README.md

---
license: cc-by-4.0
language:
- en
pretty_name: Quant Data Example
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
- tabular-regression
tags:
- survey
- consumer-research
- tourism
- segmentation
- psychographics
- self-reported
configs:
- config_name: responses
  default: true
  data_files: data/responses.parquet
- config_name: questions
  data_files: data/questions.parquet
- config_name: everything
  data_files: data/everything.parquet
- config_name: wide
  data_files: data/respondents.parquet
---

# Quant Data Example

De-identified responses from a structured US consumer survey on **travel planning, multi-attraction pass preferences, lifestyle interests, aesthetic taste, values and demographics**.
2,007 respondents, 394 survey variables (33 multi-item blocks and 36 single-choice variables), fielded 2022-03-22 to 2022-03-28.

- In `responses` and `wide`, one row = one respondent; in `everything`, one row = one answer. Every file carries the same opaque `respondent_id` (`r0001`...), assigned after a seeded shuffle; it is not derived from any source identifier.
- Most variables are categorical choices or 'select all that apply' items, so the data suits segmentation, preference modelling and propensity experiments, and demonstrating survey analytics without personal data.
- Self-reported panel data from one week, US only. No behavioural outcomes are observed.

## Quick start

```python
from datasets import load_dataset
responses = load_dataset("odyn-network/quant-data-example", "responses", split="train")  # nested: section > block > answers, 1 row per respondent
questions = load_dataset("odyn-network/quant-data-example", "questions", split="train")  # tree as a flat table: node_type, parent_id
everything = load_dataset("odyn-network/quant-data-example", "everything", split="train")  # flat: 1 row per answered item
wide = load_dataset("odyn-network/quant-data-example", "wide", split="train")  # 1 row per respondent, for modelling
wide.to_pandas()["age_band"].value_counts()
```

```python
import pandas as pd
df = pd.read_parquet("hf://datasets/odyn-network/quant-data-example/data/respondents.parquet")
```

## Files

| File | What it is |
|---|---|
| `data/responses.parquet` / `data/responses.jsonl` | **Start here.** One nested record per respondent mirroring the question hierarchy: `sections > blocks > answers`. Each block carries the page and this respondent's seconds on it; each answer carries the question id, item label (omitted when it equals the response) and response. The Parquet is what the Hub loads and shows as nested JSON; the `.jsonl` holds identical content as plain JSON lines for agents and scripts. |
| `data/questions.json` | The question tree with metadata: `sections > blocks (parent questions) > items`, with answer options, answer counts, % answered, page and page timing (median, quartiles), plus a `file_guide` explaining every field of every file. |
| `data/questions.parquet` | The same tree as a flat table: one row per section / block / item with `node_type` and `parent_id`. |
| `data/everything.parquet` | Flat alternative: one row per answered item (228,175 rows) with question labels, page and seconds. Handy for SQL / pandas group-bys. |
| `data/respondents.parquet` | **For modelling.** Wide, 394 readable snake_case columns + `respondent_id` + `total_seconds`. Select-all items are nullable booleans (`True` = selected, null = not selected *or* not shown); scales are `int8`; everything else is a string. |
| `input.jsonl` | Same data, **original schema and original column names** (`Item:SOURCE_CODE`), HTML removed, empty answers omitted (a missing key means no answer), plus `respondent_id`. |
| `input_structured.jsonl` | Same data in the original nested layout `{"meta": {...}, "responses": {block: {item: value}}}`. See *Fixes to the structured file*. |
| `original_questions.json` | Column-by-column mapping to the source instrument, same schema as before with added `role`, `released`, `duplicate_of`, `col_id`. Includes the dropped columns (names only). |
| `docs/codebook.md` / `docs/codebook.csv` | Full data dictionary: every variable, option, answer count and distribution. **Give this file to an LLM or agent along with the Parquet.** |
| `docs/dropped_columns.csv` | Every removed source column, why, and how many respondents had a value (counts only). |
| `docs/example_respondents.md` | Four respondents rendered as question -> answer. |
| `docs/stats.json` | Machine-readable summary statistics used in this card. |
| `scripts/build_release.py` | Deterministic script that produced everything here from the raw export. |

## Contents at a glance

| Section | Blocks | Variables |
|---|---|---|
| Multi-attraction passes: awareness, consideration, attitudes | 12 | 76 |
| Travel behaviour and intent | 7 | 50 |
| Lifestyle, interests and brands | 10 | 161 |
| Media habits | 1 | 6 |
| Aesthetic and style preferences | 11 | 21 |
| Values, motivations and psychographics | 9 | 60 |
| Demographics | 9 | 9 |
| Socio-economic | 9 | 9 |
| Screener / attention items | 1 | 2 |

Respondent profile (share of 2,007):

| Age band | n | % |
|---|---|---|
| 18-24 | 90 | 4.5% |
| 25-34 | 361 | 18.0% |
| 35-44 | 320 | 15.9% |
| 45-54 | 357 | 17.8% |
| 55-64 | 393 | 19.6% |
| 65 and above | 486 | 24.2% |

| Gender | n | % |
|---|---|---|
| Female | 1,174 | 58.5% |
| Male | 833 | 41.5% |

| Generation | n | % |
|---|---|---|
| Baby boomers | 700 | 34.9% |
| Generation X | 585 | 29.1% |
| Millennials | 533 | 26.6% |
| Generation Z | 100 | 5.0% |
| The Silent Generation | 89 | 4.4% |

Sample skews older: 43.8% are 55 or over, and 80.4% report their ethnicity as White. Weight or stratify before generalising.

### A few headline results

Agreement with statements about multi-attraction passes (Agree + Strongly agree, % of those who answered):

| Statement | % agree |
|---|---|
| They are a great idea | 80.1% |
| They are a convenient option for me when planning my trip | 77.6% |
| They provide lots of activities/excursions to choose from | 77.5% |
| They allow me to make the most from my trip | 75.7% |
| They are an exciting choice to have as part of my trip | 72.4% |
| They provide an easy one-stop-shop for everything I want to see | 72.2% |
| They offer excellent value for money | 70.3% |
| They make me feel confident when planning my trip | 68.7% |
| They are easy to understand | 66.3% |

Preferred destination types (select all; % of the 2,007 respondents who answered):

| Type | n | % |
|---|---|---|
| Beach or seaside resort | 1,045 | 52.1% |
| City or Metropolis | 859 | 42.8% |
| Rural or countryside locales | 453 | 22.6% |
| Mountain or alpine resort | 354 | 17.6% |
| Something else | 268 | 13.4% |
| Culturally/Spiritually significant place | 192 | 9.6% |
| Niche or extreme destinations (e.g. Antarctica) | 51 | 2.5% |

Travel frequency:

| Frequency | n | % |
|---|---|---|
| Once a year or about once a year | 768 | 38.3% |
| Multiple times a year | 692 | 34.5% |
| Less than once a year | 547 | 27.3% |

First-choice pass (answered by 755 respondents; pass brands are shown as letters):

| Pass | n | % |
|---|---|---|
| Pass B | 254 | 33.6% |
| Pass A | 222 | 29.4% |
| Pass F | 94 | 12.5% |
| Pass C | 61 | 8.1% |
| Pass D | 46 | 6.1% |
| Pass E | 42 | 5.6% |
| Pass I | 18 | 2.4% |
| Pass H | 18 | 2.4% |

## How to read missing values (important)

Many blocks were shown to subsets of respondents (for example the Pass A-K blocks were answered by 816 of 2,007). In select-all blocks the export stores a value only when an option was ticked, so **null means 'not ticked' or 'not shown' and the two cannot be told apart**. For rates, use as denominator the respondents who answered at least one item in the block; `docs/codebook.md` gives both denominators for every item. Overall 28.9% of respondent x variable cells are answered.

## Structure: parents and children

Questions are nested. A **section** (theme) contains **blocks**, and a block is a *parent question* such as a select-all list or a rating grid; its **items** are the child rows or options. A single-choice question is a block with exactly one item. 9 sections, 69 blocks, 394 items. `data/responses.jsonl` stores answers in the same nesting, and `data/questions.json` describes it.

A block in `questions.json` (items truncated):

```json
{
 "section_id": "pass_funnel",
 "label": "Multi-attraction passes: awareness, consideration, attitudes",
 "blocks": [
  {
   "block_id": "city_trip_activities",
   "label": "Activities on a city trip (select all)",
   "answer_type": "select_all",
   "question_wording": null,
   "page": 4,
   "page_presentation": 1,
   "page_seconds": {
    "median": 22,
    "p25": 17,
    "p75": 32,
    "n_viewers": 2007
   },
   "n_answered": 1934,
   "pct_answered_of_all": 96.4,
   "options": [
    "Have a stroll or cycle around town",
    "Buy a souvenir",
    "Find the best views"
   ],
   "options_ordered": false,
   "source_code": "AIDA_WW_CBA_CB_27092021",
   "items": [
    {
     "question_id": "city_trip_activities__have_a_stroll_or_cycle_around_town",
     "label": "Have a stroll or cycle around town",
     "n_answered": 1005,
     "pct_of_block_respondents": 52.0,
     "source_column": "Have a stroll or cycle around town:AIDA_WW_CBA_CB_27092021"
    },
    {
     "question_id": "city_trip_activities__buy_a_souvenir",
     "label": "Buy a souvenir",
     "n_answered": 1264,
     "pct_of_block_respondents": 65.4,
     "source_column": "Buy a souvenir:AIDA_WW_CBA_CB_27092021"
    }
   ]
  }
 ]
}
```

A respondent in `responses.jsonl` (truncated):

```json
{
 "respondent_id": "r0001",
 "total_seconds": 2318,
 "sections": [
  {
   "section_id": "travel_behaviour",
   "label": "Travel behaviour and intent",
   "blocks": [
    {
     "block_id": "destination_type",
     "label": "Preferred destination types (select all)",
     "page": 20,
     "page_seconds": 7,
     "answers": [
      {
       "question_id": "destination_type__beach_or_seaside_resort",
       "response": "Beach or seaside resort"
      },
      {
       "question_id": "destination_type__mountain_or_alpine_resort",
       "response": "Mountain or alpine resort"
      }
     ]
    }
   ]
  },
  "..."
 ]
}
```

## Timing

The survey platform logged how long each respondent spent on each page. Questions on a page share that time, so **time is per page, not per question**. In the export a page's time column follows the questions on that page; that rule maps 54 pages to 394 of 394 variables, and was checked against the data: on 40 of 54 pages the respondents with a time are exactly those who answered something on the page, and on the rest no respondent answered a page without having a time (0 violations).

- `page_seconds` (in `responses` and `everything`) is the respondent's time on that page; `total_seconds` (in `respondents`) is their total time. Units are not documented in the source; the typical total of about 1,164 suggests seconds.
- Times are **capped at the 99th percentile of each page** (total at 6,241) because the raw maxima are idle sessions (hours or days). Use the median and quartiles in `questions` for typical timing.
- Timing is not in `input.jsonl` or `input_structured.jsonl`, which keep the original schema.
- Individual timing patterns are a possible link to the source platform's logs. Do not use them to try to match respondents to other records.

## Column naming

`<block_id>__<option>` for grid items (e.g. `destination_type__beach_or_seaside_resort`), and a plain name for single-choice variables (e.g. `age_band`). The `docs/codebook.csv` column `original_column` maps every id back to the source export, and `input.jsonl` keeps the source column names. Block titles are **curated** from the option wording because the source instrument only provides opaque codes (for example `SBEH_WW_DESTTYPE_CB_07062021`). Only 4 variables/blocks have question wording in the source; those are marked in the codebook, and nothing was invented for the rest.

## What was removed, and what residual risk remains

536 columns in the raw export, 394 released, 142 removed:

| Removed category | Columns |
|---|---|
| telemetry | 66 |
| empty | 51 |
| constant | 6 |
| identifier | 5 |
| webhook | 5 |
| open text | 3 |
| timestamp | 2 |
| comments | 2 |
| location geo | 2 |

- Identifiers: response / contact / session / respondent UUID and the row index. Replaced by an opaque id after a seeded row shuffle (so file order carries no timing information).
- Exact timestamps (only the fielding window above is reported) and geo-IP city / region.
- Page timing and total-time telemetry, webhook / redirect plumbing.
- **Pass brand names anonymised.** In two questions the source showed real brand names for four of the passes; they were replaced by the letter used for the same pass elsewhere in the survey, so each pass now has a single consistent label (A-K). The key is held by the dataset owner. Generic category lists (car makers, tech brands, booking channels such as Expedia or Groupon) are unchanged.
- Free-text answers (3 columns: two brand-awareness text boxes and the 'something else' write-in for causes) are **withheld** because they may contain personal details.
- Columns that are constant for every respondent (completion status, language, country, opening agreement item, quota, one screener item) are not repeated; all respondents are marked Complete and English; Country is 'United States' for every respondent who has a value (2,006 of 2,007); everyone answered 'Yes, I agree' to the opening agreement item (`S01Q01`); the screener item `ATR_01` was 'No' for all.
- Columns that are empty for every respondent in this export.

No names, contact details, free text, timestamps, or location finer than the respondent's declared state of residence remain. **Residual re-identification risk is not zero.** The survey records many demographic attributes at once, so respondents can be distinguishable by combination:

| Attributes combined | Respondents unique in the file | Smallest group |
|---|---|---|
| age band + gender + declared state | 123 of 2,007 (6.1%) | 1 |
| + ethnicity | 371 of 2,007 (18.5%) | 1 |
| 12 demographic variables together | 1,848 of 2,007 (92.1%) | 1 |

Someone who already knows many facts about a specific respondent could locate their row. Do not attempt to re-identify respondents, and do not link this file to other datasets for that purpose.

## Known issues in the source data (kept as-is, not recoded)

- **Empty blocks.** The social-media, TV-channel and news-outlet blocks (`social_media_use`, `tv_channels`, `news_outlets`, 41 columns) and Pass G-K in the awareness block contain no responses in this export, so they are not released. Media habits are represented only by the six-channel ranking (`media_rank`); its direction (is 1 most or least used?) is not documented in the source.
- **Label inconsistencies.** The NPS group appears as both `Pasives` (n=77) and `Passives` (n=8); `Saftety` and `Fariness` are misspellings in the source; the same option can appear with a straight or a curly apostrophe (`It's` vs `It’s`). Recode before analysis if you want them merged.
- **Two presentations of one list.** The 'life priorities' block (10 options) appears twice in the source export (second set suffixed `.1`; the two sets differ for many respondents). Both are released (`life_priorities__*` and `life_priorities_p2__*`); what the second presentation represents is not documented. The only other `.1` column with content was a free-text write-in and is withheld.
- **Partly unordered scales.** Age, income bands, income satisfaction, agreement and consideration scales have an explicit order in the codebook. Values such as 'Prefer not to say' are listed as unordered.
- Self-reported, single-week, US-only, older-skewed panel sample; response bias is likely; agreement with every pass statement is between 66.3% and 80.1%, which can reflect acquiescence.

## Fixes to the structured file

Relative to the previous `input_structured.jsonl`: values are now always strings (previously a list in 2,046 places, i.e. a string-or-list mix that strict typed readers reject, and which also embedded free-text write-ins); the second presentation is a separate block key ending `.1` instead of being merged; HTML markup is removed from keys and values; identifiers, free text and constants are removed; `respondent_id` is added to `meta`. Checked against the previous structured file: 226,331 compared cells, differences: none.

## Reproducibility

`python scripts/build_release.py --raw <raw dir> --out <release dir>` reproduces every file from the raw export (seed 20261001). The build asserts that wide, long, flat and structured outputs contain the same 228,175 answered cells and that no identifier-like pattern (UUID, session id, timestamp, URL fragment) appears in the released JSONL.

## Intended use and limitations

Intended for demonstrating and testing survey-analytics methods (descriptive summaries, segmentation, factor analysis, propensity modelling) on non-personal data. Not suitable for inferring behaviour, for population estimates without weighting, or for any attempt to identify individuals. For modelling, validate out of sample.