Repeated anova
6 important questions on Repeated anova
When is a repeated measurement anova used?
How do you perform a bayesian repeated measures anova in jasp?
- Go to anova and select repeated measures bayesian anova
- in repeated measures factor you specify the design of your experiment
- for a flanker task for example you had three blocks and inconsistent and consistent trials
- specify blocks with three levels and consistency with 2 levels
- in repeated measures cells, jasp has made categories for your data to be provided to based on your factors. This is where your variables are placed.
- select which bayes factor you want to obtain
- in the order you can specify in which way the models will be compared
How does a bayesian repeated measures anova analyse the data?
- In frequentist statistics the repeated measures anova would test whether a factor, consistency for example, would cause a significant difference on the dependent variable between the levels.
- in bayesian statistic various models are created based on the independent variables specified. These models vary in what independent variables they include.
- these models are then compared to each other in how well they describe the data.
- if a model which includes an independent variable performs better than a model which doesn't include that variable, it can be said that that variable is crucial in describing the data.
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What is a downside of comparing models to each other in bayesian repeated anova?
in a frequentist approach this wouldn't happen since the effects of each individual factor are tested irrespective of each other instead of as a collective
How can you interpret the effects table of a bayesian repeated measures anova?
- The table shows how much evidence there is for including a factor into a model, based on the comparisons of models.
- if BF incl is very larger the factor should be included.
What would it look like if two variables do not have an interaction effect?
- The relationship between prozac and no prozac on the test score is not influenced by whether the participants receive therapy or not. This is shown because the lines go down equally steep.
- viewed from the other perspective, medication does not influence the effectiveness of the therapy because the distance between the ends of the green and blue line is equal in both conditions.
if lines are not parallel, there is an interaction
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