As Q methodology matures, Q scholars have witnessed the development of tools for Q sorting and factor analyzing Q sorts. Regarding concourse statements, however, some scholars failed even to mention how they constructed the concourse in their research. Gleaning concourse statements and sampling from the statements requires persistent efforts from Q researchers. Software for these tasks could lead to more effective Q studies. To expand Q as a tool for solving urgent problems in society or as a pedagogical tool in the classroom, Q researchers need to implement their Q studies with swiftness. Researchers may need a tool to accelerate the entire process. This article describes MarginNote 3 for Mac users, QDAMiner Lite for PC users, Dedoose for web users, and Weava extension for Google Chrome web browser users. These tools, including iThoughtsX, quickly categorize statements into themes. Q researchers are able to tap text mining algorithms like LDA (Latent Dirichlet Allocation) and R's widyr package thus quickly revealing aspects of a topic that may have been neglected or missed and retrieve statements that represent those same aspects.

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doi.org/10.15133/j.os.2019.010
Operant Subjectivity

Byung Lee. (2020). Tools for Collecting a Concourse and Selecting a Q Sample. Operant Subjectivity, 41(1), 17–47. doi:10.15133/j.os.2019.010