The choice and mastery of Q-specific software can be an important factor for a successful Q study.
Q methodology includes some mathematic operations not easily accomplished with generic statistical software, and Q practitioners require custom interfaces to guide their analyses and interpretation.
Happily, there has recently been a lot of new software released to the Q community, often generously as free and open source code.
My plan is that we first have a relatively short introduction of the different pieces of Q software, ideally by their creators/maintainers, and then split up into small groups, where I and my fellow developers help participants set up and use their desired software.
The workshop would take place on September 7, probably in one of the hotel’s conference rooms (details are TBA). I’m guessing we’ll have a projector, and, if we’re lucky, WiFi. Participants would have to pay a modest additional fee to cover the costs for the room etc. People would bring their own computers, and perhaps, datasets.
The following software will be presented in greater detail (because those are the developers who have agreed to attend):
qmethod and other Q-software for the R project
qmethod, created and first released by Aiora Zabala (University of Cambridge, UK) in 2014 is a free and open source (FOSS) software package for the R project for statistical computing. It offers most of the features of established Q software, has been validated against PQMethod and provides some advanced visualisations and analysis. It allows Q researchers to tap into the rich eco system of the R project, the de-facto standard of statistics and (much of) data science.
Maximilian Held (that’s me) is now extending qmethod and other R packages for Q methodology with additional features developed as part of Sabine Pfeiffer‘s Labouratory at the University of Hohenheim (Stuttgart, Germany). Some of these new features will also be highlighted.
For now at least, R and qmethod have a somewhat steeper learning curve than other tools, and they carry some software requirements. If you want to test drive R, the following free downloads and preparations are recommended:
- The current version of R for your platform
- The free and open source variant of RStudio Desktop, a helpful integrated development environment (IDE) for R.
- Frequently used R packages (here’s a list); you can install each of them by calling `install.packages(“package_name”)` on the console, or clicking the requisite button in RStudio’s packages pane.
- Install my (Max Held’s) fork of
qmethod, which you can find here, with instructions here.
- Prepare and bring your data, ideally somewhere along these lines (optional).
- Uh, and don’t forget your computer.
If you get stuck installing any of this, don’t fret, I’ll be happy to help in New Orleans.
A user fills out “introduction statements” and “consent form” in text format, which will be displayed on the first Q sorting webpage. Questions about demographic or psychographic data should be filled in xml format. This second form page can handle check boxes, multiple-choice questions, and essay type questions. Q statements should be saved in text format while images for sorting should be put in a folder for display.
Pictures and/or statements are presented as big items in the beginning for inspection of sorters. Once they are dragged toward sorting boxes, they will be shrunken.
At the end, the sorting results can be emailed or sent to a Mysql database (it is being revamped). This program uses Flash and actionscript 3.
Ken-Q Analysis is a web application for analyzing Q methodology data. It
works with most modern web browsers on Windows, Mac or Linux operating
systems. It provides a variety of options to enter data into the
application, including manual input and upload of Microsoft Excel files.
The user can extract either centroid factors or principal components for
analysis. Both judgemental rotation and varimax rotation are available
for use. Analysis results can be downloaded in Microsoft Excel format.
Ken-Q Analysis is currently in Beta testing and the current version is
available at shawnbanasick.github.io/ken-q-analysis.
Ken-Q analysis is developed by Shawn Banasick at Kobe College, Department of English.
PCQ is commercial and proprietary software by Mike Stricklin, a program designed specifically for analyzing data according to Q Technique. With it you have a complete package to help you complete a Q Study efficiently.