pingouin.pairwise_gameshowell#

pingouin.pairwise_gameshowell(data=None, dv=None, between=None, effsize='hedges')[source]#

Pairwise Games-Howell post-hoc test.

Parameters:
datapandas.DataFrame

DataFrame

dvstring

Name of column containing the dependent variable.

between: string

Name of column containing the between factor.

effsizestring or None

Effect size type. Available methods are:

  • 'none': no effect size

  • 'cohen': Unbiased Cohen d

  • 'hedges': Hedges g

  • 'r': Pearson correlation coefficient

  • 'eta-square': Eta-square

  • 'odds-ratio': Odds ratio

  • 'AUC': Area Under the Curve

  • 'CLES': Common Language Effect Size

Returns:
statspandas.DataFrame

Stats summary:

  • 'A': Name of first measurement

  • 'B': Name of second measurement

  • 'mean(A)': Mean of first measurement

  • 'mean(B)': Mean of second measurement

  • 'diff': Mean difference (= mean(A) - mean(B))

  • 'se': Standard error

  • 'T': T-values

  • 'df': adjusted degrees of freedom

  • 'pval': Games-Howell corrected p-values

  • 'hedges': Hedges effect size (or any effect size defined in effsize)

See also

pairwise_tests, pairwise_tukey

Notes

Games-Howell [1] is very similar to the Tukey HSD post-hoc test but is much more robust to heterogeneity of variances. While the Tukey-HSD post-hoc is optimal after a classic one-way ANOVA, the Games-Howell is optimal after a Welch ANOVA. Please note that Games-Howell is not valid for repeated measures ANOVA. Only one-way ANOVA design are supported.

Compared to the Tukey-HSD test, the Games-Howell test uses different pooled variances for each pair of variables instead of the same pooled variance.

The T-values are defined as:

\[t = \frac{\overline{x}_i - \overline{x}_j} {\sqrt{(\frac{s_i^2}{n_i} + \frac{s_j^2}{n_j})}}\]

and the corrected degrees of freedom are:

\[v = \frac{(\frac{s_i^2}{n_i} + \frac{s_j^2}{n_j})^2} {\frac{(\frac{s_i^2}{n_i})^2}{n_i-1} + \frac{(\frac{s_j^2}{n_j})^2}{n_j-1}}\]

where \(\overline{x}_i\), \(s_i^2\), and \(n_i\) are the mean, variance and sample size of the first group and \(\overline{x}_j\), \(s_j^2\), and \(n_j\) the mean, variance and sample size of the second group.

The p-values are then approximated using the Studentized range distribution \(Q(\sqrt2|t_i|, r, v_i)\).

References

[1]

Games, Paul A., and John F. Howell. “Pairwise multiple comparison procedures with unequal n’s and/or variances: a Monte Carlo study.” Journal of Educational Statistics 1.2 (1976): 113-125.

[2]

Gleason, John R. “An accurate, non-iterative approximation for studentized range quantiles.” Computational statistics & data analysis 31.2 (1999): 147-158.

Examples

Pairwise Games-Howell post-hocs on the Penguins dataset.

>>> import pingouin as pg
>>> df = pg.read_dataset('penguins')
>>> pg.pairwise_gameshowell(data=df, dv='body_mass_g',
...                         between='species').round(3)
           A          B   mean(A)   mean(B)      diff      se       T       df  pval  hedges
0     Adelie  Chinstrap  3700.662  3733.088   -32.426  59.706  -0.543  152.455  0.85  -0.074
1     Adelie     Gentoo  3700.662  5076.016 -1375.354  58.811 -23.386  249.643  0.00  -2.860
2  Chinstrap     Gentoo  3733.088  5076.016 -1342.928  65.103 -20.628  170.404  0.00  -2.875