esci (version 1.0.7)

estimate_mdiff_2x2_between: Estimates for a 2x2 between-subjects design with a continuous outcome variable

Description

Returns object estimate_mdiff_2x2_between is suitable for a 2x2 between-subjects design with a continuous outcome variable. It estimates each main effect, the simple effects for the first factor, and the interaction. It can express these estimates as mean differences, standardized mean differences (Cohen's d), and as median differences (raw data only). You can pass raw data or or summary data (summary data does not return medians).

Usage

estimate_mdiff_2x2_between(
  data = NULL,
  outcome_variable = NULL,
  grouping_variable_A = NULL,
  grouping_variable_B = NULL,
  means = NULL,
  sds = NULL,
  ns = NULL,
  grouping_variable_A_levels = NULL,
  grouping_variable_B_levels = NULL,
  outcome_variable_name = "My outcome variable",
  grouping_variable_A_name = "A",
  grouping_variable_B_name = "A",
  conf_level = 0.95,
  assume_equal_variance = FALSE,
  save_raw_data = TRUE
)

Value

Returns object of class esci_estimate

• es_mean_difference

  • type -

  • outcome_variable_name -

  • grouping_variable_name -

  • effect -

  • effect_size -

  • LL -

  • UL -

  • SE -

  • df -

  • ta_LL -

  • ta_UL -

  • effect_type -

  • effects_complex -

• es_median_difference

  • type -

  • outcome_variable_name -

  • grouping_variable_name -

  • effect -

  • effect_size -

  • LL -

  • UL -

  • SE -

  • ta_LL -

  • ta_UL -

  • effect_type -

  • effects_complex -

• es_smd

  • outcome_variable_name -

  • grouping_variable_name -

  • effect -

  • effect_size -

  • LL -

  • UL -

  • numerator -

  • denominator -

  • SE -

  • df -

  • d_biased -

  • effect_type -

  • effects_complex -

• overview

  • outcome_variable_name -

  • grouping_variable_name -

  • grouping_variable_level -

  • mean -

  • mean_LL -

  • mean_UL -

  • median -

  • median_LL -

  • median_UL -

  • sd -

  • min -

  • max -

  • q1 -

  • q3 -

  • n -

  • missing -

  • df -

  • mean_SE -

  • median_SE -

• raw_data

  • grouping_variable -

  • outcome_variable -

  • grouping_variable_A -

  • grouping_variable_B -

Arguments

data

For raw data - a data frame or tibble

outcome_variable

For raw data - The column name of the outcome variable, or a vector of numeric data

grouping_variable_A

For raw data - The column name of the grouping variable, or a vector of group names, only 2 levels allowed

grouping_variable_B

For raw data - The column name of the grouping variable, or a vector of group names, only 2 levels allowed

means

For summary data - A vector of 4 means: A1B1, A1B2, A2B1, A2B2

sds

For summary data - A vector of 4 standard deviations, same order

ns

For summary data - A vector of 4 sample sizes

grouping_variable_A_levels

For summary data - An optional vector of 2 group labels

grouping_variable_B_levels

For summary data - An optional vector of 2 group labels

outcome_variable_name

Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed.

grouping_variable_A_name

Optional friendly name for the grouping variable. Defaults to 'A' or the grouping variable column name if a data.frame is passed.

grouping_variable_B_name

Optional friendly name for the grouping variable. Defaults to 'A' or the grouping variable column name if a data.frame is passed.

conf_level

The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95.

assume_equal_variance

Defaults to FALSE

save_raw_data

For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object

Details

Reach for this function in place of a 2x2 between-subjects ANOVA.

Once you generate an estimate with this function, you can visualize it with plot_mdiff() and you can visualize the interaction specifically with plot_interaction(). You can test hypotheses with test_mdiff().

The estimated mean differences are from statpsych::ci.2x2.mean.bs().

The estimated SMDs are from statpsych::ci.2x2.stdmean.bs().

The estimated median differences are from statpsych::ci.2x2.median.bs()

Examples

data("data_videogameaggression")

estimates_from_raw <- esci::estimate_mdiff_2x2_between(
  esci::data_videogameaggression,
  Agression,
  Violence,
  Difficulty
)

# To visualize the estimated mean difference for the interaction
myplot_from_raw <- esci::plot_mdiff(
  estimates_from_raw$interaction,
  effect_size = "median"
)

# To conduct a hypothesis test on the mean difference
res_htest_from_raw <- esci::test_mdiff(
  estimates_from_raw$interaction,
  effect_size = "median"
)


# From summary data
means <- c(1.5, 1.14, 1.38, 2.22)
sds <- c(1.38, .96,1.5, 1.68)
ns <- c(26, 26, 25, 26)
grouping_variable_A_levels <- c("Evening", "Morning")
grouping_variable_B_levels <- c("Sleep", "No Sleep")

estimates_from_summary <- esci::estimate_mdiff_2x2_between(
  means = means,
  sds = sds,
  ns = ns,
  grouping_variable_A_levels = grouping_variable_A_levels,
  grouping_variable_B_levels = grouping_variable_B_levels,
  grouping_variable_A_name = "Testing Time",
  grouping_variable_B_name = "Rest",
  outcome_variable_name = "False Memory Score",
  assume_equal_variance = TRUE
)

# To visualize the estimated mean difference for the interaction
plot_mdiff_interaction <- esci::plot_mdiff(
  estimates_from_summary$interaction,
  effect_size = "mean"
)

# To visualize the interaction as a line plot
plot_interaction_line <- esci::plot_interaction(estimates_from_summary)

# Same but with fan effect representing each simple-effect CI
plot_interaction_line_CI <- esci::plot_interaction(
  estimates_from_summary,
  show_CI = TRUE
)

# To conduct a hypothesis test on the mean difference
res_htest_from_raw <- esci::test_mdiff(
  estimates_from_summary$interaction,
  effect_size = "mean"
)