qmethod
R package to analyse Q methodology data
Would you like to send the author an enquiry about Q methodology? Read this first.qmethod, an R package to analyse Q methodology data
Q is a methodology to study the distinct perspectives existing within a group of people, on a topic of interest. It is used across disciplines. See further about the method in http://qmethod.org/about.
New to R? ▶ Jump directly to the graphical interface.
Overview
This package performs the analysis of Q methodology data. Data can be imported from a range of formats, and results can be explored and exported in multiple ways. See a graphical interface with the basic functionality and an example of what you can do with this package.
The package provides all the options for standard Q analysis, such as different extraction methods (principal components analysis and centroid factor extraction), rotation methods (none or varimax), and both forced and non-forced distributions. Manual flagging can be easily run using R code (see an example). Additional options include different correlation coefficients for the initial correlation matrix (Pearson, Spearman and Kendall) and other mathematical rotations.
A single function runs the full analysis (qmethod()). Each step can also be run separately using the corresponding functions for correlation matrix, automatic flagging, statement scores, distinguishing and consensus statements, and general factor characteristics.
Additional functions are available to import data from raw .CSV, ‘HTMLQ’ and ‘FlashQ’ .CSV files, ‘PQMethod’ .DAT files and ‘easy-htmlq’ .JSON files; to plot and summarise Q results; to import raw data from individual and multilingual .CSV files; to make printable cards; and to perform bootstrapping.
For full details about what you can do, see the package reference manual.
Resources
Here are further links to learn more about the software and about conducting Q methodology (more references in the package reference manual):
- A cookbook of qmethod.
- The package in CRAN, the R repository.
- The package reference manual.
- The latest code of the package is on this Github site.
- An introduction to the package, usage, and validation in Zabala (2014).
- For those not familiar with R, see a simple graphical visual interface to use either online (without installation) or offline.
- Introduction to Q methodology (slides of a graduate course): session 1 and session 2, including a demo of online Q-sorting using HtmlQ.
- Recommendations to report Q studies, for transparency and to facilitate evidence review and reproducibility.
- Suggested best practices in data management for Q studies.
- A video demonstrating the use of “qmethod” to analyse Q sort data (by Dr. Kimberlee Everson)
Development
The package has been created and is maintained by Aiora Zabala, with contributions from Max Held and Frans Hermans. Further contributions are most welcome. To do so, please read the guidelines, post your suggestions on the issue tracker, or email the maintainer.
The package is free and open source. It has been thoroughly tested an validated. If you use it, please cite it in your work.
Pathway of priority developments, should resources allow (also a record of some of the previous work is here):
- Video explaining how to interpret the Q plot #373
- Shiny GUI & usability #375, #374, #369, #366, ~#287~, ~#262~, #217, #195, ~#81~, #23
- Compatibility with online Q-sort collection using HTMLQ and easy-HTMLQ (also FlashQ):
If you find the package and these resources useful, consider supporting maintenance and further enhancements:
