effect_metrics_items_cor_items: Output correlation coefficients for multiple items

Description

The correlation is calculated using stats::cor.test.

Usage

effect_metrics_items_cor_items(
  data,
  cols,
  cross,
  method = "pearson",
  labels = TRUE,
  clean = TRUE,
  ...
)

Value

A volker table containing correlations.

If method = "pearson":

R-squared: Coefficient of determination.
n: Number of cases the calculation is based on.
Pearson's r: Correlation coefficient.
ci low / ci high: Lower and upper bounds of the 95% confidence interval.
df: Degrees of freedom.
t: t-statistic.
p: p-value for the statistical test, indicating whether the correlation differs from zero.
stars: Significance stars based on the p-value (*, **, ***).

If method = "spearman":

Spearman's rho is displayed instead of Pearson's r.
S-statistic is used instead of the t-statistic.

Arguments

data: A tibble containing item measures.
cols: Tidyselect item variables (e.g. starts_with...).
cross: Tidyselect item variables (e.g. starts_with...).
method: The output metrics, pearson = Pearson's R, spearman = Spearman's rho.
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_items_cor_items(
  data,
  starts_with("cg_adoption_adv"),
  starts_with("use"),
  metric = TRUE
)

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