| name | spq-research-design |
| description | Use when defending the research design of a Social Psychology Quarterly (SPQ) manuscript — laboratory and survey experiments (group processes, status), survey and secondary-data analysis (social structure and personality), or observation/ethnography and interviews (symbolic interaction). SPQ judges each tradition on its own terms but always asks how the design captures the link between structure and the individual. Strengthens the design; it does not write code. |
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
