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AgentValor10744
Fix the mistakes Summary for: Repeated Measures ANOVA …
Fix the mistakes
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, a significant 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.
