This lab introduces the essential principles of survey data analysis. Public health data often comes from complex surveys like NHANES, which don’t sample people randomly — they use a strategic design with weights, strata, and clusters so the sample represents the entire U.S. population. Ignoring these features can badly bias your results.
Using data from Flegal et al. (2016), you will work through the difference between a naive analysis and a methodologically sound survey analysis: creating a survey design object, generating weighted descriptive statistics, and fitting survey-weighted regression models to replicate published findings. Your TA will walk through the worked solution during the session.
This is the workflow you will reuse in P4 on your own locked paper.
Before you come to lab, install the package this exercise needs:
install.packages("survey")Follow along as the TA explains the worked solution.
Run the R Markdown file yourself, as a group, on your own laptop.
Put your group name and the first names of everyone
present in the author: field at the top of the
file.
Add sessionInfo() in a final code chunk.
Knit to PDF or HTML and submit that one file before the lab ends.
No written explanations are required. If you get stuck, ask the TA in the room — that is what the session is for.
You receive Complete if the document knitted without errors, your group name and members are in it, and it was submitted before the lab ended. Only members present in the room receive credit.
This lab is one step in a weekly chain: Tuesday’s lecture sets up the method, Thursday’s lab runs it on fixed teaching data, and your increment applies it to the paper your group locked at M0.
| Document | How this lab connects | |
|---|---|---|
| This week’s lecture | Week 6 — Complex survey design (weights, strata, PSU) | Tuesday showed why a complex survey is not a simple random sample, and what weights, strata and PSUs are actually doing. This lab makes the contrast concrete: a naive unweighted fit beside a design-aware one, with the attenuation and the CI widening tabulated side by side. |
| Slide deck | Week 6 deck | The same material as slides, with speaker notes. Press
S for notes, F for fullscreen. |
| Your increment | P4 — Design-aware estimate | Apply the same workflow to your paper’s headline estimate: declare and justify the weight you chose, report the naive-versus-weighted contrast, and write the one-line design statement your paper omitted. |
| Worked example | P4 example — its code | A completed P4: weight declared and justified, naive-versus-weighted contrast reported, and the one-line design statement written. |
| Milestone | M1 — Replication and interrogation | P4 enters M1 as Design-aware estimate. P4 is optional and ungraded with nothing to submit; M1 is where it is assessed. |