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

Usage

effect_counts_items_grouped(
  data,
  cols,
  cross,
  method = "cramer",
  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.
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 with Gender                       | Cramer's V| Chi-squared|  n| df|     p| stars|
#> |:-----------------------------------------------------------|----------:|-----------:|--:|--:|-----:|-----:|
#> |ChatGPT has clear advantages compared to similar offerings. |       0.14|        3.76| 99|   | 0.829|      |
#> |Using ChatGPT brings financial benefits.                    |       0.16|        4.99| 99|   | 0.813|      |
#> |Using ChatGPT is advantageous in many tasks.                |       0.14|        3.78| 99|   | 0.830|      |
#> |Compared to other systems, using ChatGPT is more fun.       |       0.13|        3.44| 99|   | 0.865|      |
#> 
#> 2 missing case(s) omitted.
#>