The Q Approach to Consensus Building

Implements a workflow based on Q method to support consensus-building processes. It prepares participant rankings, selects and fits group perspectives, calculates consensus priority scores, validates results by bootstrap resampling, and produces publication-ready figures. The underlying method is described by Geschke et al. (2022) "The Q approach to consensus building: integrating diverse perspectives to guide decision-making" .

qapproach

qapproach implements a workflow to support consensus building based on Q method. It prepares participant-by-statement rankings, identifies group perspectives, computes consensus priority scores (cp-scores), validates results by bootstrap resampling, and creates figures.

Installation

# install.packages("qapproach") # after CRAN publication
# remotes::install_github("jgeschke/qapproach")

Basic workflow

library(qapproach)

raw <- read.csv2("test_data/TCA_strategies.csv")
rankings <- prepare_rankings(raw)
consensusal_priorities <- qapproach(rankings)
consensusal_priorities[["cp-scores"]]

cp-scores use a fixed standard-normal cumulative-probability scale. A value of 0.5 represents neutral prioritization across all group perspectives; higher and lower values represent relatively higher and lower priority. Cross-analysis comparisons require the same statements and meanings, ranking distribution, instructions, data preparation, and analytical settings.

For specialist inspection, the barplot can optionally overlay the weighted z-scores after rescaling them to the observed cp-score range:

plot_barplot(
  consensusal_priorities,
  show_normalized_weighted_z = TRUE,
  normalized_line_color = "black",
  normalized_line_width = 1.5
)

See vignette("qapproach") for analysis, validation, and visualization examples.