cohen_d_borenstein function

Calculate Cohen's d as illustrated by Borenstein et al. (2009, ISBN: 978-0-470-05724-7)

Calculates Cohen's d, its standard error, and confidence interval, as illustrated in the Borenstein et al. (2009, ISBN: 978-0-470-05724-7).

cohen_d_borenstein(
  sample_1 = NULL,
  sample_2 = NULL,
  data = NULL,
  iv_name = NULL,
  dv_name = NULL,
  direction = "2_minus_1",
  ci_range = 0.95,
  output_type = "all",
  initial_value = 0
)

Arguments

• sample_1: a vector of values in the first of two samples
• sample_2: a vector of values in the second of two samples
• data: a data object (a data frame or a data.table)
• iv_name: name of the independent variable
• dv_name: name of the dependent variable
• direction: If direction == "2_minus_1", Cohen's d will reflect the extent to which the mean of IV level 2 is greater than the mean of IV level 2. If direction == "1_minus_2", Cohen's d will reflect the extent to which the mean of IV level 1 is greater than the mean of IV level 2. By default, direction == "2_minus_1".
• ci_range: range of the confidence interval for Cohen's d (default = 0.95)
• output_type: If output_type == "all" or if output_type == "d_var_se_and_ci", the output will be a vector of Cohen's d and its variance, SE, and confidence interval. If output_type == "d_se_and_ci", the output will be a vector of Cohen's d and its SE and confidence interval. If output_type == "d_and_ci", the output will be a vector of Cohen's d and its confidence interval. If output_type == "d", the output will be Cohen's d. If output_type == "ci", the output will be a vector of the confidence interval around Cohen's d. If output_type == "se", the output will be the standard error of Cohen's d. By default, output_type == "all".
• initial_value: initial value of the noncentrality parameter for optimization (default = 0). Adjust this value if confidence interval results look strange.

Examples

cohen_d_borenstein(sample_1 = 1:10, sample_2 = 3:12)
cohen_d_borenstein(
data = mtcars, iv_name = "vs", dv_name = "mpg", ci_range = 0.99)
sample_dt <- data.table::data.table(iris)[Species != "setosa"]
cohen_d_borenstein(
data = sample_dt, iv_name = "Species", dv_name = "Petal.Width",
initial_value = 10)