Worksheet

Mixed‑Methods Integration Worksheet

An actionable, savable worksheet that helps teams plan, align, and document qualitative and quantitative evidence for a single discovery question. Prompts and structured fields guide method choices, sampling, ethics, analysis plans, integration design, conflict resolution, timeline, and a worked example.

Interactive Tool

Mixed‑Methods Integration Worksheet

This worksheet helps teams plan and align qualitative and quantitative work so findings can be integrated into clearer, more credible discovery evidence. Use the prompts to capture purpose, methods, sampling and analysis plans, how you will integrate results, potential conflicts, and a short timeline. Save this form to retain a documented plan you can iterate on.

Write the specific question you want this mixed-methods study to answer. Make it actionable: who, what, outcome, context. (Required)
What decision, experiment, product change, or policy will this evidence inform? Who are the primary decision-makers or stakeholders?
List the teams, roles, or external stakeholders who will use these findings.
Describe the qualitative approach (e.g., semi-structured interviews, contextual inquiry, diary study), key topics to explore, and what nuance you expect to capture.
Summarize the interview guide or observation checklist. Note main prompts, probes, and any materials you'll show participants.
Who will you recruit, why they matter, inclusion/exclusion criteria, recruitment channels, and target sample size (justify saturation approach).
Have you prepared consent language, confidentiality rules, and a data handling plan for qualitative data?
Describe the quantitative approach (e.g., event analytics, A/B test, survey), the key metrics, and what measurable signal you expect to observe.
List data sources (logs, analytics, CRM, sensor data), owners, access status, and any sampling or extraction notes.
Define each metric precisely (what counts as an event, numerator/denominator, time windows) and the thresholds for meaningful change.
Describe sample frame, sampling approach, expected sample size, and any power calculations or minimum detectable effects you considered.
Outline analysis methods (segmentation, regression, time-series, significance tests), covariates, pre-registration notes, and robustness checks.
Choose how you'll combine evidence. Typical designs: converge both simultaneously, or sequential explanatory/exploratory. Pick the model that best fits the question.
Describe how you'll weigh and combine evidence. Examples: convergence = consistent themes + significant metric change; divergence = additional probing or prioritized follow-up evidence. Be explicit about thresholds for action.
List likely tensions (sampling mismatch, measurement error, social desirability, missing segments) and how they might affect conclusions.
Describe rules to resolve conflicts: additional data collection, reconciliation interviews, triangulation, pre-planned experiments, or analyst review panels.
Tick items you will complete before analysis begins.
Give key dates or durations for recruitment, data collection, analysis, integration, and decision checkpoints. Keep it realistic for the scope.
List expected outputs (report, slide deck, dashboard, decision memo), preferred formats, and where final data and artifacts will be stored.
Who will do the next task and what is it? Include an owner and due date externally if needed.
An example showing how interview insights and event-level analytics can be combined. Use it as a model for your own entry.
Anything else worth capturing about constraints, assumptions, known risks, or dependencies.
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