Test homogeneity of category shares for multiple items

Performs a goodness-of-fit test and calculates the Gini coefficient for each item. The goodness-of-fit-test is calculated using stats::chisq.test.

Usage

effect_counts_items(
  data,
  cols,
  adjust = "fdr",
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

data: A tibble containing item measures.
cols: Tidyselect item variables (e.g. starts_with...).
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 with the following statistical measures:

Gini coefficient: Gini coefficient, measuring inequality.
n: Number of cases the calculation is based on.
Chi-squared: Chi-Squared test statistic.
p: p-value for the statistical test.
stars: Significance stars based on p-value (*, **, ***).

Examples

library(volker)
data <- volker::chatgpt

effect_counts_items(data, starts_with("cg_adoption_adv"))
#> 
#> 
#> |Expectations                             | Gini coefficient|  n| Chi-squared|     p| stars|
#> |:----------------------------------------|----------------:|--:|-----------:|-----:|-----:|
#> |ChatGPT has clear advantages compared... |             0.36| 99|       43.47| 0.000|   ***|
#> |Using ChatGPT brings financial benefits. |             0.19| 99|       14.28| 0.006|    **|
#> |Using ChatGPT is advantageous in many... |             0.36| 99|       47.01| 0.000|   ***|
#> |Compared to other systems, using Chat... |             0.40| 99|       53.68| 0.000|   ***|
#> 
#> 2 missing case(s) omitted. Adjusted significance p values with fdr method.
#>