Effect size and test for comparing multiple variables by a grouping variable

Usage

effect_counts_items_grouped(
  data,
  cols,
  cross,
  method = "cramer",
  adjust = "fdr",
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

data: A tibble containing item measures and grouping variable.
cols: Tidyselect item variables (e.g. starts_with...).
cross: The column holding groups to compare.
method: The output metrics, currently only cramer is supported.
adjust: Performing multiple significance tests inflates the alpha error. Thus, p values need to be adjusted according to the number of tests. Set a method supported by stats::p.adjust, e.g. "fdr" (the default) or "bonferroni". Disable adjustment with FALSE.
labels: If TRUE (default) extracts labels from the attributes, see codebook.
clean: Prepare data by data_clean.
...: Placeholder to allow calling the method with unused parameters from effect_counts.

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

effect_counts_items_grouped(
  data, starts_with("cg_adoption_adv"),  sd_gender
)
#> 
#> 
#> |Expectations: Correlation w...           | Cramer's V| Chi-squared|  n| df|     p| stars|
#> |:----------------------------------------|----------:|-----------:|--:|--:|-----:|-----:|
#> |ChatGPT has clear advantages compared... |       0.14|        3.76| 99|   | 0.851|      |
#> |Using ChatGPT brings financial benefits. |       0.16|        4.99| 99|   | 0.851|      |
#> |Using ChatGPT is advantageous in many... |       0.14|        3.78| 99|   | 0.851|      |
#> |Compared to other systems, using Chat... |       0.13|        3.44| 99|   | 0.851|      |
#> 
#> n=99. 2 missing case(s) omitted. Adjusted significance p values with fdr method.
#>