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

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

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)

Bootstrapped ANOVA for Mixed 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 Mixed ANOVAs. 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

AObootMixed(var.within,
            var.between,
            var.id,
            levels.w1,
            levels.b1,
            eff.si = c("pes", "ges"),
            data,
            silence = FALSE,
            n.sim = 1000,
            alpha = .05,
            seed = 1234,
            n.round = 2)
            

Arguments

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

output <- list(type.aov = "Two-way mixed ANOVA", factor1 = levels.b1, factor2 = levels.w1, anova = round(orig.aov$anova_table, n.round), em.1 = dat.em1, no.test1 = no.test1, ph.1 = dat.ph1, em.2 = dat.em2, no.test2 = no.test2, ph.2 = dat.ph2, em.3 = dat.em3, no.test3 = no.test3, ph.3 = dat.ph3, em.4 = dat.em4, no.test4 = no.test4, ph.4 = dat.ph4)

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)

# Mixed ANOVA

AObootMixed(
    var.within = c("pre.1", "post.1", "fup.1"),
    var.between = "treatment",
    var.id = "pers",
    levels.w1 = c("pre", "post", "fup"),
    levels.b1 = c("control", "A", "B"),
    eff.si = "pes",
    data = ao,
    n.sim = 1000,
    alpha = .05,
    seed = 1234,
    n.round = 2)

Bootstrapped ANOVA for Within-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 within-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

AObootWithin(var.within,
             var.id,
             levels.w1,
             levels.w2 = NULL,
             eff.si = c("pes", "ges"),
             data,
             silence = FALSE,
             n.sim = 1000,
             alpha = .05,
             seed = 1234,
             n.round = 2)
             

Arguments

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

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 within-subjects ANOVA

AObootWithin(
    var.within = c("pre.1", "post.1", "fup.1"),
    var.id = "pers",
    levels.w1 = c("pre", "post", "fup"),
    eff.si = "ges",
    data = ao,
    n.sim = 1000,
    alpha = .05,
    seed = 1234,
    n.round = 2)
    

# Two-way within-subjects ANOVA

AObootWithin(
  var.within = c("pre.1", "pre.2", "pre.3", "pre.4", "pre.5",
                 "post.1", "post.2", "post.3", "post.4", "post.5",
                 "fup.1", "fup.2", "fup.3", "fup.4", "fup.5"),
  var.id = "pers",
  levels.w1 = c("pre", "post", "fup"),
  levels.w2 = c("1", "2", "3", "4", "5"),
  eff.si = "pes",
  data = ao,
  n.sim = 1000,
  alpha = .05,
  seed = 1234,
  n.round = 2)

AOboot.one Class

Description

A S3 class to represent one-way ANOVAs.

Usage

AOboot_one(type.aov, factor, anova, em, no.test, ph)

Arguments

Value

An object of class "AOboot.one".

AOboot.two Class

Description

A S3 class to represent two-way ANOVAs.

Usage

AOboot_two(
  type.aov,
  factor1,
  factor2,
  anova,
  em.1,
  no.test1,
  ph.1,
  em.2,
  no.test2,
  ph.2,
  em.3,
  no.test3,
  ph.3,
  em.4,
  no.test4,
  ph.4
)

Arguments

Value

An object of class "AOboot.two".