> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wenite.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Survey Analysis
> Reading and interpreting a single survey's results dashboard.
## 1. Accessing results: statuses and actions
You find results per survey under **Surveys** in the left-hand navigation panel. For any survey with status **Active** or **Closed**, you can navigate to the results via the **action menu** (on the right). You don't need to wait until a survey is closed — if the survey is still active, a disclaimer appears at the top indicating that these are interim results.
## 2. The overview page
When you open the results, you first see an overview page with:
* **Title, status, and company name**
* **Target** — the expected number of respondents, set when the survey was created. Only used to calculate the response rate (not as a maximum). The target can be adjusted by moving the survey back to draft via the action menu.
* **Response rate** — the number of completed surveys divided by the target.
### Performance overview chart
Below the basic details is a chart that summarizes the entire survey. The **first column** shows the score across the whole organization, per module. The remaining columns break the score down per socio-demographic group, so you can compare at a glance.
Use the purple **"Generate AI summary"** button to get a first interpretation of the results based on a closed AI model — see [§10 AI integrations](#10-ai-integrations) for the full list of AI features on this page.
You can filter the chart's rows, and hover any score to see a tooltip with the exact score and benchmark comparison. Export the chart itself as a CSV alongside the raw-data export described in [§9](#9-exporting-data).
In the results dashboard, scores are only shown grouped across respondents. When setting up a survey, you can configure the **minimum groupsize**. For socio-demographic categories with fewer respondents than this minimum, scores are not shown separately. These respondents are still included in the score calculations for the group as a whole.
## 3. Structure, scores, and benchmarks
The platform works with up to three levels:
| Level | Description |
| ------------ | ------------------------------------------------------------------------- |
| **Question** | What is concretely asked of the respondent. |
| **Module** | A grouping of multiple questions around one theme, with a combined score. |
| **KPI** | A grouping of multiple modules into one overarching score. |
### How are scores calculated?
We rescale all questions that use a response scale to a uniform scale running from **0 to 100**, using a simple linear transformation. This ensures that all questions, modules, and KPIs can be interpreted in the same way, regardless of the scale they were originally measured on.
Example for a question with 5 answer options:
| Answer | Score |
| ------ | ----- |
| 1 | 0 |
| 2 | 25 |
| 3 | 50 |
| 4 | 75 |
| 5 | 100 |
A score of 73/100 doesn't mean that 73% of people scored well — it means the average answer falls at that position on the scale. The **module score** is the average of the individual question scores. The **KPI score** is the average of its underlying module scores.
### How to interpret module and KPI scores
A higher score means that there is "more" of that construct. Some examples:
* A higher score on Job Satisfaction means respondents are more satisfied.
* A higher score on Burnout means respondents have more burn-out indications — a worse result, not a better one.
A higher score therefore does not always equal a more positive score — it's important to take the meaning of a construct into account when interpreting its absolute score. Use the WPS if you need a metric where positive always means "better".
### WPS (Wenite Performance Score)
The **WPS**, or **Wenite Performance Score**, tells you how good or bad an obtained score is relative to the chosen benchmark. A positive WPS means the score is better than the benchmark; a negative WPS means it's worse. With the WPS, positive always means "better" — even for negatively-framed constructs like Burnout. The WPS color indicates whether the difference is relevant.
### Color codes
| Color | Meaning |
| ----------- | --------------------------------------- |
| Dark green | +20 or more relative to the benchmark |
| Light green | +10 to +20 |
| Yellow | Less than 10 absolute points difference |
| Light red | −10 to −20 |
| Dark red | −20 or worse |
### Benchmarks
Wenite offers four benchmark types, three of which can be enabled on an individual survey (see the [Creating a Survey guide](/product/survey-creation)):
| Benchmark | Description |
| -------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Standard WPS** (default) | Compares answers with the middle of the response scale. A score above 0 means better than neutral, below 0 means worse. |
| **Wenite benchmark** | Compares results with an external group — for Wenite modules, Belgian white-collar workers balanced by age and gender. A score above 0 means better than the benchmark, below 0 means worse. |
| **Company benchmark** | Compares a filtered group against the company's total group. A score above 0 means the group scores better than the total, below 0 means worse. |
| **Trend benchmark** | Compares the most recent score with a previous round's score. Not selectable when creating a survey — only available in the company dashboard, once a client has more than one round. |
### Statistical significance
When a score has a **dot as superscript**, this means the difference with the benchmark is statistically significant. Wenite uses a Bayesian framework for this: Bayes Factors are calculated, and a score is labeled significant when the Bayes Factor is greater than 3.
In practice: the dot shows that we can say, with reasonable statistical certainty, that this score genuinely differs from the benchmark rather than being due to chance. This certainty is driven both by the absolute difference and by the number of respondents.
## 4. Filters
At the top of the dashboard, you can set **filters** based on the socio-demographic questions asked (e.g. Location or Department). When you activate a filter, all scores shown reflect only that selection.
Combined with the **company benchmark**, this becomes especially powerful — letting you easily compare a filtered group's score against the rest of the respondents.
## 5. Module tab
For each module, you see:
* Number of respondents
* WPS score with benchmark comparison
* Distribution of responses (histogram)
* Group differences per socio-demographic question
* The underlying questions with their answer distribution
For surveys with conditional logic, not every respondent may see every question, so the number of respondents per module can vary.
On the module page, you can view group differences not just as a **bar chart**, but also as a **heatmap**. Each tile then shows the score difference between two groups. Hover over a tile for more detail on the comparison.
You can **pin** a module so it's easy to find again, switch between a list and a grid view, and filter or sort modules (e.g. by score, or by name) — useful once a survey has more than a handful.
## 6. KPI tab
The KPI tab contains the same information as the module tab, at a higher level. It also shows the underlying modules per KPI (if KPIs were used in the survey). The same **pin**, **grid/list view**, and **filter/sort** options are available here too.
## 7. Questions tab
On the **Questions** tab, you can view the answer distribution for each individual question. You can filter by question type: single-choice, open questions, or multi-choice (where available).
For open answers, the main themes are automatically summarized and classified (as positive, negative, or neutral) by an AI model. This happens once the survey is closed.
## 8. Insights tab
The **Insights** tab is most relevant when you use Wenite's scientifically validated item library. Here you find advanced analyses that go beyond the basic scores.
### Focus Areas
The **Focus Areas** view shows how **job stressors** and **job resources** influence organizational outcomes such as job satisfaction or employee recommendation. Impact is quantified using regression coefficients. Hover over the dots on the chart to see which predictor has the greatest impact and what the recommended action is.
Use the purple **"Generate AI summary"** button to get a first interpretation of the results based on a closed AI model.
### Predictive insights
Certain KPIs in Wenite can be used to make predictions about relevant outcome measures that were not directly measured. Currently:
* **Job Stressors** can be measured to predict **Burnout**
* **Job Resources** can be measured to predict **Engagement**
The prediction shows a risk estimate (**high/medium/low**) along with a visualization of how respondents are distributed across the risk categories.
## 9. Exporting data
The export function lets you download the **raw results per respondent** as an Excel file. Each respondent appears as a row with all their scores. Respondents are anonymized (numbered), but you do see their individual answers, including open answers.
For the dashboard views themselves (not the raw data), use your browser's print function to export a page as **PDF** — this captures whatever chart, filter, and benchmark selection is currently on screen.
Based on individual answers, it is sometimes possible to identify certain respondents. It's important to safeguard respondent anonymity. This export does not include the groupsize checks that apply in the results dashboard. If you give a client access to the dashboard, you can disable the export function so they cannot access individual answers.
## 10. AI integrations
Throughout the results dashboard, you'll find **purple buttons** — these indicate AI-powered functionality. This is one of the fastest ways to turn raw scores into something a client can actually read, so it's worth knowing all four touchpoints exist, not just the one you happen to click first:
AI functionality runs in a closed environment: data never leaves the Wenite system. Every AI model's output passes through additional review layers. If anything looks off, contact [support@wenite.io](mailto:support@wenite.io).
| Feature | Location | Description |
| ---------------------------------- | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Executive summary** | Top right of every page | Generates a one-page Word document covering all results — including who responded, all strengths and weaknesses, and suggestions for improvement. Can be freely edited afterwards. |
| **Performance overview summary** | Overview tab | Generates an automatic summary of the overview chart, broken down into strengths, challenges, and mixed results. Adapts based on the filters applied. |
| **Focus Areas summary** | Insights tab | Generates a first interpretation of the stressor/resource impact chart — see [§8](#8-insights-tab). |
| **Open questions: theme analysis** | Questions tab | For open questions, the AI analyzes the answers and distills the main themes. Each theme gets a label, a short explanation, and two representative quotes. Identifiable information is automatically removed. |
## Recommended workflow for results analysis
1. **Start on the overview page** — Check whether the response rate and the distribution of socio-demographic answers match expectations.
2. **Set the right benchmark and filters** — The Wenite benchmark is usually the most valuable. If unavailable, use the Standard benchmark (comparison against the middle of the scale). The Company benchmark is most valuable when combined with filters.
3. **Pull out the most striking insights from the performance overview table** — Look at which scores stand out (strong deviations, red/green), both across the whole organization and across the different socio-demographic groups.
4. **Go to the KPI and/or module tab to zoom in** — Review the underlying questions, the spread of scores, and the group differences.
5. **Go to the questions tab to zoom in on specific questions** — Review extra questions outside modules and the summaries of open questions.
6. **Use the AI features** — The Executive Summary gives a clean overview of the entire survey on a single page.
7. **Export the results for further analysis** — Use the export button for scores and open answers per respondent.
8. **Need something more tailored?** — See the [Custom Reporting guide](/product/custom-reporting) for group reports and individual respondent reports.
If no results are shown for a particular group of respondents, this is because there aren't enough respondents to meet the minimum groupsize configured when the survey was created.