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.
Arguments
- data
A tibble containing item measures.
- cols
Tidyselect item variables (e.g. starts_with...).
- 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 to similar offerings. | 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 tasks. | 0.36| 99| 47.01| 0.000| ***|
#> |Compared to other systems, using ChatGPT is more fun. | 0.40| 99| 53.68| 0.000| ***|
#>
#> 2 missing case(s) omitted.
#>