open-science-skills

Apply conjoint data cleaning expertise to the task below. Cover the relevant aspects of Qualtrics column conventions, wide-to-long reshaping, choice variable mapping, attribute translation, pilot data detection, reference category selection, and validation based on what is needed.

Apply conjoint experiment design expertise to the task below. Cover the relevant aspects of attribute architecture, statistical power, AMCE/AMIE estimation, design variants, and quality checks based on what is needed.

Run a conjoint diagnostics review of the task or study described below. Evaluate design integrity, estimation choices, measurement error, external validity, and interpretation against the diagnostic checklist.

Apply cross-national survey experiment design expertise to the task below. Cover per-country power, measurement equivalence, sensitivity bias auditing, instrument localization, and multi-country estimation as relevant.

Apply causal hypothesis architecture expertise to the task below. Cover falsifiability, counterfactuals, DAGs, FPCI, three-level hypothesis specification, equivalence testing, and SESOI as relevant.

Specialized logic for cleaning and reshaping choice-based conjoint data from Qualtrics exports into analysis-ready long format. Use when (1) preparing conjoint survey data for analysis, (2) reshaping wide Qualtrics exports to long format, (3) mapping conjoint choice and rating variables to profile-level outcomes, (4) translating attribute labels across languages, (5) diagnosing pilot contamination or data quality issues in conjoint data, or (6) setting AMCE reference categories. Covers Qualtrics column conventions, existing R packages, wide-to-long reshaping, choice variable encoding, attribute-level translation, data validation, and analysis-ready output.

Specialized logic for designing conjoint and factorial vignette experiments. Use when (1) designing a new conjoint experiment, (2) selecting and structuring attributes and levels, (3) conducting a conjoint power analysis, (4) choosing between design variants (paired-choice, rating, factorial vignette), (5) writing conjoint regression specifications, or (6) drafting the conjoint portion of a pre-analysis plan. Covers attribute architecture, AMCE/MM estimation, interaction effects, power formulas, treatment validation, and design variants.

Systematic diagnostic checklist for evaluating choice-based conjoint experiments. Use when (1) reviewing a conjoint paper or manuscript, (2) auditing a conjoint analysis script or dataset, (3) assessing measurement error and IRR in conjoint data, (4) evaluating external validity of a conjoint design, or (5) checking interpretation of AMCEs, marginal means, and interaction effects. Covers design, estimation, measurement error correction, external validity, and reporting.

Guides the design of cross-national comparative survey experiments. Use when (1) selecting countries for a multi-country study, (2) localizing experimental instruments across languages and institutional contexts, (3) calibrating origin-country stimuli for immigration experiments, (4) conducting per-country power analyses, or (5) planning a cross-national analytical strategy with pooled and per-country models. Covers case selection, instrument localization, ecological validity, power management, and sensitivity bias auditing.

Guides the transformation of theoretical concepts into falsifiable, counterfactual-based hypotheses with formal estimands. Use when (1) drafting hypotheses for a pre-analysis plan, (2) specifying estimands and linking them to regression models, (3) choosing between NHST, equivalence, and minimum-effect tests, (4) structuring a multi-experiment hypothesis architecture, or (5) classifying hypotheses as primary, secondary, or exploratory. Ensures every claim has a named estimand, a SESOI, and a three-level specification (conceptual, operationalized, statistical).