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

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,
  ...
)

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.

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 (*, **, ***).

Examples

library(volker)
data <- volker::chatgpt

effect_metrics(data, starts_with("cg_adoption_"), adopter)
#> 
#> 
#> |Expectations                             | Eta-squared|  Eta|  n|     F|     p| stars|
#> |:----------------------------------------|-----------:|----:|--:|-----:|-----:|-----:|
#> |ChatGPT has clear advantages compared... |        0.03| 0.18| 97|  1.03| 0.719|      |
#> |Using ChatGPT brings financial benefits. |        0.08| 0.29| 97|  2.88| 0.026|     *|
#> |Using ChatGPT is advantageous in many... |        0.01| 0.09| 97|  0.23| 0.630|      |
#> |Compared to other systems, using Chat... |        0.03| 0.19| 97|  1.12| 0.206|      |
#> |Much can go wrong when using ChatGPT.    |        0.00| 0.07| 97|  0.16| 0.621|      |
#> |There are legal issues with using Cha... |        0.05| 0.21| 97|  1.47| 0.221|      |
#> |The security of user data is not guar... |        0.04| 0.21| 97|  1.37| 0.219|      |
#> |Using ChatGPT could bring personal di... |        0.04| 0.20| 97|  1.36| 0.219|      |
#> |In my environment, using ChatGPT is s... |        0.22| 0.47| 97|  8.96| 0.000|   ***|
#> |Almost everyone in my environment use... |        0.23| 0.48| 97|  9.05| 0.000|   ***|
#> |Not using ChatGPT is considered being... |        0.27| 0.52| 97| 11.63| 0.000|   ***|
#> |Using ChatGPT brings me recognition f... |        0.25| 0.50| 97| 10.13| 0.000|   ***|
#> 
#> 4 missing case(s) omitted. Adjusted significance p values with fdr method.
#>