effect_metrics_items_grouped: Compare groups for each item by calculating F-statistics and effect sizes

Description

The models are fitted using stats::lm. ANOVA of type II is computed for each fitted model using car::Anova. Eta Squared is calculated for each ANOVA result using effectsize::eta_squared.

Usage

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

Value

A volker tibble with the following statistical measures:

Eta-squared: Effect size indicating the proportion of variance in the dependent variable explained by the predictor.
Eta: Root of Eta-squared, a standardized effect size.
n: Number of cases the calculation is based on.
F: F-statistic from the linear model.
p: p-value for the statistical test.
stars: Significance stars based on p-value (*, **, ***).

Arguments

data: A tibble containing item measures.
cols: Tidyselect item variables (e.g. starts_with...).
cross: The column holding groups to compare.
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.

Examples

Run this code

library(volker)
data <- volker::chatgpt

effect_metrics(data, starts_with("cg_adoption_"), adopter)

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