Agent skill · Content & Marketing

commres-research-design

Use when defending the research design of a Communication Research (CR) manuscript — experiments (lab/online), panel surveys, and content analysis with intercoder reliability. CR is quantitative and demanding about identification, measurement, and confound control. Strengthens the design; it does not write code.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill commres-research-design --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 7 KB
Bundled scripts: none
Path: Communication-Research-Skills/skills/commres-research-design/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Research Design (commres-research-design) CR is a **quantitative** journal and demanding about each design. The design must credibly connect the hypotheses (`commres-theory-building`) to evidence and defeat the strongest rival explanation. This skill is mode-aware — pick the section that matches your study and defend it on social-science terms. ## When to trigger - Specifying an experiment, panel survey, or content-analysis protocol - A reviewer questioned causal claims, sampling, coding reliability, validity, or a confound - Preparing a **preregistration** / pre-analysis plan (note it in the cover letter) - Justifying why your design adjudicates the rival account from `commres-literature-positioning` ## Experiments (lab / online / survey-embedded) - Preregister design and primary analyses; report **a-priori power / MDE**; pre-specify subgroups. - **Stimulus sampling**: treat messages as a sample, not a fixture — multiple exemplars per condition; model message as a random factor so the *feature* effect is not one text's idiosyncrasy. - Manipulation and attention checks; treatment realism; report and model attrition. - Pre-specify the **mediation/moderation** test that operational

What's inside
Steps it walks through
  1. When to trigger
  2. Experiments (lab / online / survey-embedded)
  3. Surveys / panels
  4. Content analysis
  5. Computational / text-as-data (when hypothesis-testing)
  6. The adjudication test (CR-specific)
  7. Reviewer-pushback patterns and the CR-specific fix
  8. Worked micro-example: framing survey-experiment design (illustrative)
  9. Execution bridge (StatsPAI / Stata MCP)
  10. Anti-patterns
  11. Output format
  12. Supplementary resources
More from Awesome-Journal-Skills
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About this skill
What does the commres-research-design skill do?

Use when defending the research design of a Communication Research (CR) manuscript — experiments (lab/online), panel surveys, and content analysis with intercoder reliability. CR is quantitative and demanding about identification, measurement, and confound control. Strengthens the design; it does not write code.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill commres-research-design --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.

Keep going