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Summary for:

Repeated Measures ANOVA

WHAT THIS ANALYSIS DOES:

Repeated measures ANOVA is used when you have the exact measure participants rated on at more than two-time points. A paired t-test will be sufficient with only two-time points, but a repeated measures ANOVA is required more times.Repeated Measures ANOVA is a statistical analysis used to determine if there is a significant difference between two or more related groups or conditions on one or more dependent variables. It is also known as within-subjects ANOVA or ANOVA with repeated measures.

TYPES OF DATA YOU NEED:

	How many?	Categorical or Continuous?	If categorical, # of levels (categories)	For DV: Paired or Repeated?
IV (predictors)	1	Categorical	3
DV (outcomes)	1	categorical or continuous		Repeated or Paired

IV = independent variable(s); DV = dependent variable(s)

HYPOTHESES:

H₀: There is no difference in exercises against calorie deficit across the time points,no significant difference between the groups or conditions on the dependent variable.

Ha: There is a difference in exercises against calorie deficit across the time points, asignificant difference between the groups or conditions on the dependent variable.

HOW TO RUN IT IN R:

To run a repeated measures ANOVA in R, the "aov()" function can be used, and the "ezANOVA()" function from the "ez" package can also be used for a more comprehensive output.

Clear the environment, then check for updates, afterward create a weekly calorie deficit and combine all the values, add an id column, and start to switch from wide to a long data set; once that's done, you will create a summary and see all of your data.

PARTS OF OUTPUTS TO READ/INTERPRET:

The output of a repeated measures ANOVA includes several parts that should be read and interpreted, including the F-value, degrees of freedom, p-value, and effect size measures such as partial eta-squared or Cohen's d.

library(psych)

library(tidyverse)

library(ggplot2)

library(ggpubr)

library(rstatix)

SAMPLE A P A STYLE WRITE-UP:

A repeated measures ANOVA was conducted to examine the effect of treatment conditions on participants' scores on the dependent variable. Results indicated a significant effect of treatment condition,F(1.67, 11.71) = 3.70, p = .06. p > .05, partial eta-squared = effect size. Post-hoc analyses revealed that scores in Condition A were significantly lower than scores in Condition B and C, t-value, and p-value for each comparison. These findings suggest that treatment condition doesn't significantly impact the dependent variable.The exercises did not differ in the calorie deficit across different time points.

OTHER NOTES:It is essential to note that the assumptions of normality, sphericity, and homogeneity of variance should be checked before running a repeated measures ANOVA. Additionally, appropriate corrections or adjustments should be made if these assumptions are violated.