Learn from finished studies

Each method comes as one set — raw data, the finished run, and paper-ready tables. Browse it, copy it into your workbench, or walk the same path with your own data.

Three axes, real paths — walk one with sample data

Pick a sample and you land on “exactly the screen” you would reach with your own file. Nothing runs by itself — it only takes you to the first stop.

Quantitative

Employee satisfaction survey — with the core 7

The question:
Does satisfaction differ by department, and do the less satisfied really intend to leave?
The data:
Survey responses · CSV · 120 rows × 10 columns
The path:
Core 7 → check the loaded data → pick a question on the rail → the statistic that answers it

Fictional data, made for practice. · 0 credits

A survey that needs cleaning — blanks, reversed items, outliers

The question:
What has to be fixed before this data can be analyzed?
The data:
Survey responses · CSV · with blanks and a reversed item · 120 rows × 8 columns
The path:
Intake → see what was read → the thorough lane (precision prep)

Fictional data, made for practice. · 0 credits

Satisfaction before and after — measuring the same people twice

The question:
Did satisfaction change from before to after, and does that change differ by participation?
The data:
Pre/post responses · CSV · with a group column and two background columns · 120 rows × 6 columns
The path:
Intake → see what was read → the “change over time” question → pick a qualified engine

Fictional data, made for practice. · 0 credits

Why people leave — workload, burnout, turnover intent

The question:
Does workload raise turnover intent by way of burnout?
The data:
Cause, mediator, outcome plus two controls · CSV · 200 rows × 6 columns
The path:
Intake → see what was read → the “mechanism” question → pick a qualified engine

Fictional data, made for practice. · 0 credits

Qualitative

12 AI news reports — which frame is doing the talking

The question:
Do different regions tell different stories about the same technology?
The data:
Press excerpts · CSV · media, region, date, excerpt · 12 rows × 5 columns
The path:
Intake → routed to qualitative → the prep bench (fix the units)

Fictional data, made for practice. · 0 credits

Open-ended survey answers — with department and tenure

The question:
What gets in the way, and does it differ by department?
The data:
Open-ended text plus meta columns · CSV · 24 rows × 4 columns
The path:
Intake → routed to qualitative → prep bench (units, then join the meta columns)

Fictional data, made for practice. · 0 credits

Decision & Game Theory

An evaluation table — five candidates on four criteria

The question:
Who comes first when criteria conflict — and who decides each criterion’s direction?
The data:
Alternatives × criteria table · CSV · 5 rows × 5 columns
The path:
Intake → detected as a table → prep (pick each direction) → confirm

Fictional data, made for practice. · 0 credits

A policy-priority survey — the counting rule changes the winner

The question:
When six people’s rankings are pooled, which option comes first?
The data:
Ranking-block survey · CSV · 6 rows × 4 columns
The path:
Intake → ranking block detected → confirm the table → social choice

Fictional data, made for practice. · 0 credits

Expert pairwise comparisons — what to do with self-contradicting answers

The question:
Derive weights from judgement — and decide what happens to the inconsistent judges.
The data:
“A vs B” pairwise survey · CSV · 4 rows × 4 columns
The path:
Intake → pairwise detected → see each judge’s CR → confirm (a stop point)

Fictional data, made for practice. · 0 credits

A long-form payoff table — four cells where two strategies meet

The question:
If the other side reasons as I do, where does it settle?
The data:
Strategy pairs with payoffs (long form) · CSV · 4 rows × 4 columns
The path:
Intake → detected as a payoff table → the game engine reads it directly

Fictional data, made for practice. · 0 credits

No file needed — check your walk-away option (BATNA)

The question:
If the deal falls through, what am I left with?
The data:
No data — entered on screen
The path:
Decision shelf → the BATNA inputs → run

Fictional data, made for practice. · 0 credits

See a finished study end to end

Qualitative · finished

Discourse frames — how the world press writes about AI

Derived from world_press data · 12 press excerpts on AI

The same technology, framed differently by region — Entman’s four functions coded, then crossed.

See the full set
Qualitative · finished

Phenomenology — what it is to live recovery

Synthetic interviews with 5 people · 35 utterances about recovery

From the bracketing memo to the description of essential structure — a phenomenological study carried through.

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Qualitative · finished

Narrative — a life story of recovery

One synthetic life-history interview · 40 transcribed turns (about 90 minutes)

A timeline rebuilt and two turning points — where one person’s story bends and turns back.

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Qualitative · finished

Grounded theory — the conditions of civic participation

Synthetic interviews with 8 people · 48 utterances

From data to theory — open coding through axial and selective coding, ending at a saturation check.

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Qualitative · finished

CQR — thesis stress among graduate students

Synthetic interviews, 6 cases · double-coded by two coders · auditor check included

How two coders working independently pass through kappa, a consensus record and an auditor check to reach a general/typical/variant frequency table.

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Qualitative · demo

Sociometry — a team retrospective, from text to a map of relations

Synthetic team retrospective interviews · 7 members · 16 confirmed relation tuples

The bridge from sentences in a retrospective to relation tuples, a 7×7 matrix, and four structures: star, bridge, clique and isolate.

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Mixed · finished

Mixed methods — civic participation, from grounded theory to a survey

Exploratory sequential design · 240 synthetic respondents (categories → items → α → EFA → regression)

Qualitative categories become survey items, and 240 responses carry through alpha, EFA and regression.

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Quantitative · finished

A small survey, start to finish: from frequencies to regression

60 synthetic respondents · six steps — frequencies → descriptives → t-test → one-way ANOVA → correlation → regression

Watch one quantitative analysis run in order, and read the tables and APA sentences the engines produce. Data and results are in English.

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Quantitative · finished

One resident survey, end to end: questionnaire to report

Synthetic data, 1,000 respondents, 32 columns, six steps — intake, preprocessing, reliability, factor analysis, group comparison, regression

A file shaped like what a research firm receives, followed all the way through the engines into tables and sentences.

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Quantitative · finished

Twelve months of service requests: daily, weekly and monthly ask different questions

Synthetic data, 17,305 records, 14 columns, five steps — intake structure, daily, weekly, monthly, explanation

A file shaped like what a local government already collects, followed through the questions each reporting cycle can and cannot answer.

Synthetic demonstration data — not a real local government

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Quantitative · finished

Did it change before and after — one pre/post set

Synthetic data, 120 respondents, 6 columns, six steps — who answered, the shape of the score, a paired t-test, the nonparametric alternative, a group comparison that controls for the pre-test, and a reshape to long for time × group

See what measuring the same people twice lets you do, and how the question “did the participants gain more?” is actually settled.

Synthetic practice data — not a real survey

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Quantitative · finished

Why people leave — burnout between workload and turnover intent

Synthetic data, 200 respondents, 6 columns, four steps — the shape of the variables, correlations, a mediation analysis, and a hierarchical regression (controls, cause, mediator)

Go past “these two move together” to “what does it travel through?”

Synthetic practice data — not a real survey

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Learn from finished studies | Statory