Mixed-Methods Research Planning Sheet

Mixed-Methods Research Planning Sheet

Use this template to plan a rigorous, usable mixed‑methods study that combines qualitative discovery with quantitative testing. Fill each section with clear, actionable details — the goal is evidence you can trust and hand off to teammates running instruments or analyzing results.

How to use this sheet

1. State concise research question(s) that connect to decisions or experiments.
2. Describe what you will measure (quantitative) and what you will explore (qualitative).
3. Plan sampling, instruments, and analysis handoffs so findings can be integrated quickly.
4. Record timeline, resources, and ethics to avoid rushed or unusable work.

1) Research question(s)

Prompt: What decision or hypothesis are you trying to inform? Keep it actionable.

Example: "How does Feature X affect onboarding completion within 7 days for new users?"

2) Quantitative hypotheses / outcomes

Prompt: List testable hypotheses or the primary outcome measures, including direction, unit, and expected effect where possible.

Example: "H1: Enabling Feature X increases 7‑day completion rate from 40% to 50% (absolute +10 pp). Primary metric: 7‑day onboarding completion rate."

3) Qualitative objectives

Prompt: Describe the specific insights you need from interviews, observations, or diaries (e.g., barriers, motivations, language people use).

Example: "Explore why users drop off during step 3 and what would motivate them to continue."

4) Mixed‑methods design choice

Prompt: Choose an overall approach and justify it briefly (e.g., convergent parallel, explanatory sequential, exploratory sequential, embedded). Explain timing of qual and quant relative to each other.

5) Sampling & recruitment plan

Prompt: Define target population, inclusion/exclusion criteria, sampling strategy for quantitative (random, stratified, convenience) and qualitative (purposive, maximum variation), and recruitment channels. Include target sample sizes and rationale or link to a power/sample‑size calculation.

Quick tip: For qualitative work, specify how you'll know you've reached sufficient depth (e.g., saturation criteria, target number per segment).

6) Instruments & protocols

Prompt: List surveys, experimental manipulations, interview guides, observation templates, and any pilot steps. Attach or link to instrument drafts.

Example items to record: question text, response scales, branching logic, estimated completion time.

7) Analysis handoffs

Prompt: Specify who will run each analysis (team, role), the deliverable format (datasets, codebook, transcripts, coded themes), expected timelines, and reproduction notes (software, random seeds, preprocessing steps).

Include: required file formats and naming conventions to make synthesis predictable.

8) Synthesis methods

Prompt: Explain how qualitative and quantitative results will be integrated. Options include joint displays, triangulation tables, meta‑inference statements, or follow‑up experiments informed by qual findings. Describe decision rules for conflicting evidence.

9) Timeline & milestones

Prompt: Provide start/end dates for planning, data collection, analysis, synthesis, and reporting. Note critical gating events (ethics approval, recruitment launch, experiment start).

10) Resources & roles

Prompt: List team members, roles (PI, data analyst, interviewer, recruiter), estimated person‑hours, and any external vendors or tools required.

11) Ethical & consent considerations

Prompt: Note IRB/ethics needs, consent language, data sensitivity, anonymization plans, storage and retention policies, and any foreseeable risks to participants. Include steps to mitigate risk.

12) Documentation & reproducibility checklist

• Pre-registered hypotheses or protocol? (yes/no + link)
• Data dictionary & codebook prepared? (yes/no)
• Version-controlled analysis scripts? (yes/no + repo link)
• Shared storage location and access plan

13) Deliverables & decision criteria

Prompt: What outputs will inform the decision? (e.g., go/no‑go thresholds, estimated ROI, prioritized feature list). Define success criteria and next steps for each possible outcome.

Common pitfalls & rescue checklist

• Vague research question — refine to be decision‑oriented.
• Sampling mismatch between qual and quant — align populations or justify differences.
• Missing handoff details — name file formats, analysis owners, and timelines.
• Underpowered quantitative test — estimate sample size before launching.
• Skipping ethics or consent — obtain approvals and record consent procedures.

Example mini plan (illustrative)

Q: Does simplified signup increase 7‑day activation?
Quant: A/B test measuring 7‑day activation rate; expected lift +8 pp; sample size 1,200 per group.
Qual: 12 follow‑up interviews (6 per variant) to understand barriers and perceived value.
Design: Explanatory sequential — run A/B, then purposive interviews with typical dropouts.
Handoffs: Analytics team delivers cleaned dataset (CSV) and pre-specified metric calculations; UX researcher provides interview transcripts and coded themes; synthesis to produce joint recommendations within two weeks of data collection close.

Next steps & adaptation

Copy this template into your project space. For repeatable use, consider converting it into an interactive form that saves plans, tracks versions, and links to instrument files and analysis repositories.