Joc Research Design

Research Design (joc-research-design)

JoC accepts many methodologies but is demanding about each. The design must credibly connect the argument (joc-theory-building) to evidence. This skill is mode-aware: pick the section that matches your work and defend it against the strongest alternative explanation.

When to trigger

• Specifying an experiment, survey, content-analysis protocol, computational pipeline, or fieldwork plan
• 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 joc-literature-positioning

Experiments (lab / online / survey / field)

• Preregister the design and primary analyses; report a-priori power / MDE; pre-specify subgroups.
• Treatment realism and ecological validity; manipulation and attention checks; attrition.
• Stimuli sampling: treat messages as a sample, not a fixture (consider stimulus-as-random-factor).
• 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; guard against common-method variance.
• For cross-sectional mediation, be explicit about causal limits; prefer panel/experimental designs for process claims.

Content analysis

• A documented codebook; trained coders; report intercoder reliability (Krippendorff's alpha or equivalent) on an adequate subsample, and the unit of analysis.
• Sampling of texts justified (timeframe, sources); construct validity of categories.

Computational / text-as-data

• 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.
• Address platform/ToS and ethics for collected data.

Qualitative / critical

• Justify case/site/text selection by design logic, not convenience; say what it is a case of.
• Trustworthiness: reflexivity, audit trail, transparent coding; state what evidence would complicate the reading.

The adjudication test (JoC-specific)

For the single strongest rival explanation, write one sentence: "If the rival were true rather than my argument, 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 JoC-specific fix

JoC referees at the ICA flagship rarely reject on a single statistic; they reject when the design cannot bear the theoretical weight the paper puts on it. The recurring objections and their venue-specific repairs:

Worked micro-example: framing survey-experiment design (illustrative)

A planned study claims that gain- vs. loss-framed vaccine messages change intention via perceived risk. A JoC-defensible design: 2 (frame) × 3 (message exemplars per frame) factorial so the frame effect is estimated across six distinct texts, not one — defeating the single-message confound. Target N ≈ 900 (illustrative; size to the registered MDE), preregister the mediation path frame → perceived risk → intention with bootstrap CIs, and add an attention check plus a behavioral exposure proxy. The adjudication sentence writes itself: if the rival "any health message moves intention" were true, the gain/loss contrast would be null while overall intention rose; instead the contrast is non-null and runs through perceived risk — advancing framing theory rather than re-documenting a persuasion effect.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe 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.

• 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
• Automated text measures used without human validation
• Convenience case/stimulus selection dressed up as theory-driven
• A single-message stimulus carrying a claim about a message feature
• A design that cannot distinguish your argument from the leading alternative

Output format

【Mode】experiment / survey / content-analysis / computational / qualitative
【Estimand or claim】what is being identified/shown
【Key assumption(s)】and how each is defended (incl. reliability/validity)
【Rival ruled out】the adjudication sentence
【Robustness/sensitivity】planned checks
【Next】joc-data-analysis

Supplementary resources

• ../../resources/external_tools.md — design, reliability, and text-as-data packages (R/SPSS/Mplus/Python) and CAQDAS for qualitative work
• ../../resources/official-source-map.md — preregistration and Open Science Badge notes