kruskal_wallis_test {sjstats}

Kruskal-Wallis test

Description

This function performs a Kruskal-Wallis rank sum test, which is a non-parametric method to test the null hypothesis that the population median of all of the groups are equal. The alternative is that they differ in at least one. Unlike the underlying base R function kruskal.test(), this function allows for weighted tests.

Usage

kruskal_wallis_test(data, select = NULL, by = NULL, weights = NULL)

Arguments

data

A data frame.

select

Name(s) of the continuous variable(s) (as character vector) to be used as samples for the test. select can be one of the following:

• select can be used in combination with by, in which case select is the name of the continous variable (and by indicates a grouping factor).

• select can also be a character vector of length two or more (more than two names only apply to kruskal_wallis_test()), in which case the two continuous variables are treated as samples to be compared. by must be NULL in this case.

• If select select is of length two and paired = TRUE, the two samples are considered as dependent and a paired test is carried out.

• If select specifies one variable and by = NULL, a one-sample test is carried out (only applicable for t_test() and wilcoxon_test())

• For chi_squared_test(), if select specifies one variable and both by and probabilities are NULL, a one-sample test against given probabilities is automatically conducted, with equal probabilities for each level of select.

by

Name of the variable indicating the groups. Required if select specifies only one variable that contains all samples to be compared in the test. If by is not a factor, it will be coerced to a factor. For chi_squared_test(), if probabilities is provided, by must be NULL.

weights

Name of an (optional) weighting variable to be used for the test.

Details

The function simply is a wrapper around kruskal.test(). The weighted version of the Kruskal-Wallis test is based on the survey package, using survey::svyranktest().

Value

A data frame with test results.

Which test to use

The following table provides an overview of which test to use for different types of data. The choice of test depends on the scale of the outcome variable and the number of samples to compare.

Samples	Scale of Outcome	Significance Test
1	binary / nominal	chi_squared_test()
1	continuous, not normal	wilcoxon_test()
1	continuous, normal	t_test()
2, independent	binary / nominal	chi_squared_test()
2, independent	continuous, not normal	mann_whitney_test()
2, independent	continuous, normal	t_test()
2, dependent	binary (only 2x2)	chi_squared_test(paired=TRUE)
2, dependent	continuous, not normal	wilcoxon_test()
2, dependent	continuous, normal	t_test(paired=TRUE)
>2, independent	continuous, not normal	kruskal_wallis_test()
>2, independent	continuous, normal	datawizard::means_by_group()
>2, dependent	continuous, not normal	not yet implemented (1)
>2, dependent	continuous, normal	not yet implemented (2)

(1) More than two dependent samples are considered as repeated measurements. For ordinal or not-normally distributed outcomes, these samples are usually tested using a friedman.test(), which requires the samples in one variable, the groups to compare in another variable, and a third variable indicating the repeated measurements (subject IDs).

(2) More than two dependent samples are considered as repeated measurements. For normally distributed outcomes, these samples are usually tested using a ANOVA for repeated measurements. A more sophisticated approach would be using a linear mixed model.

References

• Bender, R., Lange, S., Ziegler, A. Wichtige Signifikanztests. Dtsch Med Wochenschr 2007; 132: e24–e25

• du Prel, J.B., Röhrig, B., Hommel, G., Blettner, M. Auswahl statistischer Testverfahren. Dtsch Arztebl Int 2010; 107(19): 343–8

See Also

• t_test() for parametric t-tests of dependent and independent samples.

• mann_whitney_test() for non-parametric tests of unpaired (independent) samples.

• wilcoxon_test() for Wilcoxon rank sum tests for non-parametric tests of paired (dependent) samples.

• kruskal_wallis_test() for non-parametric tests with more than two independent samples.

• chi_squared_test() for chi-squared tests (two categorical variables, dependent and independent).

Examples

data(efc)
# Kruskal-Wallis test for elder's age by education
kruskal_wallis_test(efc, "e17age", by = "c172code")

# when data is in wide-format, specify all relevant continuous
# variables in `select` and omit `by`
set.seed(123)
wide_data <- data.frame(
  scale1 = runif(20),
  scale2 = runif(20),
  scale3 = runif(20)
)
kruskal_wallis_test(wide_data, select = c("scale1", "scale2", "scale3"))

# same as if we had data in long format, with grouping variable
long_data <- data.frame(
  scales = c(wide_data$scale1, wide_data$scale2, wide_data$scale3),
  groups = rep(c("A", "B", "C"), each = 20)
)
kruskal_wallis_test(long_data, select = "scales", by = "groups")
# base R equivalent
kruskal.test(scales ~ groups, data = long_data)