Demographics¶

The four demographic fields the quiz collects.

Decision: ⚪ idea

Context¶

The quiz asks four demographic questions, as dropdowns with fixed option lists:

Age - banded, not a birth date.
Gender - fixed list.
Settlement size - village through to a city over 500k, the closest thing to an urban/rural axis.
Education - highest level completed.

How they behave:

Optional - every field is skippable, and a skipped field is missing data, not a guessed one.
Fixed lists, no free text - answers stay comparable across quizzes and years, and nobody can type anything identifying.
Asked once - stored on the account and reused, so a returning user is not asked again on every quiz.
Shown near the end - they sit with the post-survey, after the questions the user actually came for.

The fields travel with the answers into exports and into the data module, which decides what can honestly be said with them.

Cross-tabs are where the value is - views by age, by city size, by education is the analysis nobody else can run on this scale in Poland.
Four fields is the minimum that works - enough to segment on, few enough to not wreck the funnel.
Collected once, used everywhere - the same fields cover every quiz and every post-survey, so answers pool into one dataset.
Consistency is what makes history usable - stable fields mean answers collected years apart can still be compared.

Every field costs completions - four dropdowns before the payoff is real friction, and the payoff is the reason people came.
Demographics narrow anonymity - age band, gender, town size and education next to a political result is close to identifying in a small community - see exports.
Self-declared and unverifiable - people misreport, and our audience skews young regardless.
Option lists age badly - changing education levels or gender options later breaks comparability with everything collected before.