smr-simulation-studies
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill smr-simulation-studies --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 repl
- Design the DGP space deliberately
- The competitor set (non-negotiable)
- Metrics that match the claim
- Presenting the study compactly
- Checklist
- Anti-patterns
- Output format
What does the smr-simulation-studies skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill smr-simulation-studies --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.