| name | smr-simulation-studies |
| description | Use when designing the Monte Carlo simulation study for a Sociological Methods & Research (SMR) paper — data-generating processes, competing methods, performance metrics, and the regimes where the method wins or breaks. Designs the simulation; does not derive properties or run the real-data illustration. |
SMR Simulation Studies
Use this to build the Monte Carlo that an SMR reviewer will trust. At a methods journal the
simulation is not a formality — it is the primary evidence that the analytical properties hold in
finite samples and that the method beats real competitors. A weak or self-serving simulation sinks
otherwise sound papers.
Design the DGP space deliberately
Reviewers attack the data-generating process first. Specify it as a designed experiment, not a
convenient example:
- Factors and levels: sample size (and, for panels/networks, the relevant dimensions), the
parameter that controls the difficulty (effect size, dependence, missingness rate, sparsity), and
any nuisance complications. State why each level is realistic for sociological data.
- Coverage of the assumption boundary: include cells where your own assumptions fail, so the
paper shows the method's limits, not just its triumphs. SMR rewards honesty about breakdown.
- Calibration to the application: at least one DGP should be calibrated to the real dataset in
smr-empirical-illustration, so the simulation speaks to a setting readers care about.
- Replications and seeds: enough Monte Carlo replications for stable estimates of the metrics, with
seeds fixed and reported for reproducibility.
The competitor set (non-negotiable)
A simulation that compares the new method only to a naive baseline is the classic reject. Include:
- The current default practitioners actually use.
- The strongest existing alternative for the same problem (often from a neighboring discipline —
see
smr-literature-positioning).
- Where relevant, an oracle / infeasible benchmark to show the gap your method closes.
If your method loses to a competitor in some cell, report it and explain when each method is
preferable — conditional recommendations are more credible than universal victory.
Metrics that match the claim
| Claim type | Report | Common SMR pitfall |
|---|
| Point estimation | bias, RMSE, relative efficiency | reporting bias but hiding variance |
| Inference / testing | empirical size, power, CI coverage and width | "performs well" with no coverage number |
| Selection / classification | accuracy + the costs of each error | accuracy only, ignoring imbalance |
| Computation | runtime, scaling, convergence rate | feasibility claim with no timing |
Coverage and size near the nominal level are the metrics SMR reviewers scrutinize most for inference
methods — report the actual numbers, not adjectives.
Presenting the study compactly
- Summarize the full grid in a table or a small-multiples figure; do not narrate every cell.
- Lead with the cell that makes the contribution's point (where the incumbent breaks and the method
holds), then show the boundary where the method itself degrades.
- Hand the exhibit design to
smr-tables-figures so the grid is self-contained and readable in print.
Checklist
Anti-patterns
- Strawman comparison: only a naive baseline, never the real competitor.
- Sunny-cell selection: showing only regimes that favor the method.
- Adjective metrics: "good size control" with no rejection rates.
- Cherry-picked n: one favorable sample size with no scaling pattern.
- Uncalibrated fantasy DGP: a design unrelated to any sociological data.
- Hidden seeds / replication count: results that cannot be reproduced.
Output format
[Simulation status] convincing / needs repair / not ready
[DGP factors] <factor : levels, with realism note>
[Competitor set] <default + strongest alternative (+ oracle)>
[Metrics] <bias/RMSE/coverage/size/power/runtime as claimed>
[Boundary cell] <where the method degrades and why that is honest>
[Next SMR skill] smr-empirical-illustration
