AHP in QualiAnalysis

Build an AHP model from qualitative findings to decision-ready ranking.

In this system, AHP is not isolated from the qualitative workflow. You can start from your themes, coding nodes, and elements, turn them into an AHP hierarchy, collect expert judgments, calculate weights, then compare alternatives with sensitivity checks.

Level 1: AHP engine Level 2: qualitative → AHP bridge Level 3: publish, alternatives, sensitivity
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Qualitative Data segments · codes · themes AHP Preparation promote sources + notes Hierarchy goal · criteria · subcriteria Freeze Survey generate pairwise items from the final hierarchy Experts respondents + groups Calculate weights + CI/CR Alternatives ranking + sensitivity Publish version and use results in research/reporting

Recommended workflow in our system

1
Prepare qualitative evidence first.
Do transcript reading, segment creation, coding, and themes before touching AHP. In this app, themes are AHP candidates, not automatic criteria.
2
Create one AHP model per decision problem.
Inside Analytics → AHP Survey, define the model name, goal statement, and domain type. Keep one clear decision question per model.
3
Use the Preparation Bridge.
Promote elements, themes, or coding nodes into the hierarchy. Write preparation notes for merge/split rules, evidence gaps, counter evidence, and expert briefing.
4
Build the hierarchy deliberately.
Refine the structure into Goal → Criteria → Subcriteria. Do not move weak or overlapping themes directly into the final structure without review.
5
Freeze the survey.
The system generates pairwise questions from the hierarchy. This is the point where the structure becomes the active survey.
6
Add experts and collect pairwise judgments.
Create respondents, optionally assign group labels, then open each questionnaire and submit complete answers.
7
Calculate and check consistency.
The system computes local/global weights, λmax, CI, and CR. Use this to see whether the judgment set is logically stable enough.
8
Publish the stable survey snapshot.
When the model and aggregate result are acceptable, publish it. The system stores a version snapshot so the state can be traced later.
9
Compare alternatives.
Add alternatives, score each alternative against the leaf nodes, then run ranking. Use sensitivity controls to override top-level criterion weights and see whether the ranking changes.

What each screen does

AHP Model
Create the decision context: name, goal, and description.
Preparation Bridge
Pull in elements, themes, and coding nodes as source candidates before formalizing the hierarchy.
Hierarchy Builder
Define criteria and subcriteria with operational definitions and expert prompts.
Survey Setup
Freeze the hierarchy, edit instructions, choose aggregation mode, and publish/archive versions.
Respondents
Add experts, roles, organizations, and optional group labels for grouped comparison.
Pairwise Questionnaire
Collect Saaty-scale judgments between sibling nodes.
Results
Review aggregate, individual, and grouped output, including consistency diagnostics.
Alternatives & Sensitivity
Score options, rank them, then override criterion weights to test robustness.

How to think about consistency

Weights are the priority scores of criteria/subcriteria after the pairwise matrix is normalized.

CI is the Consistency Index. CR is the Consistency Ratio. They tell you whether the expert judgments are reasonably coherent.

Practical rule: if CR <= 0.10, the response is usually acceptable. If it is much higher, re-check the expert’s pairwise answers.
A B C If A > B and B > C, then A should also be stronger than C. CR measures how far the full answer set drifts from that logic.

Recommended research sequence

  1. Finish segmenting and coding first.
  2. Stabilize themes/findings.
  3. Open AHP only when you already know which factors are decision-relevant.
  4. Use themes as candidate inputs, then refine them into criteria/subcriteria.
  5. Collect expert judgments.
  6. Calculate weights and use the result for prioritization or recommendation.
Avoid this shortcut: do not force every theme directly into AHP. Some themes belong in the qualitative explanation only and should not become criteria.