The ChakataStat Tutorial Workbook
A ten-module course that teaches ChakataStat by doing — built for self-study
and for classroom training of students, teaching staff and researchers. Every
module works on the same bundled sample health study (600 synthetic
participants; see the codebook), so there is nothing to
download: File → Open Sample Dataset (Ctrl+Shift+O) and you are ready.
Each module has the same shape: a question a researcher would actually ask, the menu path that answers it, how to read the output, an exercise to do on your own, and — at the end — the syntax the module generated, because every GUI action in ChakataStat also records an editable, re-runnable command. By module 10 that trail is the star of the show.
If you have not run the app before, do the ten-minute Quick Start first; it previews modules 1–7 in one pass.
The modules
| # | Module | You learn |
|---|---|---|
| 1 | Opening data & the two Views | The sample dataset, Data vs Variable View, and a CSV export→import round trip |
| 2 | The Variable View | Types, labels, value labels, missing codes, measures — and why they matter |
| 3 | Describing your data | Frequencies, Descriptives, Explore |
| 4 | Graphs | Histogram, Boxplot, the Chart Builder |
| 5 | Two categorical variables | Crosstabs and the chi-square test |
| 6 | Comparing group means | Independent/paired t tests, One-Way ANOVA |
| 7 | Relationships | Correlation and linear regression |
| 8 | Making new variables | Compute Variable and Recode (grouping BMI into categories) |
| 9 | Missing data | Blanks vs declared missing codes; Missing Value Analysis |
| 10 | Reproducibility | The Syntax console, .cks scripts, exporting output |
For instructors
- Modules are 20–40 minutes each including the exercise; 1–5 make a half-day introductory session, 6–10 a second session.
- The dataset's structure is planted and documented — the codebook lists which effects are real, and which relationships are deliberately null so students also experience a non-significant result. Answers to each exercise are therefore checkable.
- The sample always opens as a new, unsaved document: students can never
damage it, and a fresh copy is one
Ctrl+Shift+Oaway. - The data are synthetic. No statistic derived from them describes any real population — they exist so the methods can be taught.