Data Collection Plan

Overview

Create detailed data collection plans for clinical trials, UX research studies, academic dissertations, and field research in under 5 minutes. Each plan includes collection procedures, quality control measures, data security protocols, and compliance documentation.

Use Cases

• Design data collection protocols for 12-week user retention studies tracking 5,000+ data points
• Generate HIPAA-compliant data collection plans for clinical trials with 250+ participants
• Build survey instruments for SaaS product research measuring feature adoption rates
• Create field research protocols for anthropological studies requiring IRB approval
• Draft data collection frameworks for PhD dissertation research spanning multiple institutions
• Develop quality control procedures for multi-site research teams collecting biological samples

Benefits

Save 4-6 hours per research protocol - Generate complete data collection plans with procedures, instruments, and compliance measures in 5 minutes instead of drafting from scratch.

Ensure regulatory compliance - Automatically includes GDPR, HIPAA, and IRB considerations tailored to your data types and collection methods.

Reduce data quality issues - Built-in quality control measures and validation procedures catch common collection errors before they impact results.

Streamline team coordination - Personnel training requirements and role assignments help research teams execute consistently across multiple sites.

Accelerate IRB submissions - Complete compliance and ethical considerations sections speed institutional review board approvals.

Template

Create a data collection plan for:

Project: {{projectName}}

Research Questions:
{{researchQuestions}}

Data Types Needed: {{dataTypes}}

Collection Methods: {{collectionMethods}}

Timeline: {{timeline}}

Resources Required:
{{resources}}

Please provide:
1. Detailed data collection procedures
2. Data collection instruments
3. Quality control measures
4. Data storage and security plan
5. Personnel training requirements
6. Budget estimates
7. Risk mitigation strategies
8. Compliance and ethical considerations

Target: {{targetDataPoints}} data points from {{targetParticipants}} participants

Properties

• projectName: Single-line Text
• researchQuestions: Multi-line Text
• dataTypes: Multiple Selection
  • Options: Quantitative (numeric), Qualitative (text/interviews), Survey responses, Observational data, Biological samples, and 3 more
• collectionMethods: Multi-line Text
• timeline: Single-line Text
• resources: Multi-line Text
• targetDataPoints: Single-line Text
• targetParticipants: Single-line Text

Example Output

Here’s what this template generates for a mobile app user engagement study:

Input:

• Project: Mobile App User Engagement Study
• Research Questions: What features drive daily active usage? How do notifications affect retention?
• Data Types: Quantitative (numeric), Survey responses, Observational data
• Collection Methods: In-app analytics, weekly surveys, session recordings
• Timeline: 12 weeks (2 setup, 8 collection, 2 analysis)
• Target: 5000 data points from 250 participants

Generated Output Excerpt:

Data Collection Procedures:

Week 1-2: Install analytics SDK, configure event tracking for 15 core features, set up automated survey distribution system, recruit 250 participants via in-app banner targeting active users.

Week 3-10: Automatically track feature usage, session duration, and navigation patterns. Distribute 5-question Likert scale surveys every Monday at 9 AM user local time. Record 10 random user sessions daily using session replay software with participant consent.

Quality Control Measures:

• Daily automated validation checks for missing data points (flag if >5% daily records incomplete)
• Weekly manual review of 50 random survey responses for response quality
• Cross-reference analytics data with session recordings to verify tracking accuracy
• Implement rate limiting to prevent duplicate event logging

Data Storage and Security:

• Store raw analytics data in encrypted PostgreSQL database with daily backups
• Survey responses collected via HTTPS with AES-256 encryption at rest
• Session recordings stored on SOC 2 compliant servers with anonymized user IDs
• Implement role-based access control - only 3 research team members can access raw data
• Retain data for 24 months post-study, then securely delete per GDPR requirements

Common Mistakes to Avoid

Vague research questions lead to unfocused data collection - “Why do users churn?” collects everything. “Which features correlate with 7-day retention?” targets specific metrics you can actually measure.

Ignoring participant burden - Asking for 30-minute surveys weekly gets 12% completion. Five questions once per week maintains 68% response rates.

Overlooking data quality early - Missing validation checks during collection means discovering unusable data during analysis. Build automated quality checks into collection instruments from day one.

Underestimating storage and security requirements - Clinical trial data, user PII, and biological samples each have different compliance requirements. Verify your storage plan meets regulatory standards before collecting the first data point.

Skipping pilot testing - Testing collection procedures with 10-15 participants surfaces unclear survey questions, buggy tracking code, and unrealistic timelines before you’ve invested in full-scale collection.

Collecting data you can’t analyze - Recording 50 variables per participant sounds thorough until you lack statistical power to analyze them. Focus collection on variables directly tied to research questions.

Frequently Used With

Pair this template with complementary research workflows:

• Methodology Design - Design overall research approach before planning data collection
• Interview Guide - Create qualitative data collection instruments for research interviews
• Research Summary - Summarize collected data findings for different audiences

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