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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 by calling:
install.packages("IPV")And the development version from GitHub by calling:
# install.packages("devtools")
devtools::install_github("NilsPetras/IPV")This is an example how charts can be created:
library(IPV)
# Here, a toy example provided in the package is used.
# ?HEXACO
# estimate the underlying model from (clean) raw data
x <- ipv_est(HEXACO[ ,c(2:41, 122:161)], "HA")
#> Negative center distance adjusted to 0
#> Negative center distance adjusted to 0
#> Negative center distance adjusted to 0
# create a nested chart (one of three available chart types)
nested_chart(x$est)
#> Facet circle radius set to 0.204 based on the data.
#> cor_spacing set to 0.23 based on the data.
#> Relative scaling set to 2.1 based on the data.
#> Axis tick set to 0.2 based on the data.
#> dist_construct_label set to 0.333 based on the data.
# the next step would be to customize the appearanceFor further introduction, please check out the vignette.
browseVignettes("IPV")
#> No vignettes found by browseVignettes("IPV")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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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