Mann-Whitney-U-Test — mwu • sjstats

This function performs a Mann-Whitney-U-Test (or Wilcoxon rank sum test, see wilcox.test and wilcox_test) for x, for each group indicated by grp. If grp has more than two categories, a comparison between each combination of two groups is performed.

The function reports U, p and Z-values as well as effect size r and group-rank-means.

mwu(
  data,
  x,
  grp,
  distribution = "asymptotic",
  out = c("txt", "viewer", "browser"),
  encoding = "UTF-8",
  file = NULL
)

mannwhitney(
  data,
  x,
  grp,
  distribution = "asymptotic",
  out = c("txt", "viewer", "browser"),
  encoding = "UTF-8",
  file = NULL
)

Arguments

data: A data frame.
x: Bare (unquoted) variable name, or a character vector with the variable name.
grp: Bare (unquoted) name of the cross-classifying variable, where x is grouped into the categories represented by grp, or a character vector with the variable name.
distribution: Indicates how the null distribution of the test statistic should be computed. May be one of "exact", "approximate" or "asymptotic" (default). See wilcox_test for details.
out: Character vector, indicating whether the results should be printed to console (out = "txt") or as HTML-table in the viewer-pane (out = "viewer") or browser (out = "browser"), of if the results should be plotted (out = "plot", only applies to certain functions). May be abbreviated.
encoding: Character vector, indicating the charset encoding used for variable and value labels. Default is "UTF-8". Only used when out is not "txt".
file: Destination file, if the output should be saved as file. Only used when out is not "txt".

Value

(Invisibly) returns a data frame with U, p and Z-values for each group-comparison as well as effect-size r; additionally, group-labels and groups' n's are also included.

Note

This function calls the wilcox_test with formula. If grp has more than two groups, additionally a Kruskal-Wallis-Test (see kruskal.test) is performed.

Interpretation of effect sizes, as a rule-of-thumb:

small effect >= 0.1
medium effect >= 0.3
large effect >= 0.5

Examples

data(efc)
# Mann-Whitney-U-Tests for elder's age by elder's dependency.
mwu(efc, e17age, e42dep)
#> 
#> # Mann-Whitney-U-Test
#> 
#> Groups 1 = independent (n = 65) | 2 = slightly dependent (n = 224):
#>   U = 7635.000, W = 5490.000, p = 0.003, Z = -3.020
#>   effect-size r =   0.178
#>    rank-mean(1) = 117.46
#>    rank-mean(2) = 152.99
#> 
#> Groups 1 = independent (n = 65) | 3 = moderately dependent (n = 304):
#>   U = 8692.000, W = 6547.000, p < .001, Z = -4.273
#>   effect-size r =   0.222
#>    rank-mean(1) = 133.72
#>    rank-mean(3) = 195.96
#> 
#> Groups 1 = independent (n = 65) | 4 = severely dependent (n = 297):
#>   U = 7905.500, W = 5760.500, p < .001, Z = -5.096
#>   effect-size r =   0.268
#>    rank-mean(1) = 121.62
#>    rank-mean(4) = 194.60
#> 
#> Groups 2 = slightly dependent (n = 224) | 3 = moderately dependent (n = 304):
#>   U = 54664.500, W = 29464.500, p = 0.008, Z = -2.647
#>   effect-size r =   0.115
#>    rank-mean(2) = 244.04
#>    rank-mean(3) = 279.58
#> 
#> Groups 2 = slightly dependent (n = 224) | 4 = severely dependent (n = 297):
#>   U = 51007.500, W = 25807.500, p < .001, Z = -4.386
#>   effect-size r =   0.192
#>    rank-mean(2) = 227.71
#>    rank-mean(4) = 286.11
#> 
#> Groups 3 = moderately dependent (n = 304) | 4 = severely dependent (n = 297):
#>   U = 87819.500, W = 41459.500, p = 0.083, Z = -1.732
#>   effect-size r =   0.071
#>    rank-mean(3) = 288.88
#>    rank-mean(4) = 313.41
#> 
#> # Kruskal-Wallis-Test
#> 
#> chi-squared = 38.476
#> df = 3
#> p < .001***