AObootBetween {AOboot}R Documentation

Bootstrapped ANOVA for Between-Subject Designs

Description

In case of violations of the assumption of the normal distribution, researchers usually employ bootstrapping. Based on the R packages afex and emmeans, this function computes bootstrapped confidence intervals for the effect sizes, estimated marginal means, and post hoc tests for one-way and two-way ANOVAs following a between-subject design. Furthermore, the p-values of the F-statistic are adjusted to reflect the probability to obtain equal or higher values than the raw, non-bootstrapped ANOVA (Stine, 1989 <doi:10.1177/0049124189018002003>; see also this tutorial by Nadine Spychala.).

Usage

AObootBetween(var.between,
              var.dv,
              var.id,
              levels.b1,
              levels.b2 = NULL,
              eff.si = c("pes", "ges"),
              data,
              silence = FALSE,
              n.sim = 1000,
              alpha = .05,
              seed = 1234,
              n.round = 2)
              

Arguments

var.between

Variable(s) reflecting the between-subject level.

var.dv

Dependent variable.

var.id

Unique person specifier.

levels.b1

Levels of the first-named independent variable. Must be identical with the levels in the dataset.

levels.b2

For two-way ANOVAs. Levels of the second-named independent variable. Must be identical with the levels in the dataset.

eff.si

Effect size for the F-tests. "pes" reflects partial eta-squared, "ges" reflects eta-squared.

data

Name of the dataframe.

silence

Logical. If FALSE, progress of the bootstrapping procedure will be displayed.

n.sim

Number of bootstrap samples to be drawn.

alpha

Type I error.

seed

To make the results reproducible, it is recommended to set a random seed parameter.

n.round

Number of digits in the output.

Details

The p-value of the F-test ('Pr(>F)') in the output reflects the probability to obtain an F-value as high as or higher than the F-value from the raw, non-bootstrapped ANOVA. Thus, it should not be mistaken as a p-value in the sense of a null hypothesis significance test. More information about this can be found in this tutorial by Nadine Spychala.

Value

type.aov

Type of ANOVA conducted.

factor

Name of the groups in the factor (in one-way ANOVA).

factor1

Name of the groups in the first factor (in two-way ANOVA).

factor2

Name of the groups in the second factor (in two-way ANOVA).

anova

Results of the conducted ANOVA (i.e., degrees of freedom, F-test, p-value, effect size with bootstrap confidence interval, and numbers of tests for which convergence was achieved.

em

Estimated marginal means in one-way ANOVA.

em.1

Estimated marginal means for factor 1 in two-way ANOVA.

em.2

Estimated marginal means for factor 2 in two-way ANOVA.

em.3

Estimated marginal means for factor 1 by factor 2 in two-way ANOVA.

em.4

Estimated marginal means for factor 2 by factor 1 in two-way ANOVA.

no.test

Number of post hoc tests in one-way ANOVAs for which convergence was achieved.

no.test1

Number of post hoc tests for factor 1 in two-way ANOVAs for which convergence was achieved.

no.test2

Number of post hoc tests for factor 2 in two-way ANOVAs for which convergence was achieved.

no.test3

Number of post hoc tests for factor 1 by factor 2 in two-way ANOVAs for which convergence was achieved.

no.test4

Number of post hoc tests for factor 2 by factor 1 in two-way ANOVAs for which convergence was achieved.

ph

Post hoc tests in one-way ANOVAs.

ph.1

Post hoc tests for factor 1 in two-way ANOVAs.

ph.2

Post hoc tests for factor 2 in two-way ANOVAs.

ph.3

Post hoc tests for factor 1 by factor 2 in two-way ANOVAs.

ph.4

Post hoc tests for factor 2 by factor 1 in two-way ANOVAs.

Author(s)

Lisa-Marie Segbert, Christian Blötner c.bloetner@gmail.com

References

Stine, R. (1989). An introduction to bootstrap methods: Examples and ideas. Sociological Methods & Research, 18(2-3), 243–291. <https://doi.org/10.1177/0049124189018002003>

Examples

library(carData)

# The OBrienKaiser dataset from the carData package

ao <- OBrienKaiser

# Add a unique person identifier to the dataset

ao$pers <- 1:nrow(OBrienKaiser)

# One-way between-subjects ANOVA

AObootBetween(
  var.between = "treatment",
  var.dv = "pre.1",
  var.id = "pers",
  levels.b1 = c("control", "A", "B"),
  eff.si = "ges",
  data = ao,
  n.sim = 1000,
  alpha = .05,
  seed = 1234,
  n.round = 2)
  

# Two-way between-subjects ANOVA

AObootBetween(
  var.between = c("treatment", "gender"),
  var.dv = "pre.1",
  var.id = "pers",
  levels.b1 = c("control", "A", "B"),
  levels.b2 = c("M", "F"),
  eff.si = "pes",
  data = ao,
  n.sim = 1000,
  alpha = .05,
  seed = 1236,
  n.round = 2)


[Package AOboot version 0.1.0 Index]