Interpretive Clustering

This package implements the Interpretive Clustering (IC) method as described in Burr, King, and Heckmann (2020). An introduction to the software is available on YouTube. Interpretive Clustering is a variant of construct clustering for repertory grid data. While derived from theoretical considerations based on Personal Construct Theory, the procedure itself is mathematically equivalent to a problem from graph theory called maximal cliques enumeration. Given a similarity measure, in our case the number of matching scores between two constructs, a network graph of relatedness between constructs is construed. A clique is a group of constructs which are all mutually related, given some cut-off criterion for relatedness (e.g. 6 matching scores in a grid with 7 elements). While an offline approach is also described to find the construct cliques, this software automates the process. Below you see the resulting cliques for Sylvia's sample grid. In the paper, the interpretation of the discovered cliques is discussed in detail.

Statement of need

Currently, the IC method is not implemented in any other existing repertory grid software. While IC can also be conducted by hand, this is very time consuming, error-prone and only feasible for small-sized grids. Hence, a software solution to support the IC procedure is needed. Without proper software support which facilitates the process, the IC method is likely to become a methodological contribution which will rarely be used in research due to above mentioned reasons.

Installation

You can install the latest official release version from CRAN

install.packages("OpenRepGrid.ic")

or the development version from GitHub.

# install.packages("devtools")
devtools::install_github("markheckmann/OpenRepGrid.ic")

Then type the following to start the shiny app.

library(OpenRepGrid.ic)
ic()

Datasets

You can find the datasets used in our publication on Zenodo.

Contributing

In order to maximize the package's usefulness for the research community, we welcome participation in the package's development. Experienced R programmers are asked to make pull requests to the OpenRepGrid.ic github repository, report issues, or commit code snippets. Non-technical oriented researchers are invited to send us feature requests or suggestions for improvement.