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How to conduct a repeated measures ANOVA (in Python, using pandas and pingouin)

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In a repeated measures test, the same subject receives multiple treatments. When you have a dataset that includes the responses of a repeated measures test where the measurements are dependent (within subjects design), you may wish to check if there is a difference in the treatment effects. How would you conduct a repeated measures ANOVA to answer that question?

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We create a hypothetical repeated measures dataset where the 5 subjects undergo all 4 skin treatments and their rating of the treatment is measured.

import pandas as pd
df = pd.DataFrame( {
    'Subject':        [1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5],
    'Skin Treatment': ['W','X','Y','Z','W','X','Y','Z','W','X',
    'Rating':         [7,5,8,4,8,10,7,5,7,6,5,4,7,7,4,5,8,8,6,6]
} )
Subject Skin Treatment Rating
0 1 W 7
1 1 X 5
2 1 Y 8
3 1 Z 4
4 2 W 8

Before we conduct a repeated measures ANOVA, we need to decide which approach to use - Univariate or Multivariate. We decide this using Mauchly’s test of sphericity. If we fail to reject the null hypothesis then we use the univariate approach.

  • $H_0 =$ the sphericity assumption holds
  • $H_A =$ the sphericity assumption is violated

We use the pingouin statistics package to conduct the test. Most of the parameters below are self-explanatory, except that dv stands for dependent variable.

import pingouin as pg
pg.sphericity( dv='Rating', within='Skin Treatment', subject='Subject', method='mauchly', data=df )
SpherResults(spher=True, W=0.06210054956238558, chi2=7.565056754547507, dof=5, pval=0.20708214225927316)

Since the $p$ value of skin_treatment is about $0.2071$, we fail to reject the sphericity assumption at a 5% significance level and use the univariate approach to conduct the repeated measures ANOVA.

# Compute a repeated measures ANOVA using a function pingouin adds to our DataFrame:
df.rm_anova( dv='Rating', within='Skin Treatment', subject='Subject', detailed=False )
Source ddof1 ddof2 F p-unc ng2 eps
0 Skin Treatment 3 12 5.117647 0.016501 0.430267 0.541199

Since the $p$ value of about $0.017$ is less than 0.05, we conclude that there is significant evidence of a treatment effect.

Note: If there is more than 1 repeated measures factor, you can add a list of them to the within parameter and conduct the test.

Content last modified on 24 July 2023.

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Contributed by Krtin Juneja (