Educational Intervention
Educational intervention projects teach a group, such as patients, caregivers, or staff, new knowledge or skills and measure what changes as a result. The key design question is how far along the chain from learning to practice to patient outcomes you can realistically measure.
Common mistakes for this type
- Measuring only knowledge right after the session and treating it as evidence of changed practice.
- Using a self-made test without checking that it covers what was taught and leaves room to show improvement.
- Collecting anonymous pre and post surveys that can't be linked, then analyzing them as if they were paired.
Choose how far along the chain to measure
A widely used way to think about training outcomes is the Kirkpatrick model, which describes four levels. Each level is harder to measure and more meaningful than the one before it.
| Level | Question it answers | Typical measures | Time needed to see change |
|---|---|---|---|
| Reaction | Did participants find it useful? | Satisfaction or feedback survey | Immediately |
| Learning | Did they gain knowledge, skill, or confidence? | Knowledge test, skills check, confidence scale | Immediately to weeks |
| Behavior | Do they practice differently? | Observation, chart audit, documentation rates | Weeks to months |
| Results | Did patient or organizational outcomes improve? | Clinical outcomes, rates, costs | Months or longer |
Most educational projects can measure learning. Fewer can measure behavior, and results are often out of reach in a DNP timeline. That’s fine if your claims match the level you measured. Read the guide
Measurement issues specific to education projects
Confidence isn’t competence. Self-rated confidence often rises after training even when skills don’t. If you measure confidence, say so, and don’t describe it as skill.
Tests need room to improve. If most people score near the top before the session, the test can’t show improvement. Check your pre-test scores early.
Self-made tests need evidence. A test you wrote should be checked by content experts against what you taught, and piloted before use. Read the guide
Timing matters. A test right after the session measures short-term recall. A delayed follow-up shows whether learning lasted.
Linking matters. Paired analyses need each person’s pre and post responses linked. If responses must be anonymous, a self-generated ID code can keep them linkable. Read the guide
Before you start
- You’ve decided which outcome levels you’ll measure, and your claims will match them.
- Your instrument covers what you’ll teach and has room to show improvement.
- You’ll use the same instrument, the same way, before and after.
- You can link each person’s pre and post responses, or you’ve planned your analysis for unlinked data.
- You’ve checked that your program accepts education-focused projects with the outcomes you plan. Some programs don’t accept projects whose only outcome is participants’ knowledge.
Related guides
- Choosing a statistical approach for your DNP project
- DNP project types explained
- Sample size and power for small DNP projects
- Valid, feasible, relevant: Choosing outcome measures
- Analyzing Likert data: Items vs scale scores
- Can your outcome change in time?
- Did the intervention actually happen? Measuring fidelity
- Do you have baseline data? Testing your problem statement
- Finding a validated instrument and getting permission
- Paired t-test or Wilcoxon? Analyzing pre-post data
- Planning for dropout and missing data
- Pre-post surveys: Same instrument, linked responses
- Setting up your project data
- Statistical vs clinical significance
- The proxy trap: When easy data measures the wrong thing
Not sure which type fits? Find your project type