How to Conduct a Path Analysis
Use a path analysis when you want to measure the direct and indirect impact of predictors on the outcome.
Step 1: Specify Your Path Model
- Click Path Analysis in the sidebar menu.
- Enter your variables:
- Enter your outcome variable (target you want to predict, e.g., Revenue, Performance).
- Enter your predictor variables (features used for prediction, e.g., satisfaction, brand loyalty, burnout, address mismatch).
- Add mediators if you want to test indirect effects (e.g., whether product satisfaction affects churn (mediator), which in turn affects revenue).
Step 2: Run the Path Analysis
- Review your model structure for accuracy.
- Click Apply variables.
- The platform will present a path diagram, estimate path coefficients (effects), significance levels, and model fit statistics.
Step 3: Interpret the Results
- Review the output:
- Review the path diagram. Significant paths are shown in red and bold.
- Review the SEM results. Examine the direction and magnitude of each path coefficient (positive (+) / negative (−), strong > weak).
- Check p-values for statistical significance. If p < 0.05, the association is statistically significant; if p ≥ 0.05, it is not.
- Look for indirect effects (mediation) if included.
- Review overall model fit (e.g., R² values).
Step 4: Report and Act on Insights
- Look for Averisys AI and click Results to view the summarized findings:
- Highlight key drivers of your outcome variable.
- Note any significant mediators or indirect effects.
- Click Reports to see the executive summary.
- Click Scenario simulation in Averisys AI to test what-if scenarios (e.g., does improving product satisfaction increase revenue?).
- Click Risk score in Averisys AI to identify risk of the outcome (e.g., revenue risk).
- Click Recommended Action in Averisys AI to make AI-based decisions and develop action plans (e.g., focus on improving product satisfaction to boost revenue).
- Click Publish this page to share results with stakeholders via a published page.
- Click Invite Users to invite stakeholders to share results via dashboards.
Example Interpretation
- Product satisfaction has a strong, significant positive effect on Revenue (p < 0.05, positive coefficient).
- Marketing satisfaction shows a weak, non-significant effect on Revenue (p > 0.05).
- Adjusted R² indicates how much variance in Revenue is explained by the model.
Tips & Best Practices
- Ensure your sample size is adequate for reliable path analysis.
- Use clear, theory-driven models to avoid overfitting.
If something does not work as described
Screens are updated as the platform changes, so a button may sit in a slightly different place than the wording above suggests. When that happens, the AI assistant inside the platform is the fastest way forward: it can see the page being worked on and answer in context, at any hour. Ask it what a message means, why a file was rejected, or which step comes next.
Most problems in this part of the workflow come from the data rather than the platform. Column headers that repeat, numbers stored as text, blank rows at the bottom of a file, and inconsistent category labels are the usual causes. Checking those four things resolves the majority of failed uploads and unexpected results before any further troubleshooting is needed.
For anything that still does not resolve, email support@averisysanalytics.com with the guide title, the step reached, and what appeared instead of the expected result. Including the file name and a screenshot usually means the issue is answered in the first reply. Related instructions are listed in the full platform guide, and common questions are collected in the support FAQ.