effect_metrics_items: Test whether a distribution is normal for each item

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

Test whether a distribution is normal for each item

Description

The test is calculated using stats::shapiro.test.

Usage

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

Arguments

data	A tibble containing item measures.
cols	The column holding metric values.
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_metrics.

Value

A volker table containing itemwise statistics:

• skewness: Measure of asymmetry in the distribution. A value of 0 indicates perfect symmetry.

• kurtosis: Measure of the "tailedness" of the distribution.

• W: W-statistic from the Shapiro-Wilk normality test.

• p: p-value for the statistical test.

• stars: Significance stars based on p-value (*, **, ***).

• normality: Interpretation of normality based on Shapiro-Wilk test.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics_items(data, starts_with("cg_adoption"))

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