PeterKol/jobcannon-entertainment-responses

JobCannon Entertainment Quiz Response Dataset

v1. 77,284 item-level responses to five for-fun quizzes, in 24 languages.

Anonymized, item-level answers to five entertainment quizzes taken by real visitors on JobCannon between March and August 2026. One row is one completed quiz: the raw per-item answers, the category totals the site computed from them, and the result the taker was shown.

We collected all of it on our own traffic. It is not a repackaging of somebody else's file.

Read this part before you use it

These five quizzes are not psychometric instruments. Nobody validated them. There is no norm sample behind them and no reliability coefficient, and they were never built to measure any particular construct. "Spirit animal" is not a trait. The result a taker sees is the largest of a handful of hand-authored category counters, and those categories were picked because they make a fun result page.

So please don't read a mean here as a population estimate. And there is no published norm for any of the five, so there is nothing to compare these numbers against.

The useful thing in the file is the behaviour around the answers: how people respond to forced-choice items in twenty-four languages, how long they take, and how answer patterns and result shares move between language audiences looking at identical items. The sample is big enough and clean enough for that kind of question.

If you want scored instruments with published norms behind them, that is a separate release: the JobCannon Psychometric Response Dataset, nine instruments, no overlap with this one. Both come out of the same generator, under the same privacy rules and the same suppression thresholds, and we kept the two test lists apart on purpose, so any given row appears in exactly one of them.

Files

Item-level, one row per completed quiz

config	quiz	items	result categories	median time	responses
jungian_archetype	Jungian archetype	24	12	5m 17s	31,081
spirit_animal	Spirit animal	12	8	2m 09s	17,520
mental_age	Mental age	12	5	2m 02s	10,985
aura_color	Aura colour	10	7	1m 52s	10,929
past_life	Past life era	12	8	2m 27s	6,769
					77,284

Columns: response_id, locale, year_month, duration_seconds, top_result, score_<category> ..., q1 ... qN.

Item wording is not distributed. The q columns hold answer values only.

The score columns work one way for four of the quizzes and a different way for mental_age. The four carry one score_<category> per possible result, and top_result is whichever of them came out largest. mental_age instead has a single score_percentage, and its top_result is a band (teen, young_adult, mature, wise, elder) rather than an age in years.

Aggregates, by quiz × language

config	rows	what it holds
coverage	126	n, item count, distinct results and median duration for every quiz × locale cell, plus a publishable flag (n ≥ 100)
result_distribution_by_locale	233	share of each result category, per quiz per language
dimension_means_by_locale	290	mean of each category score, per quiz per language

Small cells are suppressed. A language cell needs n ≥ 100 to appear at all, and inside a cell any result category with n < 30 gets folded into other_below_30. 37 of the 121 quiz × language cells clear that floor. The other 84 are in the file with publishable=false and no distribution rows, so you can see they exist without anyone computing a share off eleven people.

Every table also carries an ALL pseudo-locale row, which is the same statistic over the whole quiz with every language pooled.

_manifest.json is not a config. It records, per quiz, how many raw rows were pulled, how many survived the item-count filter, and the resulting column count, so you can check the numbers quoted on this page against the build that produced the files.

Language coverage

Responses per language, all five quizzes combined:

en 35,541	ja 26,769	ar 5,885	es 2,541	de 927	fr 769
pt 717	ua 543	id 343	ru 305	ko 201	it 197
he 171	tr 129	zh 126	pl 114	th 107	nl 92
sv 92	vi 68	nb 40	fa 39	uz 31	hi 30

A further 1,507 rows arrived without a locale and are labelled unknown. Treat those as missing values. We kept them in a bucket of their own so that nobody quietly counts them as English.

English is 46.0% of the rows, so most of this file is in some other language: 41,743 rows, of which 40,236 carry a named language. For a lot of uses that half is the reason to download it at all. Japanese alone is 26,769 rows, around eight times the Japanese sample in our psychometric release.

The Japanese mass is bunched into one quiz, which limits what you can do with it. 22,223 of the 26,769 Japanese rows sit in the Jungian archetype quiz, where Japanese takers are 71.5% of everyone who finished. Arabic behaves differently: 3,098 of its 5,885 rows are mental_age, and there Arabic, Japanese and English land within four points of each other at 28.2%, 29.7% and 26.6%. past_life and aura_color go the other way, at 86% and 78% English. Check the coverage table for the cell you care about before you assume a language is represented in the quiz you are looking at.

Twelve labels reach n ≥ 100 somewhere, so those are the ones that show up in the aggregate tables: ar de en es fr id ja ko pt ru ua and the unknown bucket.

Privacy

No personal data is in these files, and none is read in the first place. The build selects exactly six columns (locale, answers, scores, top_result, duration_seconds, created_at). Everything identifying on the source table is therefore out of reach of the output even if somebody later changes the writer: user id, anonymous id, participant name and email, referrer, entry host and path, UTM parameters, cohort id and organization id.

Two more reductions on top of that. response_id is a per-file sequential integer instead of the database id, and the timestamp is cut back to year_month, so no row can be joined to a session by its time.

Method

• Rows are filtered to each quiz's current item count. A response whose answer array is any other length is either a legacy version of the quiz or an incomplete one, and it gets dropped: 2,649 of 79,933 raw rows, 3.3%.
• score_* columns hold the category totals exactly as the live site computed them. Nothing was recomputed for the release.
• duration_seconds is blank where the recorded value was ≤ 0 or ≥ 7,200 s. Roughly 2% of rows are blank on that column.
• Nothing is weighted, balanced or down-sampled. Language shares are traffic shares, and traffic follows wherever the quiz got shared.

Citation

@dataset{jobcannon_entertainment_2026,
  title  = {JobCannon Entertainment Quiz Response Dataset},
  author = {Kolomiets, Peter},
  year   = {2026},
  version = {1.0},
  publisher = {JobCannon},
  doi    = {10.5281/zenodo.21950814},
  url    = {https://doi.org/10.5281/zenodo.21950814}
}

License

CC-BY-4.0. If you publish anything off it, cite the DOI above.