This vignette provides a go-to summary for which test is carried out for each function included in the package and what effect size it returns. Additionally, there are also recommendations on how to interpret those effect sizes.

# Summary of statistical tests and effect sizes

Here is a summary table of all the statistical tests currently supported across various functions:

Functions Type Test Effect size 95% CI available?
expr_anova_parametric (2 groups) Parametric Student’s and Welch’s t-test Cohen’s d, Hedge’s g $$\checkmark$$
expr_anova_parametric (> 2 groups) Parametric Fisher’s and Welch’s one-way ANOVA $$\eta^2_p, \omega^2_p$$ $$\checkmark$$
expr_anova_nonparametric (2 groups) Non-parametric Mann-Whitney U-test r $$\checkmark$$
expr_anova_nonparametric (> 2 groups) Non-parametric Kruskal-Wallis Rank Sum Test $$\epsilon^2$$ $$\checkmark$$
expr_anova_robust (2 groups) Robust Yuen’s test for trimmed means $$\xi$$ $$\checkmark$$
expr_anova_robust (> 2 groups) Robust Heteroscedastic one-way ANOVA for trimmed means $$\xi$$ $$\checkmark$$
expr_anova_parametric (2 groups) Parametric Student’s t-test Cohen’s d, Hedge’s g $$\checkmark$$
expr_anova_parametric (> 2 groups) Parametric Fisher’s one-way repeated measures ANOVA $$\eta^2_p, \omega^2$$ $$\checkmark$$
expr_anova_nonparametric (2 groups) Non-parametric Wilcoxon signed-rank test r $$\checkmark$$
expr_anova_nonparametric (> 2 groups) Non-parametric Friedman rank sum test $$W_{Kendall}$$ $$\checkmark$$
expr_anova_robust (2 groups) Robust Yuen’s test on trimmed means for dependent samples $$\xi$$ $$\checkmark$$
expr_anova_robust (> 2 groups) Robust Heteroscedastic one-way repeated measures ANOVA for trimmed means $$\times$$ $$\times$$
expr_contingency_tab (unpaired) Parametric $$\text{Pearson's}~ \chi^2 ~\text{test}$$ Cramér’s V $$\checkmark$$
expr_contingency_tab (paired) Parametric McNemar’s test Cohen’s g $$\checkmark$$
expr_contingency_tab Parametric One-sample proportion test Cramér’s V $$\checkmark$$
expr_corr_test Parametric Pearson’s r r $$\checkmark$$
expr_corr_test Non-parametric $$\text{Spearman's}~ \rho$$ $$\rho$$ $$\checkmark$$
expr_corr_test Robust Percentage bend correlation r $$\checkmark$$
expr_t_onesample Parametric One-sample t-test Cohen’s d, Hedge’s g $$\checkmark$$
expr_t_onesample Non-parametric One-sample Wilcoxon signed rank test r $$\checkmark$$
expr_t_onesample Robust One-sample percentile bootstrap robust estimator $$\checkmark$$
expr_meta_random Parametric Meta-analysis via random-effects models $$\beta$$ $$\checkmark$$
expr_meta_random Robust Meta-analysis via robust random-effects models $$\beta$$ $$\checkmark$$

Note that the following recommendations on how to interpret the effect sizes are just suggestions and there is nothing universal about them. The interpretation of any effect size measures is always going to be relative to the discipline, the specific data, and the aims of the analyst. Here the guidelines are given for small, medium, and large effects and references should shed more information on the baseline discipline with respect to which these guidelines were recommended. This is important because what might be considered a small effect in psychology might be large for some other field like public health.

(Additionally, you will also see which function is used internally to compute the effect size and their confidence intervals.)

# One-sample tests

## parametric

Test: One-sample t-test
Effect size: Cohen’s d, Hedge’s g
Function: effectsize::cohens_d

Effect size Small Medium Large Range
Cohen’s d 0 – < 0.20 0.20 – < 0.50 ≥ 0.80 [-Inf,Inf]
Hedge’s g 0 – < 0.20 0.20 – < 0.50 ≥ 0.80 [-Inf,Inf]

## non-parametric

Test: One-sample Wilcoxon Signed-rank Test
Effect size: $$r$$ ( = $$Z/\sqrt(N_{obs})$$)
Function: rcompanion::wilcoxonOneSampleR

Effect size Small Medium Large Range
r 0.10 – < 0.30 0.30 – < 0.50 ≥ 0.50 [0,1]

## robust

Test: One-sample percentile bootstrap test
Effect size: robust location measure
Function: WRS2::onesampb

# Two-sample tests

## within-subjects design

### parametric

Test: Student’s dependent samples t-test
Effect size: Cohen’s d, Hedge’s g
Function: effectsize::cohens_d

Effect size Small Medium Large Range
Cohen’s d 0.20 0.50 0.80 [0,1]
Hedge’s g 0.20 0.50 0.80 [0,1]

### non-parametric

Test: Wilcoxon signed-rank test
Effect size: $$r$$ ( = $$Z/\sqrt(N_{pairs})$$)
Function: rcompanion::wilcoxonPairedR

Effect size Small Medium Large Range
r 0.10 – < 0.30 0.30 – < 0.50 ≥ 0.50 [0,1]

### robust

Test: Yuen’s dependent sample trimmed means t-test
Effect size: robust (trimmed-Winsorized) standardized difference similar to Cohen’s d
Function: WRS2::dep.effect

Effect size Small Medium Large Range
$$\delta_{R}$$ 0.10 – < 0.30 0.30 – < 0.50 ≥ 0.50 [0,1]

## between-subjects design

### parametric

Test: Student’s and Welch’s independent samples t-test
Effect size: Cohen’s d, Hedge’s g
Function: effectsize::cohens_d

Effect size Small Medium Large Range
Cohen’s d 0.20 0.50 0.80 [-Inf,Inf]
Hedge’s g 0.20 0.50 0.80 [-Inf,Inf]

### non-parametric

Test: Two-sample Mann–Whitney U Test
Effect size: $$r$$ ( = $$Z/\sqrt(N_{obs})$$)
Function: rcompanion::wilcoxonR

Effect size Small Medium Large Range
r 0.10 – < 0.30 0.30 – < 0.50 ≥ 0.50 [0,1]

### robust

Test: Yuen’s independent sample trimmed means t-test
Effect size: Explanatory measure of effect size ($$\xi$$)
Function: WRS2::yuen.effect.ci

Effect size Small Medium Large Range
$$\xi$$ 0.10 – < 0.30 0.30 – < 0.50 ≥ 0.50 [0,1]

# One-way ANOVAs

## within-subjects design

### parametric

Test: Fisher’s repeated measures one-way ANOVA
Effect size: $$\eta^2_p$$, $$\omega^2_p$$
Function: effectsize::eta_squared and effectsize::omega_squared

Effect size Small Medium Large Range
$$\omega^2$$ 0.01 – < 0.06 0.06 – < 0.14 ≥ 0.14 [0,1]
$$\eta^2_p$$ 0.01 – < 0.06 0.06 – < 0.14 ≥ 0.14 [0,1]

### non-parametric

Test: Friedman’s rank sum test
Effect size: Kendall’s W
Function: rcompanion::kendallW

In the following table, k is the number of treatments, groups, or things being rated.

k Small Medium Large Range
k = 3 < 0.10 0.10 – < 0.30 ≥ 0.30 [0,1]
k = 5 < 0.10 0.10 – < 0.25 ≥ 0.25 [0,1]
k = 7 < 0.10 0.10 – < 0.20 ≥ 0.20 [0,1]
k = 9 < 0.10 0.10 – < 0.20 ≥ 0.20 [0,1]

### robust

Test: Heteroscedastic one-way repeated measures ANOVA for trimmed means
Effect size: Not available

## between-subjects design

### parametric

Test: Fisher’s or Welch’s one-way ANOVA
Effect size: $$\eta^2_p$$, $$\omega^2_p$$
Function: effectsize::eta_squared and effectsize::omega_squared

Effect size Small Medium Large Range
$$\eta^2$$ 0.01 – < 0.06 0.06 – < 0.14 ≥ 0.14 [0,1]
$$\omega^2$$ 0.01 – < 0.06 0.06 – < 0.14 ≥ 0.14 [0,1]
$$\eta^2_p$$ 0.01 – < 0.06 0.06 – < 0.14 ≥ 0.14 [0,1]
$$\omega^2_p$$ 0.01 – < 0.06 0.06 – < 0.14 ≥ 0.14 [0,1]

### non-parametric

Test: Kruskal–Wallis test
Effect size: $$\epsilon^2$$
Function: rcompanion::epsilonSquared

Effect size Small Medium Large Range
$$\epsilon^2$$ 0.01 – < 0.08 0.08 – < 0.26 ≥ 0.26 [0,1]

### robust

Test: Heteroscedastic one-way ANOVA for trimmed means
Effect size: Explanatory measure of effect size ($$\xi$$)
Function: WRS2::t1way

Effect size Small Medium Large Range
$$\xi$$ 0.10 – < 0.30 0.30 – < 0.50 ≥ 0.50 [0,1]

# Contingency table analyses

## association test - unpaired

Test: Pearson’s $$\chi^2$$-squared test
Effect size: Cramér’s V
Function: effectsize::cramers_v

In the following table, k is the minimum number of categories in either rows or columns.

k Small Medium Large Range
k = 2 0.10 – < 0.30 0.30 – < 0.50 ≥ 0.50 [0,1]
k = 3 0.07 – < 0.20 0.20 – < 0.35 ≥ 0.35 [0,1]
k = 4 0.06 – < 0.17 0.17 – < 0.29 ≥ 0.29 [0,1]

## association test - paired

Test: McNemar’s test
Effect size: Cohen’s g
Function: effectsize::cohens_g

Effect size Small Medium Large Range
Cohen’s g 0.05 – < 0.15 0.15 – < 0.25 ≥ 0.25 [0,1]

## goodness-of-fit test

Test: Pearson’s $$\chi^2$$-squared goodness-of-fit test
Effect size: Cramér’s V
Function: effectsize::cramers_v

In the following table, k is the number of categories.

k Small Medium Large Range
k = 2 0.100 – < 0.300 0.300 – < 0.500 ≥ 0.500 [0,1]
k = 3 0.071 – < 0.212 0.212 – < 0.354 ≥ 0.354 [0,1]
k = 4 0.058 – < 0.173 0.173 – < 0.289 ≥ 0.289 [0,1]
k = 5 0.050 – < 0.150 0.150 – < 0.250 ≥ 0.250 [0,1]
k = 6 0.045 – < 0.134 0.134 – < 0.224 ≥ 0.224 [0,1]
k = 7 0.043 – < 0.130 0.130 – < 0.217 ≥ 0.217 [0,1]
k = 8 0.042 – < 0.127 0.127 – < 0.212 ≥ 0.212 [0,1]
k = 9 0.042 – < 0.125 0.125 – < 0.209 ≥ 0.209 [0,1]
k = 10 0.041 – < 0.124 0.124 – < 0.207 ≥ 0.207 [0,1]

# Correlation analyses

## parametric

Test: Pearson product-moment correlation coefficient
Effect size: Pearson’s correlation coefficient (r)
Function: correlation::correlation

Effect size Small Medium Large Range
Pearson’s r 0.10 – < 0.30 0.30 – < 0.50 ≥ 0.50 [-1,1]

## non-parametric

Test: Spearman’s rank correlation coefficient
Effect size: Spearman’s rank correlation coefficient ($$\rho$$)
Function: correlation::correlation

Effect size Small Medium Large Range
Spearman’s $$\rho$$ 0.10 – < 0.30 0.30 – < 0.50 ≥ 0.50 [-1,1]

## robust

Test: Percentage bend correlation coefficient
Effect size: Percentage bend correlation coefficient ($$\rho_{pb}$$)
Function: correlation::correlation

Effect size Small Medium Large Range
$$\rho_{pb}$$ 0.10 – < 0.30 0.30 – < 0.50 ≥ 0.50 [-1,1]

# Meta-analysis

## parametric

Test: Parametric random-effects meta-analysis
Effect size: Regression estimate ($$\beta$$)
Function: metafor::rma

## robust

Test: Random-effects meta-analysis using a mixture of normals for the random effect
Effect size: Regression estimate ($$\beta$$)
Function: metaplus::metaplus

## Bayesian

Test: Bayesian random-effects meta-analysis
Effect size: Regression estimate ($$\beta$$)
Function: metaBMA::meta_random

# Dataframe as output

Although the primary focus of this package is to get expressions containing statistical results, one can also use it to extract dataframes containing these details.

For a more detailed summary of these dataframe: https://indrajeetpatil.github.io/statsExpressions/articles/dataframe_outputs.html

# Suggestions

If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues