Spq research design

Research Design (spq-research-design)

SPQ accepts experiments, surveys, and observational/interpretive work, but is demanding about each. The design must credibly connect the social-psychological argument (spq-theory-building) to evidence about the structure–individual link. This skill is tradition-aware: pick the section that matches your work and defend it against the strongest alternative explanation.

When to trigger

• Specifying an experimental setting, a survey/measurement plan, or a fieldwork/interview design
• A reviewer questioned causal claims, construct validity, case/site selection, or a confound
• Justifying why your design adjudicates the rival account from spq-literature-positioning
• Deciding how the design operationalizes the social-psychological mechanism

Experimental (group processes, status, exchange — the lab tradition)

• Standardized experimental settings. State the setting (e.g., status/expectation-states paradigm, exchange networks) and how the manipulation realizes the theoretical construct.
• Manipulation / standardized-setting checks; randomization; attention checks; attrition.
• Inference: pre-specify primary outcomes; correct for multiple comparisons; power/MDE; appropriate models for nested (group/dyad) data.
• Generalization: be explicit about what a lab effect does and does not license about real settings.

Survey / secondary-data (social structure and personality)

• Measurement first. Validate the social-psychological constructs (identity salience, mastery, status, sentiment); report reliability; show results aren't an artifact of a scaling choice.
• Structural variables measured and theorized, not just controls — the structure–individual link is the point.
• Inference for complex designs: survey weights/clustering for GSS/PSID-type data; multilevel models for individuals nested in contexts; sensitivity to unobserved confounding for any causal claim.

Observational / interpretive (symbolic interaction)

• Site / case selection justified by analytic logic (what is this a case of?), not convenience.
• Evidence and disconfirmation: state what observations would have challenged the analytic claim; document how interaction, accounts, or fieldnotes support it.
• Reflexivity and access: position of the researcher, consent, and how meaning is interpreted.

The adjudication test (SPQ-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 ___." For experiments this is the manipulation contrast; for surveys, the confound ruled out; for interpretive work, the alternative reading. If you cannot, the design does not yet identify the contribution.

Design stress ledger

Use a design stress ledger before committing to the analysis plan:

Tradition	Stress test
Experiment	What manipulation failure, demand effect, group-composition imbalance, or dyadic dependence would overturn the status/process claim?
Survey / secondary data	Which omitted structural variable, measurement-invariance failure, weighting choice, or contextual clustering rule could flip the conclusion?
Observational / interpretive	Which negative case, deviant interaction, or access/reflexivity concern would force a narrower interpretation?

For each row, write the planned diagnostic and the interpretation if it fails. SPQ reviewers are comfortable with different methods, but they expect the method's limits to be explicit. A design that names its own failure mode usually reads stronger than a design that implies no failure mode exists.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. SPQ spans lab/survey experiments and observational work; randomization inference and mediation done right matter for the experimental lane.

• 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

• A lab effect over-generalized to real-world structure with no caveat
• Treating structural variables as nuisance controls rather than theorized causes
• Convenience site selection dressed up as theory-driven
• "Causal" language on a cross-sectional survey that only supports association
• A design that cannot distinguish your social-psychological mechanism from the leading alternative

Output format

【Tradition】experiment / survey-SSP / observation-interpretive
【Estimand or analytic claim】what is identified/shown about the structure–individual link
【Key assumption(s)】and how each is defended
【Rival ruled out】the adjudication sentence
【Measurement / setting validity】constructs validated or setting standardized? [Y/N]
【Robustness/sensitivity】planned checks
【Next】spq-data-analysis

Supplementary resources

• ../../resources/external_tools.md — experimental, survey, measurement, and CAQDAS tooling by tradition
• ../../resources/official-source-map.md — SPQ scope and methods breadth