> For the complete documentation index, see [llms.txt](https://help.metaforms.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.metaforms.ai/data-processing/python/step-1-setting-up-a-project.md).
# Step 1: Setting Up a Data Validation Project
This section covers everything from creating your project to generating validation scripts — the foundational setup before you begin reviewing or running checks.
### Step 1: Create a New Project
From the Metaforms home screen, enter a project name and click **Create Project**. This creates the workspace where your questionnaire, data files, and validation scripts will live.
### Step 2: Open the Data Validation Tab
From the left-hand menu inside your project, select **Data Validation** to enter the module.
### Step 3: Upload Your Questionnaire
Click the upload area to select your questionnaire file (PDF or DOCX). Metaforms will parse and analyze the document, extracting question texts, response options, and routing logic. This typically takes a few seconds.
> **Tip:** You can also upload additional reference files (such as brand lists, option lists, or routing specifications) alongside your questionnaire. The AI reads these files to build more accurate validation logic for complex survey designs.
### Step 4: Select a Scripting Method and Provide Your Data File
* Choose your scripting language as **Python** then provide the path to your data file (.SAV).
* Click **Start Validation** to begin.
### Step 5: AI Generates Validation Scripts
Once started, the AI agent takes over. On the right side of the screen, you can watch the agent's progress as it works through three stages:
1. **Metadata extraction** — The agent reads the SAV file and extracts all variable definitions, question structures, and coding schemes.
2. **Questionnaire comparison** — It compares the extracted metadata against your uploaded questionnaire to identify any mismatches in question text, response options, or variable codes. Questions where the metadata aligns with the questionnaire are marked **valid**; those with discrepancies are marked **invalid** and flagged for your attention.
3. **Script generation** — For each valid question, the agent writes a Python validation script. These scripts check for conditions like:
* **Range checks** — Whether response values fall within the expected set of options (e.g., options 1–5 for a single-select question, flagging any unexpected 6th or 7th value).
* **Single-select vs. multi-select enforcement** — Whether a question marked as single-select truly has only one response per respondent, or whether a multi-select question is being treated as single-select.
* **Termination logic** — Whether respondents who should have been terminated (e.g., answering "No" to a screening question) were actually routed out of the survey.
* **Skip and routing logic** — Whether respondents were correctly shown or skipped past questions based on their prior answers and the questionnaire's base conditions.
This process typically takes **5–15 minutes** depending on the number of questions. For a 50-question survey, expect roughly 12–15 minutes; a 36-question survey may complete in 10–15 minutes.
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