The aim of IPV is to handily create item pool visualizations, as introduced in: Dantlgraber, M., Stieger, S., & Reips, U. D. (2019). Introducing Item Pool Visualization: A method for investigation of concepts in self-reports and psychometric tests. Methodological Innovations, 12(3), 2059799119884283.
You can install the released version of IPV from CRAN with:
And the development version from GitHub with:
This is an example how charts can be created:
library(IPV)
# read model estimates from excel files (manual input is also facilitated)
# Here, a toy example provided in the package is used.
# ?self_confidence
global <- system.file("extdata", "IPV_global.xlsx", package = "IPV", mustWork = TRUE)
tests <- c(system.file("extdata", "IPV_DSSEI.xlsx", package = "IPV", mustWork = TRUE),
system.file("extdata", "IPV_SMTQ.xlsx", package = "IPV", mustWork = TRUE),
system.file("extdata", "IPV_RSES.xlsx", package = "IPV", mustWork = TRUE))
x <- input_excel(global = global, tests = tests)
#> New names:
#> * `` -> ...1
#> Negative center distance adjusted to 0
#> New names:
#> * `` -> ...1
#> Negative center distance adjusted to 0
#> New names:
#> * `` -> ...1
#> Negative center distance adjusted to 0
#> New names:
#> * `` -> ...1
# create a nested chart (one of three available chart types)
nested_chart(x)
#> Facet circle radius set to 0.23 based on the data.
#> cor_spacing set to 0.224 based on the data.
#> Relative scaling set to 4.06 based on the data.
#> Axis tick set to 0.1 based on the data.
#> dist_construct_label set to 0.5 based on the data.
For further introduction, please check out the vignette.
When using item pool visualization, please cite:
Dantlgraber, M., Stieger, S., & Reips, U. D. (2019). Introducing Item Pool Visualization: A method for investigation of concepts in self-reports and psychometric tests. Methodological Innovations, 12(3), 2059799119884283.