Can you run a repeated measures ANOVA with missing data?
One of the biggest problems with traditional repeated measures ANOVA is missing data on the response variable. The problem is that repeated measures ANOVA treats each measurement as a separate variable. Because it uses listwise deletion, if one measurement is missing, the entire case gets dropped.
What is wrong with using a repeated measures design?
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 handle missing data in statistics?
Best techniques to handle missing data
- Use deletion methods to eliminate missing data. The deletion methods only work for certain datasets where participants have missing fields.
- Use regression analysis to systematically eliminate data.
- Data scientists can use data imputation techniques.
Can ANOVA handle missing data?
This analysis works fine even when there are some missing values. This mixed model choice is offered in the ANOVA parameters dialogs. The results of fitting a mixed model with missing values will be meaningful, of course, only if the values are missing for random reasons.
What is a 2 way repeated-measures ANOVA?
For Two-Way Repeated Measures ANOVA, “Two-way” means that there are two factors in the experiment, for example, different treatments and different conditions. “Repeated-measures” means that the same subject received more than one treatment and/or more than one condition.
What is a repeated-measures t-test?
The t-test assesses whether the mean scores from two experimental conditions are statistically different from one another. A repeated-measures t-test (also known by other names such as the ‘paired samples’ or ‘related’ t-test) is what you should use in situations when your design is within participants.
What is an example of a repeated measures design?
In a repeated measures design, each group member in an experiment is tested for multiple conditions over time or under different conditions. For example, a group of people with Type II diabetes might be given medications to see if it helps control their disease, and then they might be given nutritional counseling.
What are the strengths of repeated measures design?
The primary strengths of the repeated measures design is that it makes an experiment more efficient and helps keep the variability low. This helps to keep the validity of the results higher, while still allowing for smaller than usual subject groups.
What are the assumptions of repeated-measures ANOVA?
Assumptions for Repeated Measures ANOVA
- Independent and identically distributed variables (“independent observations”).
- Normality: the test variables follow a multivariate normal distribution in the population.
- Sphericity: the variances of all difference scores among the test variables must be equal in the population.