Commres Data Analysis

Data Analysis (commres-data-analysis)

CR reviewers are quantitatively sophisticated, and the journal expects APA-style statistical reporting (effect sizes and standard deviations, not stars alone). Analyze as if your numbers will be scrutinized — because they will. This skill covers execution and reporting norms; design decisions live in commres-research-design, and deposit live in commres-transparency-and-data.

When to trigger

• Running main and supporting analyses; building the Results section
• A reviewer asked for robustness, an alternative specification, or a mediation re-analysis
• Reconciling preregistered vs. exploratory analyses
• Making the analysis reproducible before deposit

Analysis norms CR expects

1. APA statistical reporting. Report effect sizes (d, η²ₚ, R², standardized β) and dispersion (SDs, CIs), test statistics with df, and exact p where feasible — not significance stars alone.
2. Right model for the design. ANOVA/ANCOVA for factorial experiments; OLS/logistic regression with proper controls; SEM/CFA for latent constructs; multilevel models for nested data (e.g., messages within participants, students within classrooms).
3. Mediation/moderation done right. For PROCESS/SEM models, justify the causal ordering; report indirect effects with bootstrap CIs; for moderated mediation report the index and conditional indirect effects; acknowledge cross-sectional limits on process claims.
4. Report uncertainty honestly. Confidence intervals and effect magnitudes; interpret the substantive meaning of the estimate, not just whether it crossed .05.
5. Robustness that probes, not decorates. Show specifications that could break the result (alternative measures, covariate sets, estimators, exclusions), and say what you learn.
6. Measurement and reliability. Report scale reliability (alpha/omega) and, for content analysis, intercoder reliability; show results are not an artifact of a coding/scaling choice.
7. Preregistration discipline. Clearly separate confirmatory (registered) from exploratory analyses; reconcile and justify deviations; correct for multiple comparisons where you test many.

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; note SPSS/Mplus/PROCESS 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. Communication Research is experiment- and survey-heavy; emphasize randomization inference, mediation done right, and family-wise corrections.

• 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.