finding_d: Finding the effect size of equivalence

In samueltobler/behavdata: Behavioral Data Preprocessing and Analysis

Finding the effect size of equivalence

Description

This functions allows finding the lowest Cohen's d value at which the two groups of interest are statistically significantly equal. This function is an extension of the TOSTtwo function of the TOSTER plugin. Please cite Tobler, S. (2022) and Lakens, D. (2017) (see below).

Usage

finding_d(
  m1, 
  m2, 
  sd1, 
  sd2, 
  n1, 
  n2, 
  alpha = 0.05, 
  var.equal = FALSE,
  steps = 0.01, 
  dmax = 1)

Arguments

m1	Mean of group 1 of interest.
m2	Mean of group 2 of interest.
sd1	Standard deviation of group 1.
sd2	Standard deviation of group 2.
n1	Sample size of group 1.
n2	Sample size of group 2.
alpha	Alpha value of significance. Default is alpha = 0.05.
var.equal	Equal variance of data. Default is FALSE.
steps	Fragmentation of Cohen's d. Smaller values lead to a more fine-grained analysis. Default steps = 0.01.
dmax	Maximal Cohen's d value of investigation. Default is dmax = 1.

References

Tobler, S. (2022). Finding equivalence: a novel tool to investigate the effect size at which two groups are statistically equivalent. Proceedings of the 7th Annual Learning Sciences Graduate Student Conference (LSGSC 2022).

Lakens, D. (2017). Equivalence tests: A practical primer for t-tests, correlations, and meta-analyses. Social Psychological and Personality Science, 8(4), 355-362. doi:10.1177/1948550617697177

Examples

finding_d(m1 = 4.4, m2 = 4.4, sd1 = 0.1, sd2 = 0.1, n1 = 25, n2 = 25)

samueltobler/behavdata documentation built on July 9, 2024, 11:33 a.m.