Introduction to Scenario Analysis

Scenario analysis in Delphi studies is about understanding how different opinions and expectations cluster into coherent pictures of the future.

Instead of just summarizing all ratings as one average, scenario analysis helps you answer questions like:

• Are there subgroups of experts with similar views?
• Which scenarios are most controversial or most consensual?
• Do ratings suggest multiple possible futures?

By applying scenario analysis, you can identify patterns, consensus, and conflicts, and communicate them in a structured, compelling way.

Below, you'll learn the three most widely used scenario analysis methods, explained step by step.

1. High/Low Consensus Items

What is Consensus?

Consensus means most participants agree on their ratings. Low consensus means their opinions are widely spread.

In Delphi studies, knowing the level of consensus is crucial:

• High consensus = more reliability in forecasts.
• Low consensus = uncertainty or controversy.

Why Use High/Low Consensus Analysis?

You want to highlight scenarios where experts largely agree versus those where opinions diverge. This helps decision-makers:

• Prioritize more robust scenarios.
• Investigate why some topics are divisive.

How to Measure Consensus in Delphi Studies – Fully Automated with Durvey.org

One of the main goals of a Delphi study is to understand how much agreement exists among your experts. High consensus means your panel largely shares the same view; low consensus shows that opinions are divided. Durvey.org automatically measures consensus for every scenario, instantly calculating the key statistics and creating easy-to-read summaries and visualizations you can export directly into your reports.

Here's exactly how it works—and why it matters.

Why Consensus Measurement Matters

When you report your results, simply showing the average rating isn't enough. For example:

• An average score of 3 could mean everyone picked 3.
• Or it could mean half the panel picked 1 and the other half picked 5.

Measuring consensus clarifies how reliable and shared your findings are.

How Consensus Is Measured (Automatically in Durvey.org)

The most widely used indicator is the Interquartile Range (IQR). Durvey.org computes the IQR for each question as soon as your data collection closes—no manual calculations required.

Step 1: Calculate the IQR

The IQR measures how widely the middle 50% of ratings are spread.

Formula: IQR = Q3 − Q1

• Q1 (25th percentile): 25% of ratings fall below this value.
• Q3 (75th percentile): 75% of ratings fall below this value.

Durvey.org automatically calculates Q1 and Q3 for you.

Step 2: Interpret the IQR

The smaller the IQR, the higher the consensus among participants.

Common thresholds (on a 1–5 scale):

• IQR ≤ 1: High consensus
• IQR > 1.5 or 2: Low consensus
• IQR in between: Moderate consensus

Durvey.org highlights these thresholds with clear color coding so you can see at a glance where consensus is strong or weak.

Example (automated):

Scenario:

"AI will fully automate 50% of current jobs by 2040."

Durvey.org results:

• Q1 = 2
• Q3 = 4
• IQR = 2

Interpretation (auto-generated):

"This scenario shows low consensus, with opinions ranging from strong skepticism to moderate agreement."

Why Use Durvey.org?

• All calculations happen automatically
• All visuals are created instantly
• All interpretations are ready to copy-paste into your final report

Best Practices (Fully Integrated)

Durvey.org applies all recommended best practices:

• Always report IQR together with means or medians.
• Flag low-consensus scenarios for discussion or refinement.
• Segment consensus by stakeholder groups if needed (e.g., by experience or region).

2. Cross-Impact Analysis

What is Cross-Impact Analysis?

Cross-impact analysis helps you understand how different scenarios or variables influence each other.

It answers questions like:

• Does belief in scenario A affect belief in scenario B?
• Are ratings of scenario X correlated with ratings of scenario Y?

Why Use Cross-Impact Analysis?

Delphi results are often interdependent. Experts who believe in strong technological progress might also expect social disruption, for example.

By examining these relationships, you can:

• Build consistent narratives of the future.
• Detect logical dependencies between scenarios.
• Cluster related scenarios together.

How to Do Cross-Impact Analysis in Delphi Studies – Fully Automated with Durvey.org

Understanding how different scenarios influence each other is a crucial part of Delphi foresight work. This process is called cross-impact analysis. Durvey.org performs all calculations, visualizations, and report-ready interpretations automatically, saving you hours of manual data handling.

Below, you'll learn exactly how it works—and why it matters.

Why Cross-Impact Analysis Matters

When exploring the future, scenarios rarely happen in isolation.

For example:

• If remote work becomes widespread, it might also impact urban decline.
• If AI automates jobs, it might affect basic income adoption.

Cross-impact analysis helps you see these hidden relationships. It reveals whether experts consistently link some scenarios together—insight that can power better strategic decisions.

With Durvey.org, this process happens in one click.

What is Cross-Impact Analysis?

Put simply, cross-impact analysis measures how strongly experts' expectations about different scenarios are connected.

Durvey.org does this by calculating correlations between all pairs of scenario ratings across participants.

How It Works (Fully Automated)

Traditionally, you would export all ratings and run complex statistical scripts. With Durvey.org, it's as easy as clicking "Run Cross-Impact Analysis."

Here's what happens behind the scenes:

Step 1: Collect Scenario Ratings

Experts complete their ratings for all scenarios in your study.

Example:

• Scenario A: "Remote work becomes the norm."
• Scenario B: "Urban centers decline."

Durvey.org securely stores all ratings in your project.

Step 2: Calculate Pairwise Correlations

Durvey.org calculates Pearson's r correlation coefficient for every combination of scenarios.

What does Pearson's r show?

• r > 0.5: Strong positive relationship (when Scenario A is rated higher, Scenario B tends to be rated higher too)
• r < -0.5: Strong negative relationship (when Scenario A is rated higher, Scenario B tends to be rated lower)
• r near 0: Little or no relationship

These calculations are fully automatic—no manual data wrangling required.

Step 3: Interpret the Strength of Relationships

Durvey.org highlights correlation strengths automatically:

Interpretation guide:

• r > 0.5: Strong positive cross-impact
• r < -0.5: Strong negative cross-impact
• r between -0.5 and +0.5: Weak or no clear relationship

Example:

You calculate r = 0.65 between:

• Scenario A: Remote work adoption
• Scenario B: Urban decline

Interpretation (auto-generated):

Experts who expect widespread remote work also expect cities to decline—showing a strong positive cross-impact.

Durvey.org includes these interpretations in your downloadable report text.

Best Practices (Integrated Automatically)

Durvey.org ensures you follow established best practices:

• Define exactly which scenarios you are correlating.
• Use consistent rating scales for all scenarios (e.g., 1–5 or 0–100).
• Interpret correlations carefully—remember, correlation ≠ causation.

The platform includes a checklist so you never miss a step.

Why Use Durvey.org for Cross-Impact Analysis?

With Durvey.org:

• All steps are automated
• Interpretations are pre-written for you
• Your report is ready to share or publish immediately

3. Fuzzy C-Means Clustering

What is Fuzzy Clustering?

Fuzzy C-Means Clustering is an advanced technique that groups scenarios or respondents into clusters—but with a twist:

Instead of each scenario belonging to only one cluster, fuzzy clustering assigns degrees of membership. This reflects the real-world fuzziness of opinions.

Why Use Fuzzy Clustering?

Fuzzy clustering allows you to:

• Detect overlapping scenario groupings.
• Respect that scenarios can belong partly to multiple themes.
• Visualize nuanced patterns in your data.

How to Do Fuzzy C-Means Clustering in Delphi Studies – Fully Automated with Durvey.org

One of the most powerful ways to uncover patterns in your Delphi data is clustering. Fuzzy C-Means clustering goes a step further:

Instead of forcing each scenario into a single category, it calculates degrees of membership—recognizing that complex topics often belong partly to multiple themes.

With Durvey.org, all clustering steps, visualizations, and interpretations are generated automatically—no coding required.

Below, you'll see exactly how it works and why it's so valuable.