An ANOVA has a balanced design if the sample sizes are equal across all treatment combinations. Conversely, an ANOVA has an unbalanced design if the sample sizes are not equal across all treatment combinations.

Can I use ANOVA for unbalanced data?

The short answer: Yes, you can perform a one-way ANOVA when the sample sizes are not equal. Equal sample sizes is not one of the assumptions made in an ANOVA.

### What is unbalanced two-way ANOVA?

The term “unbalanced” means that the sample sizes nkj are not all equal. A balanced design is one in which all nkj = n. In the unbalanced case, there are 2 ways to define sums of squares for factors A and B.

What is a disadvantage of using a repeated measures ANOVA?

Repeated measures designs have some disadvantages compared to designs that have independent groups. The biggest drawbacks are known as order effects, and they are caused by exposing the subjects to multiple treatments. Order effects are related to the order that treatments are given but not due to the treatment itself.

## How do you know if data is balanced or unbalanced?

In ANOVA and Design of Experiments, a balanced design has an equal number of observations for all possible level combinations. This is compared to an unbalanced design, which has an unequal number of observations. Levels (sometimes called groups) are different groups of observations for the same independent variable.

How do you analyze unbalanced data?

7 Techniques to Handle Imbalanced Data

1. Use the right evaluation metrics.
2. Resample the training set.
3. Use K-fold Cross-Validation in the right way.
4. Ensemble different resampled datasets.
5. Resample with different ratios.
6. Cluster the abundant class.

### Can you do two way ANOVA with unequal sample sizes?

According to Keppel (1993), there is no good rule of thumb for how unequal the sample sizes need to be for heterogeneity of variance to be a problem. So if you have equal variances in your groups and unequal sample sizes, no problem. If you have unequal variances and equal sample sizes, no problem.

How do you know if an experiment is balanced?

Repeated measures designs have some great benefits, but there are a few drawbacks that you should consider. The largest downside is the problem of order effects, which can happen when you expose subjects to multiple treatments. These effects are associated with the treatment order but are not caused by the treatment.

What are the strengths and weaknesses of repeated measures design?

2. Repeated Measures:

• Pro: As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con: There may be order effects.
• Pro: Fewer people are needed as they take part in all conditions (i.e. saves time).

### When to use repeated measures?

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What are repeated measures?

Practice effects. Practice effects occur when a subject in an experiment is able to perform a task and then perform it again at some later time.

• Two types of repeated measures designs. Complete: A complete repeated measures design balances the practice effects that participants undergo against each other.