Use when executing and reporting the analysis for a Demography (PAA / Duke University Press) manuscript so it survives expert, double-blind review — correct rate construction, honest uncertainty, and demographic methods done right (life tables, decomposition, event history, age-period-cohort). Guides analysis norms; it does not fabricate results.
Scanned 6/5/2026
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---
name: demog-data-analysis
description: Use when executing and reporting the analysis for a Demography (PAA / Duke University Press) manuscript so it survives expert, double-blind review — correct rate construction, honest uncertainty, and demographic methods done right (life tables, decomposition, event history, age-period-cohort). Guides analysis norms; it does not fabricate results.
---
# Data Analysis (demog-data-analysis)
Demography reviewers are expert demographers and the journal expects **reproducible code** behind the
results (see `demog-data-and-reproducibility`). Analyze as if a methodologist will re-derive your rates
and re-run your decomposition — because they may. This skill covers execution and reporting norms;
method choice lives in `demog-research-design`.
## When to trigger
- Constructing rates and life tables; building the results section
- Running a decomposition, event-history, APC, or projection analysis
- A reviewer asked for robustness, sensitivity, or alternative specifications
- Making the analysis reproducible before deposit
## Analysis norms Demography expects
1. **Get the denominators right.** Exposure (person-years), the correct base population, and
age/period alignment are where demographic analyses live or die. Document how rates were built.
2. **Report uncertainty honestly.** Confidence/credible intervals for rates, life-expectancy
contributions, and derived quantities — not just point estimates or stars. Bootstrap or delta-method
intervals for decomposition components and life-table functions.
3. **Decomposition with clear components.** State precisely what each component (rate vs. composition,
age contribution, factor) represents; ensure components sum to the total being explained.
4. **APC discipline.** Be explicit about the identification problem; report results under the stated
constraint and show sensitivity to plausible alternatives — never imply a unique decomposition.
5. **Survival/event-history rigor.** Check proportional hazards; handle censoring, truncation, and
competing risks correctly; report on the right time scale (age, duration, period).
6. **Right inference for the data.** Survey weights and complex-design variance where applicable;
cluster at the appropriate level; small-sample corrections when groups are few.
## Demographic computation specifics
- Document data version/vintage (e.g., HMD/HFD release), harmonization steps, and any smoothing/
graduation applied to rates.
- For microsimulation/projection: report seeds, number of runs, and convergence; show sensitivity to
the key transition-rate and base-population assumptions.
## Reproducibility while you work (not at the end)
- One **master script** regenerates every table, figure, life table, and decomposition from the
(raw or constructed) data.
- **Set and report seeds** for bootstrap, simulation, and microsimulation.
- Pin software/package versions (`renv.lock`, `requirements.txt`, recorded `ssc`/`net` installs).
- Keep table/figure numbers in the manuscript matched to script outputs (Demography expects runnable,
commented code — see `demog-data-and-reproducibility`).
## Anti-patterns
- Mismatched numerator/denominator or wrong exposure (the classic demographic error)
- Point estimates of life expectancy or decomposition components with no uncertainty
- An APC model presented as the uniquely correct partition
- Ignoring censoring/competing risks in survival analysis
- A results section whose rates and decompositions the code cannot reproduce
## Output format
```
【Main quantity】rate / e0 / decomposition / hazard + magnitude + interval
【Exposure / denominator check】correctly constructed? [Y/N]
【Decomposition】components defined + sum to total? [Y/N/NA]
【APC】identifying constraint stated + sensitivity shown? [Y/N/NA]
【Inference】weights/clustering/competing risks handled? [Y/N]
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】demog-tables-figures
```
## Supplementary resources
- [`../../resources/external_tools.md`](../../resources/external_tools.md) — life-table, decomposition, survival, APC, and simulation packages
- [`../../resources/official-source-map.md`](../../resources/official-source-map.md) — data-availability and reproducible-code expectations
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