Compare groups for each item by calculating F-statistics and effect sizes
Source:R/effects.R
effect_metrics_items_grouped.Rd
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
.
Arguments
- data
A tibble containing item measures.
- cols
Tidyselect item variables (e.g. starts_with...).
- cross
The column holding groups to compare.
- 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
library(volker)
data <- volker::chatgpt
effect_metrics(data, starts_with("cg_adoption_"), adopter)
#>
#>
#> |Expectations | F| p| stars| Eta| Eta squared|
#> |:-----------------------------------------------------------|----:|---:|-----:|---:|-----------:|
#> |ChatGPT has clear advantages compared to similar offerings. | 1.0| 0.7| | 0.2| 0.0|
#> |Using ChatGPT brings financial benefits. | 2.9| 0.0| *| 0.3| 0.1|
#> |Using ChatGPT is advantageous in many tasks. | 0.2| 0.6| | 0.1| 0.0|
#> |Compared to other systems, using ChatGPT is more fun. | 1.1| 0.1| | 0.2| 0.0|
#> |Much can go wrong when using ChatGPT. | 0.2| 0.5| | 0.1| 0.0|
#> |There are legal issues with using ChatGPT. | 1.5| 0.2| | 0.2| 0.0|
#> |The security of user data is not guaranteed with ChatGPT. | 1.4| 0.1| | 0.2| 0.0|
#> |Using ChatGPT could bring personal disadvantages. | 1.4| 0.1| | 0.2| 0.0|
#> |In my environment, using ChatGPT is standard. | 9.0| 0.0| ***| 0.5| 0.2|
#> |Almost everyone in my environment uses ChatGPT. | 9.1| 0.0| ***| 0.5| 0.2|
#> |Not using ChatGPT is considered being an outsider. | 11.6| 0.0| ***| 0.5| 0.3|
#> |Using ChatGPT brings me recognition from my environment. | 10.1| 0.0| ***| 0.5| 0.2|
#>
#> 4 missing case(s) omitted.
#>