# Workshop

Learn PRISM through hands-on exercises with real example data.

## Overview

The PRISM workshop takes you from raw survey data to a fully validated, scored, and exportable dataset. The **core basics track uses 4 folders** and optional extensions can be added if time allows.

| Exercise | Topic | Duration | What You'll Learn |
|----------|-------|----------|-------------------|
| **0** | Project Setup | 15 min | YODA principles, folder structure |
| **1** | Data Conversion | 30 min | Excel → PRISM format |
| **2** | Metadata & Validation | 25 min | Find and fix metadata gaps |
| **3** | Recipes & Scoring | 20 min | Calculate questionnaire scores |
| *(Optional)* | Participant Mapping | 30-45 min | Demographic transformations |
| *(Optional)* | Templates | 20 min | Create reusable item descriptions |

**Total time: ~90 minutes (core) / ~2 hours (with extensions)**

## Getting Started

### 1. Launch PRISM Studio

```bash
python prism-studio.py
```

Run the command from repository root.

### 2. Open the Workshop Materials

The workshop files are in:
```
examples/workshop/
├── exercise_0_project_setup/
├── exercise_1_raw_data/
├── exercise_2_hunting_errors/
├── exercise_3_using_recipes/
├── exercise_4_templates/             # optional
└── exercise_5_participant_mapping/   # optional extension
```

### 3. Follow the Handout

Open the complete guide: `examples/workshop/WORKSHOP_HANDOUT_WELLBEING.md`

---

## Exercise Summaries

### Exercise 0: Project Setup (YODA)

**Goal**: Create an organized research project following YODA principles.

**Key Concepts**:
- Separation of raw data, code, and results
- Project root contains the validated PRISM dataset
- `code/` contains analysis scripts
- `analysis/` contains derived results

**Steps**:
1. Go to **Projects → Create New Project**
2. Name your project `wellbeing_study`
3. Observe the created folder structure

### Exercise 1: Data Conversion

**Goal**: Convert `wellbeing.xlsx` to PRISM format.

**Source Data**: `exercise_1_raw_data/raw_data/wellbeing.xlsx`

| participant_id | WB01 | WB02 | WB03 | WB04 | WB05 |
|---------------|------|------|------|------|------|
| sub-001 | 3 | 4 | 3 | 4 | 3 |
| sub-002 | 2 | 2 | 3 | 2 | 2 |

**Steps**:
1. Go to **Converter**
2. Load `wellbeing.xlsx`
3. Map the `participant_id` column
4. Select all `WB*` columns as survey items
5. Convert and save to your project

**Output**: BIDS-structured files in `sub-XXX/survey/` at project root

### Exercise 2: Metadata & Validation

**Goal**: Validate the converted dataset and fix missing survey metadata.

**Expected findings**:
- Missing study-level survey metadata
- Missing item descriptions
- Missing response-level labels

**Steps**:
1. Go to **Validator** (`/validate`)
2. Select your project folder and run validation
3. Use `exercise_4_templates/survey-wellbeing.json` as source metadata
4. Re-run validation until issues are resolved

### Exercise 3: Recipes & Scoring

**Goal**: Calculate dummy wellbeing scores and export to SPSS.

**Recipe**: The demo total score is the sum of items (WB01-WB05).

```json
{
  "RecipeName": "Workshop Dummy Wellbeing",
  "Scoring": {
    "wellbeing_total": {
      "operation": "sum",
      "items": ["WB01", "WB02", "WB03", "WB04", "WB05"]
    }
  }
}
```

**Steps**:
1. Go to **Tools → Recipes & Scoring**
2. Select your dataset
3. Load `recipe-wellbeing.json`
4. Run and export as SPSS (.save)

### Optional Extension: Templates

**Goal**: Create survey metadata with item descriptions.

**Why?**: Templates make your data self-documenting. Anyone who opens your `.json` sidecar can understand what each item measures.

**Steps**:
1. Go to **Tools → Template Editor**
2. Create a new survey template
3. Add items with questions in English and German
4. Add response options with labels
5. Save to your project

### Optional Extension: Participant Mapping

**Goal**: Transform demographic encodings into standardized values.

**Example**:
- Sex codes: `1/2/4` → `M/F/O`
- Age text: `"25 years"` → `25`

**Steps**:
1. Add a `participants_mapping.json` in project `code/library/`
2. Run conversion/validation again
3. Confirm standardized `participants.tsv` output

---

## Sample Data

The workshop uses a **dummy wellbeing questionnaire** for demonstration:

| Item | Question |
|------|----------|
| WB01 | I felt motivated to start my daily tasks |
| WB02 | I could focus well during important activities |
| WB03 | I had enough energy throughout the day |
| WB04 | I recovered well after work or study periods |
| WB05 | I made time for activities I personally enjoy |

**Scale**: 0 (At no time) to 5 (All of the time)

**Scoring**: Sum of WB01-WB05 (demo score)

---

## Tips for Instructors

1. **Allow buffer time** – Participants work at different speeds
2. **Show the raw data first** – Context helps understanding
3. **Validate after Exercise 1** – See what's missing before adding metadata
4. **Demo SPSS export** – This is often the "wow" moment
5. **Have solutions ready** – Each exercise folder includes a solution

---

## Additional Resources

- [CLI Reference](CLI_REFERENCE.md) – Command-line options
- [Recipes Guide](RECIPES.md) – Creating custom scoring recipes
- [Survey Library](SURVEY_LIBRARY.md) – Pre-built survey templates
- [Error Codes](ERROR_CODES.md) – Understanding validation messages