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Output correlation coefficients for multiple items

Source: R/effects.R
effect_metrics_items_cor_items.Rd

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

Usage

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

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.

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 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.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics_items_cor_items(
  data,
  starts_with("cg_adoption_adv"),
  starts_with("use"),
  metric = TRUE
)
#> 
#> 
#> |Expectations                             |                   Usage| Pearson's r| R-squared|  n| ci low| ci high| df|    t|     p| stars|
#> |:----------------------------------------|-----------------------:|-----------:|---------:|--:|------:|-------:|--:|----:|-----:|-----:|
#> |ChatGPT has clear advantages compared... |      in private context|        0.50|      0.25| 99|   0.33|    0.63| 97| 5.61| 0.000|   ***|
#> |ChatGPT has clear advantages compared... | in professional context|        0.27|      0.07| 99|   0.07|    0.44| 97| 2.73| 0.009|    **|
#> |Using ChatGPT brings financial benefits. |      in private context|        0.17|      0.03| 99|  -0.03|    0.36| 97| 1.73| 0.087|     .|
#> |Using ChatGPT brings financial benefits. | in professional context|        0.53|      0.28| 99|   0.37|    0.66| 97| 6.19| 0.000|   ***|
#> |Using ChatGPT is advantageous in many... |      in private context|        0.34|      0.12| 99|   0.16|    0.51| 97| 3.59| 0.001|   ***|
#> |Using ChatGPT is advantageous in many... | in professional context|        0.35|      0.12| 99|   0.17|    0.51| 97| 3.71| 0.001|   ***|
#> |Compared to other systems, using Chat... |      in private context|        0.47|      0.22| 99|   0.30|    0.61| 97| 5.28| 0.000|   ***|
#> |Compared to other systems, using Chat... | in professional context|        0.27|      0.07| 99|   0.07|    0.44| 97| 2.73| 0.009|    **|
#> 
#> n=99. 2 missing case(s) omitted. Adjusted significance p values with fdr method.
#>