How Do We Combine Many Studies Into One Answer?
Experimental Design domain · Lesson 17 of 20 · Biomedical Innovations (BI)
Today's goal: Explain how a systematic review pools studies using PRISMA, read a PRISMA flow diagram, and state when studies are too different to combine (heterogeneity).
What a finished product looks like
This is a model of the work you should turn in. Use it to check your own: match the structure and the level of detail, do not copy it. Your wording should be your own.
1. Pool the 3 studies that used the same validated speech test; keep the 1 home-made-test study separate, because pooling a different outcome definition would average apples and oranges and inflate heterogeneity.
2. The '?' becomes 3: only the 3 matched-test studies go into the meta-analysis.
3. Advice to the PI: The review can honestly conclude only about the 3 comparable studies; the odd-test study should be described narratively, not folded into the pooled number.
How this was built, step by step
The finished product above did not appear all at once. Here is the path from the question to the turned-in work, so you can follow the same steps.
- 1Start from today's question: How do we combine many separate studies into one trustworthy answer, without cherry-picking?
- 2Work the Model and the Explore questions to reason it out before writing anything.
- 3Pull the specific evidence the product needs from the reading and any database you used.
- 4Write it up in the required format: Use the flow diagram. Of the 4 included studies, suppose 3 measured speech with the same validated test and 1 used a completely different home-made test. As Data Scientist: (1) which studies could you reasonably pool, and which one would you keep separate, and why? (2) fill the '?' in the diagram: how many studies go into the meta-analysis under your decision? (3) write one sentence telling the PI what the review can honestly conclude.
- 5Check it against the rubric, then submit.
| Criterion | Proficient | Developing | Beginning |
|---|---|---|---|
| Complete | Every required part of the artifact is present and filled in. | Most parts are present, but one is missing or left blank. | Several parts are missing. |
| Accurate | The science and data are correct and match the evidence. | Mostly correct, with a small factual slip. | Key science or data is wrong. |
| Scientific reasoning (CER) | States a claim, backs it with specific evidence, and explains the reasoning. | Has a claim and evidence, but the reasoning is thin or missing. | Gives an answer with no evidence or reasoning. |
| Professional communication | Clear, organized, and labeled the way a clinician or scientist would write it. | Readable but disorganized or missing labels. | Hard to follow. |
| Submitted | Turned in the right way (Schoology for routine work) and confirmed. | Turned in, but in the wrong place or unconfirmed. | Not turned in. |
- CompleteProficient: Nothing is left blank: the model fills every part of "Use the flow diagram. Of the 4 included studies, suppose 3 measured speech with the same validated test and 1 used a completely different home-made test. As Data Scientist: (1) which studies could you reasonably pool, and which one would you keep separate, and why? (2) fill the '?' in the diagram: how many studies go into the meta-analysis under your decision? (3) write one sentence telling the PI what the review can honestly conclude.".
- AccurateProficient: Every number and claim matches the case evidence.
- Scientific reasoning (CER)Proficient: It names a claim, cites the specific evidence, and explains the reasoning, not just the answer.
- Professional communicationProficient: It is organized and labeled like a real chart note.
- SubmittedProficient: It would be turned in on Schoology and confirmed.
WebXam problem for today's skill
One exam-style question that uses exactly what you practiced today. Try it before you reveal the answer, then read why each choice is right or wrong.
Tap an answer to see the full explanation. Nothing is recorded or graded.