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Research Design and Study Planning Questions

End to end planning and design of research studies to rigorously answer product, user experience, or scientific questions. Candidates should be able to translate business or product problems into clear and testable research questions and hypotheses and convert those questions into feasible and valid study plans. Core skills include selecting appropriate qualitative, quantitative, or mixed methods, defining primary outcomes and success metrics, aligning sampling strategy and inclusion and exclusion criteria, estimating sample sizes and articulating precision and power considerations, designing recruitment approaches and consent procedures, drafting interview guides survey items and measurement instruments with attention to reliability and validity, planning data collection workflows and quality controls, and outlining statistical and qualitative analysis plans and integration strategies for mixed methods. Candidates should also be able to identify potential confounds and threats to internal and external validity and propose mitigation approaches, scope studies to remain feasible under time and resource constraints, plan logistics timelines and resource allocation, pilot and iterate instruments, address ethical and regulatory requirements such as institutional review board review and data privacy, and communicate research plans limitations and actionable findings to stakeholders. Interviewers may probe trade offs among methodologies bias mitigation strategies reproducibility and documentation practices how the candidate managed scope and stakeholder expectations and how preliminary findings or stakeholder input influenced the evolution of research questions and study scope while avoiding scope creep.

HardTechnical
56 practiced
Your team proposes using a 7-day engagement metric as a surrogate endpoint for 6-month retention to get faster feedback on experiments. Outline criteria and statistical approaches to validate whether the 7-day metric is an acceptable surrogate: discuss Prentice-type criteria, proportion of treatment effect explained, mediation analysis, external validation datasets, and sensitivity analyses. What are the risks of acting on surrogate outcomes?
EasyTechnical
43 practiced
Design a pilot study plan for a new 20-question customer experience survey that will be used in a large product rollout. Include objectives of the pilot, sample size rationale (including cognitive interviews), recruitment approach, timeline, metrics to monitor (e.g., item nonresponse, time to complete, item distribution, ceiling/floor effects), and criteria for instrument revision before full launch.
HardTechnical
43 practiced
Provide Python pseudocode to estimate an indirect (mediation) effect of A -> M -> Y and compute a bootstrap 95% confidence interval for the indirect effect. Data are clustered by user_id and you must account for clustering in bootstrap resampling. Describe how you would implement cluster bootstrap (resampling clusters), choose number of resamples, parallelize computation, and interpret results when cluster sizes vary.
EasyTechnical
40 practiced
Your product team reports a 7% monthly churn and asks whether changes to the onboarding flow could reduce churn. Translate this business need into 2–3 clear, testable research questions and corresponding null/alternative hypotheses. For each hypothesis specify: the primary outcome (exact operational definition and units), study population (inclusion/exclusion), measurement timeframe, one key confounder to control for, and whether the analysis will use intent-to-treat or per-protocol population. Assume you have 8 weeks and existing event instrumentation.
HardTechnical
48 practiced
Your qualitative interviews identify three user personas, but quantitative clustering on usage metrics yields only two clusters. Design a rigorous mixed-methods integration plan to reconcile this discrepancy: propose additional data collection (who, how many), analytic techniques to link qualitative themes to quantitative features (e.g., supervised coding, latent class analysis), and explain how you would present integrated findings to product teams to inform prioritization.

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