What this is

Collecting validated self-reports is the unglamorous half of an affective-computing pipeline. This started as a small helper for scoring BFI-2 and VAS-F responses in one experiment; it is now a full data-collection application, built around one idea: an instrument is data, not code. Items, response ranges, subscale membership and reverse keys are declared as values, and a single vectorised scorer turns responses into scores without knowing which questionnaire it is holding. Adding an instrument adds no arithmetic.

Instruments

Instrument	Items	Scores
BFI-2 — Big Five Inventory-2	60	5 domains, 15 facets
BFI-2-XS — Extra-Short Form	15	5 domains
BFI-10	10	5 domains
PANAS — Positive and Negative Affect Schedule	20	Positive / Negative Affect
VAS-F — Visual Analogue Scale for Fatigue	18	Fatigue, Energy, composite

The application

Beyond the Python API and CLI, pq ui serves a local data-collection application with five tabs: an overview, participant setup, the questionnaire itself — rendered as the authors published it, radio buttons with the labelled response scale, sliders for visual-analogue items — the computed scores, and every record collected so far with filters and export. It binds to 127.0.0.1 and makes no outbound request: participant self-reports are consent-restricted, and a tool that cannot be reached from another machine is the honest default for that kind of data. Two browser tabs are two independent participants, so one machine runs two stations side by side.

Try it

pip install "personality_questionnaire[ui]"
pq ui                      # http://127.0.0.1:8080

Or from the command line directly:

pq list                                  # what is available
pq info bfi2                             # items, subscales, citation
pq run bfi2 --participant P01            # ask, score, and store
pq score bfi2 --input answers.csv        # score a file
pq export --shape long --output study-a.csv