assumes that all measurements on all subjects were taken in the same time intervals. This means that you have to carefully design your experiments to ensure that all measurements are taken at the correct time.
• Cannot account for multiple features or numeric features. Another disadvantage of repeated measures ANOVA models is that it can only be used when you want to examine the relationship between your outcome variable and a single categorical feature. If you have multiple features you want to consider, or if you have a numeric feature you want to consider, you will need to use a different model.

When to use repeated measures ANOVA

When should you use a repeated measures ANOVA model? Here are some examples of situations where you should use a repeated measures ANOVA model.

• Small datasets. Since the repeated measures ANOVA model is more simple and has fewer parameters that need to be estimated than a mixed model, you are better off using it in situations where you have a small dataset. You should keep this in mind when designing experiments to collect data for your analysis.
• Regular measurements taken at fixed time intervals. If your data has clean measurements that were taken at regular intervals and is generally suitable for a repeated measures ANOVA model, you are generally better off using a repeated measures ANOVA than a mixed model. All else considered equal, it is generally better to stick with the simplest model that suits your purposes.

When not to use repeated measures ANOVA

When should you avoid using a repeated measures ANOVA? Here are some examples of situations where you should not use a repeated measures ANOVA.

• Outcome variable is not continuous. You should avoid using a repeated measures ANOVA in cases where you do not have a simple continuous outcome variable. In these cases, your best bet is generally to use a generalized linear mixed model rather than an ANOVA model. This family of models is relatively flexible and can be adapted to handle a range of different types of outcome variables.
• Multiple features to consider. If you have many features that you want to consider in your analysis, you are also better off using a model like a mixed model that can account for multiple features of differing types.
• Measurements taken at irregular intervals. You should also avoid using a repeated measures ANOVA if the repeated measurements in your dataset were taken at irregular time intervals. If there are different numbers of measurements per subject, or if the measurements were taken at different points in time, then you are likely better off using a mixed model.

Related articles

• When to use mixed models
• When to use multinomial regression
• When to use ordinal logistic regression
• When to use linear regression
• When to use logistic regression
• When to use poisson regression
• When to use Bayesian regression
• When to use ridge regression
• When to use LASSO

Are you trying to figure out which machine learning model is best for your next data science project? Check out our comprehensive guide on how to choose the right machine learning model.