effect_counts_items: Test homogeneity of category shares for multiple items

In volker: High-Level Functions for Tabulating, Charting and Reporting Survey Data

Test homogeneity of category shares for multiple items

Description

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"))

volker documentation built on Nov. 5, 2025, 5:21 p.m.