This calculator analyzes data where the same subjects are measured multiple times under different conditions or time points. Whether you have long format data (one row per measurement) or wide format data (one row per subject with multiple measurement columns), this calculator handles both formats. Unlike regular ANOVA that compares different groups, Repeated Measures ANOVA controls for individual differences between subjects, making it more powerful for detecting treatment effects when using the same participants across conditions.
Important Note: This test requires each subject to have measurements for all conditions. Subjects with missing data will be excluded from the analysis.
Ready to analyze your repeated measures data? or to see how it works, or upload your own data to discover if there are significant changes across your conditions.
Leave blank to sort alphabetically by default
Repeated Measures ANOVA analyzes the differences between three or more related measurements on the same subjects over time or under different conditions. It accounts for the correlation between repeated measurements on the same subject. It is commonly applied in within-subjects experimental designs where the same subjects are exposed to different treatments or conditions.
F-statistic:
Sum of Squares:
We'll organize the scores data in a long format, which is required by rstatix. Each score will be associated with its respective student and teaching method.
library(tidyverse)
library(rstatix)
scores_long <- tibble(
Student = rep(1:5, each = 3),
Method = rep(c("A", "B", "C"), times = 5),
Score = c(85, 90, 88, 78, 85, 82, 92, 95, 93, 88, 86, 89, 80, 84, 83)
)
Using rstatixwe will compute repeated measures ANOVA and summarize the results. Here's the code:
# Perform repeated measures ANOVA
res.aov <- scores_long |>
anova_test(dv = Score, wid = Student, within = Method)
# Get ANOVA table
res.aov %>%
get_anova_table()
The output provides the following key information:
Output:
# ANOVA Table (type III tests)
# Effect DFn DFd F p p<.05 ges
# 1 Method 2 8 4.925 0.04 * 0.094
Since and , we reject . There is sufficient evidence to conclude that the teaching methods differ significantly.
Generalized Eta-squared ():
Interpretation guidelines:
For the example above, the generalized eta-squared effect size is , indicating a medium to large effect.
library(tidyverse)
library(rstatix)
# Original Data
scores <- tibble(
Student = 1:5,
Method_A = c(85, 78, 92, 88, 80),
Method_B = c(90, 85, 95, 86, 84),
Method_C = c(88, 82, 93, 89, 83)
)
# Convert to long format
scores_long <- scores |>
pivot_longer(
cols = starts_with("Method"),
names_to = "Method",
values_to = "Score"
)
res.aov <- scores_long %>%
anova_test(dv = Score, wid = Student, within = Method)
# Get ANOVA table
res.aov |>
get_anova_table()import pandas as pd
from statsmodels.stats.anova import AnovaRM
# Data
data = pd.DataFrame({
'Student': [1, 2, 3, 4, 5],
'Method_A': [85, 78, 92, 88, 80],
'Method_B': [90, 85, 95, 86, 84],
'Method_C': [88, 82, 93, 89, 83]
})
# Reshape to long format
data_long = pd.melt(data, id_vars=['Student'], var_name='Method', value_name='Score')
# Repeated Measures ANOVA
anova = AnovaRM(data_long, 'Score', 'Student', within=['Method'])
res = anova.fit()
print(res)Consider these alternatives:
This calculator analyzes data where the same subjects are measured multiple times under different conditions or time points. Whether you have long format data (one row per measurement) or wide format data (one row per subject with multiple measurement columns), this calculator handles both formats. Unlike regular ANOVA that compares different groups, Repeated Measures ANOVA controls for individual differences between subjects, making it more powerful for detecting treatment effects when using the same participants across conditions.
Important Note: This test requires each subject to have measurements for all conditions. Subjects with missing data will be excluded from the analysis.
Ready to analyze your repeated measures data? or to see how it works, or upload your own data to discover if there are significant changes across your conditions.
Leave blank to sort alphabetically by default
Repeated Measures ANOVA analyzes the differences between three or more related measurements on the same subjects over time or under different conditions. It accounts for the correlation between repeated measurements on the same subject. It is commonly applied in within-subjects experimental designs where the same subjects are exposed to different treatments or conditions.
F-statistic:
Sum of Squares:
We'll organize the scores data in a long format, which is required by rstatix. Each score will be associated with its respective student and teaching method.
library(tidyverse)
library(rstatix)
scores_long <- tibble(
Student = rep(1:5, each = 3),
Method = rep(c("A", "B", "C"), times = 5),
Score = c(85, 90, 88, 78, 85, 82, 92, 95, 93, 88, 86, 89, 80, 84, 83)
)
Using rstatixwe will compute repeated measures ANOVA and summarize the results. Here's the code:
# Perform repeated measures ANOVA
res.aov <- scores_long |>
anova_test(dv = Score, wid = Student, within = Method)
# Get ANOVA table
res.aov %>%
get_anova_table()
The output provides the following key information:
Output:
# ANOVA Table (type III tests)
# Effect DFn DFd F p p<.05 ges
# 1 Method 2 8 4.925 0.04 * 0.094
Since and , we reject . There is sufficient evidence to conclude that the teaching methods differ significantly.
Generalized Eta-squared ():
Interpretation guidelines:
For the example above, the generalized eta-squared effect size is , indicating a medium to large effect.
library(tidyverse)
library(rstatix)
# Original Data
scores <- tibble(
Student = 1:5,
Method_A = c(85, 78, 92, 88, 80),
Method_B = c(90, 85, 95, 86, 84),
Method_C = c(88, 82, 93, 89, 83)
)
# Convert to long format
scores_long <- scores |>
pivot_longer(
cols = starts_with("Method"),
names_to = "Method",
values_to = "Score"
)
res.aov <- scores_long %>%
anova_test(dv = Score, wid = Student, within = Method)
# Get ANOVA table
res.aov |>
get_anova_table()import pandas as pd
from statsmodels.stats.anova import AnovaRM
# Data
data = pd.DataFrame({
'Student': [1, 2, 3, 4, 5],
'Method_A': [85, 78, 92, 88, 80],
'Method_B': [90, 85, 95, 86, 84],
'Method_C': [88, 82, 93, 89, 83]
})
# Reshape to long format
data_long = pd.melt(data, id_vars=['Student'], var_name='Method', value_name='Score')
# Repeated Measures ANOVA
anova = AnovaRM(data_long, 'Score', 'Student', within=['Method'])
res = anova.fit()
print(res)Consider these alternatives: