| name | commres-research-design |
| description | 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. |
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 operationalizes H2/H3; for causal mediation,
prefer manipulating the mediator or a measurement-of-mediation design with stated assumptions.
- Ethics/IRB and informed consent; debrief where deception is used.
Surveys / panels
- Sampling frame, mode, and generalization claims; weighting where appropriate.
- Validated multi-item measures; report reliability (alpha/omega) and, ideally, a CFA/measurement
model; guard against common-method variance (procedural and statistical remedies).
- For process claims, prefer panel (lagged) or experimental leverage — cross-sectional mediation
cannot license a temporal/causal story; say so plainly if you are cross-sectional.
Content analysis
- A documented codebook; trained coders; report intercoder reliability (Krippendorff's alpha
or equivalent) on an adequate subsample, and the unit of analysis.
- Justify text sampling (timeframe, sources); establish construct validity of categories, not just
reliability — reliable coding of the wrong construct is still wrong.
Computational / text-as-data (when hypothesis-testing)
- Validate automated measures against human-coded gold-standard samples; report agreement.
- Document model/version, hyperparameters, seeds; report stability; do not treat outputs as ground truth.
The adjudication test (CR-specific)
For the single strongest rival explanation, write one sentence: "If the rival were true rather
than my hypothesis, the data would look like ___; instead they look like ___." If you cannot, the
design does not yet identify the contribution.
Reviewer-pushback patterns and the CR-specific fix
| Reviewer objection | Why it lands at CR | Design-stage fix |
|---|
| "Single-message confound" | one stimulus cannot separate the message feature from the text | sample multiple messages per condition; model message as a random factor |
| "Measurement validity unclear" | a scale or coded category may not be the construct | report CFA / construct validity, not just reliability |
| "Common-method variance" | same-survey predictor and outcome inflate the path | procedural separation + a statistical CMV check |
| "Cross-sectional process claim" | mediation on one wave cannot license a causal story | move to an experiment/panel, or hedge the claim |
| "Effect without mechanism" | a main effect alone does not advance theory | measure and pre-specify the mediator/moderator before collection |
Worked micro-example: framing survey-experiment design (illustrative)
A study claims gain- vs. loss-framed vaccine messages change intention via perceived
response-efficacy, moderated by prior knowledge. A CR-defensible design: 2 (frame) × 3 (message
exemplars per frame) factorial so the frame effect is estimated across six texts — defeating the
single-message confound. Validated multi-item efficacy and intention scales (report alpha + a CFA),
target N sized to the registered MDE, preregister the moderated-mediation model (frame →
response-efficacy → intention, moderated by knowledge) with bootstrap CIs, plus an attention check.
The adjudication sentence: if "any health message moves intention" were true, the gain/loss contrast
would be null while overall intention rose; instead the contrast runs through efficacy and only for
low-knowledge audiences — advancing framing theory rather than re-documenting persuasion.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. Communication Research is experiment- and survey-heavy; emphasize randomization inference, mediation done right, and family-wise corrections.
detect_design → recommend → fit with as_handle=true → audit_result.- Observational causal claims: staggered DiD (
callaway_santanna / sun_abraham +
bacon_decomposition + honest_did_from_result); IV (effective_f_test +
anderson_rubin_ci); RDD (rdrobust + mccrary_test).
- Experiments: randomization-based inference,
romano_wolf for many-outcome
family-wise control, and mediate for mediation (not naive controlling-away).
- Sensitivity:
oster_delta / sensemakr for observational claims.
Report the effect size in interpretable units; route the full battery to the
appendix/supplement. A run end-to-end (synthetic data, real returns) is in the
JF execution walkthrough.
Anti-patterns
- Causal language on a cross-sectional survey that only supports association
- Content analysis with no reported intercoder reliability or an unstated unit of analysis
- A single-message stimulus carrying a claim about a message feature
- Scales used with no reliability/validity evidence; ignoring common-method variance
- A design that cannot distinguish your hypothesis from the leading alternative
Output format
【Mode】experiment / survey-panel / content-analysis / computational
【Estimand or claim】what is being identified/tested
【Key assumption(s)】and how each is defended (incl. reliability/validity, CMV)
【Rival ruled out】the adjudication sentence
【Mediation/moderation】design supports the causal ordering? [Y/N]
【Robustness/sensitivity】planned checks
【Next】commres-data-analysis
Supplementary resources
