Joc Data Analysis

Data Analysis (joc-data-analysis)

JoC reviewers are methodologically sophisticated, and the journal requires a Data Availability Statement so others can scrutinize how your numbers were produced (see joc-open-science-and-transparency). Analyze as if both are true — because they are. This skill covers execution and reporting norms; design decisions live in joc-research-design.

When to trigger

• Running main and supporting analyses; building the results section
• A reviewer asked for robustness, heterogeneity, or alternative specifications
• Reconciling preregistered vs. exploratory analyses
• Making the analysis reproducible before deposit

Analysis norms JoC expects

1. Report uncertainty honestly. Confidence/credible intervals and effect sizes, not just stars or p-values; the substantive magnitude and meaning of the estimate.
2. Robustness that probes, not decorates. Show specifications that could break the result (alternative measures, samples, estimators, covariate sets), and say what you learn.
3. Heterogeneity with discipline. Pre-specify subgroups where possible; correct for multiple comparisons; do not mine for a significant interaction and theorize it post hoc.
4. Mediation/moderation done right. For PROCESS/SEM-style models, justify the causal ordering; report indirect effects with bootstrap CIs; acknowledge cross-sectional limits on process claims.
5. Measurement and reliability. Report scale reliability (e.g., alpha/omega) and, for content analysis, intercoder reliability; show results are not an artifact of a coding/scaling choice.
6. Preregistration discipline. Clearly separate registered from exploratory analyses; reconcile and justify deviations from the plan.

Computational / text-as-data specifics

• Document model/version, hyperparameters, seeds, and validation against human-labeled samples.
• For topic models/embeddings/LLM pipelines: report stability and a validation step; don't treat outputs as ground truth.

Reproducibility while you work (not at the end)

• One master script regenerates every table and figure from the (raw or constructed) data.
• Set and report seeds for bootstrap, simulation, and any stochastic step.
• Pin software/package versions (renv.lock, requirements.txt, recorded installs; note Mplus/SPSS versions).
• Keep table/figure numbers in the manuscript matched to script outputs.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. Journal of Communication spans experiments, surveys, and content analysis; randomization inference for experiments, DiD/IV for observational media-effects claims.

• Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg — report the adjusted threshold.
• OVB sensitivity: oster_delta / sensemakr.
• Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley; multilevel data → cluster at the right level.
• Re-fit off one handle: audit_result(result_id) lists the missing checks and the exact suggest_function for each.
• Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive battery in the supplement. See the executed chain in the JF execution walkthrough.

Anti-patterns

• Stars-only tables with no effect sizes or intervals
• "Robustness" that only reruns near-identical specs to manufacture stability
• p-hacking / fishing for a significant interaction; HARKing exploratory results into hypotheses
• Reporting a content analysis without intercoder reliability
• A results section whose numbers the code cannot reproduce

Evidence pass for Journal of Communication

Treat this skill as an executable review pass, not a prose hint. First lock the communication process, platform/media setting, construct measurement, and study design; then judge whether the current manuscript answers the venue's real reader: communication reviewers who balance theory, media context, measurement, and social implications.

• Do the pass: Audit the research design before polishing prose: unit of analysis, comparison set, uncertainty, sensitivity, missingness, and reproducibility must be visible.
• Return a ledger: give claim / evidence / risk / manuscript location rows, so the next agent can edit rather than rediscover the issue.
• Sibling guard: compare against Communication Research for quantitative communication, New Media & Society for platform focus, Human Communication Research for theory testing; if a sibling owns the contribution, recommend re-routing before polishing format.
• Stop condition: do not give submission-ready advice until the pack's resources/official-source-map.md has been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.

Output format

【Main estimate】magnitude + interval + substantive meaning
【Identification/validity check】(per research-design) result
【Robustness】specs that could break it → what held
【Heterogeneity】pre-specified? MHT-adjusted?
【Reliability】scale / intercoder reliability reported?
【Registered vs exploratory】clearly separated?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】joc-tables-figures

Supplementary resources

• ../../resources/external_tools.md — estimation, reliability, mediation/SEM, and text-as-data packages
• ../../resources/official-source-map.md — Data Availability Statement requirement

Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-Communication-Skills/skills/joc-data-analysis/SKILL.md