GQR: Generalised Q Analysis in R for Computational Analysis of Multidimensional Preferences

Q methodology identifies shared patterns in how respondents evaluate sets of statements, but analysis is limited to a few respondents whose number should be lower than that of statements. Moreover, the process can become cumbersome when respondents rank many statements and difficult to reproduce when methodological choices span data preparation, component extraction and interpretation. Generalised Q (GQ) analysis addresses these limitations by constructing synthetic combined statements from smaller sets of constituent statements. Here, we introduce GQR (Generalised Q Analysis in R), an open-source R package implementing the GQ workflow through both a programmatic interface and an interactive Shiny application.GQR supports unrestricted full binary, one-per-group, and random-pattern designs, data transformation and respondent filtering, construction of synthetic evaluation matrices, ordinary and SPSS-compatible principal component analysis (PCA), and regression-based interpretation of both component content and respondent associations. For large ordinary-PCA problems, GQR provides an exact compact method that avoids materialising the synthetic evaluation matrix. The Shiny application uses the same analytical core and can export executable R code recording the current analysis and settings.Computational benchmarks showed that compact PCA reproduced materialised-matrix solutions to numerical precision while reducing runtime and memory requirements by orders of magnitude as the number of synthetic statements and respondents increased. Using the built-in Central and Eastern European gardening dataset, we further demonstrate how GQ analysis can identify multidimensional response patterns and relate them to respondent and garden characteristics. GQR therefore provides an accessible, reproducible and computationally efficient implementation of GQ analysis for multidimensional preference and survey data.