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Gather and Analyze your Research Data

This section provides best practices for active organization, handling and maintenance of the data created throughout the research process. Data lifecycle goes from planning, collection, processing and data analysis.

An image of the "research cycle". The box at the bottom left says "Gather and analyze your research data" and is highlighted while the other boxes are greyed out.

Data Management

Before collecting any data, it’s critical to establish a Data Management strategy. Data management refers to the process of collecting, storing, organizing, and maintaining research data throughout a project’s lifecycle. It ensures that data is: accurate, consistent, secure, accessible and preserved for future use or sharing. In research, good data management practices help maintain integrity, reproducibility, and compliance with institutional and funding requirements.

A Data Management Plan is a formal document that outlines how data will be handled during and after a research project. It typically includes information on the types of data you will collect (e.g., surveys, interviews, lab results), file formats and organization, storage and backup procedures, access and sharing policies, and ethical and legal considerations (privacy, consent, confidentiality). Many funding agencies (e.g., CIHR, NIH) require a DMP as part of grant applications to ensure transparency and sustainability.

  • Standard Operating Procedure: DFM Research Data Management SOP
  • Template: DFM Data Management Plan
  • Presentation: Data Management for Tri-Agency Grants by Rebecca Clark, Research Knowledge and Skill Builder [Slides | Video]
  • Presentation: Managing your research data: an introduction to DMP Assistant by Jay Brodeur and Isaac Pratt, Research Knowledge and Skill Builder [Slides | Video]
  • Examples: DMP exemplars
  • Presentation: Frontend and Backend Database Development by Steve Dragos, Research Knowledge and Skill Builder [Video | Slides]

Designing Research Instruments for Data Collection

Research instruments are the tools used to collect data systematically and reliably. These can include surveys, questionnaires, interview guides, observation checklists, and standardized tests. Well-designed instruments ensure that the data you gather is valid, reliable, and aligned with your research objectives.

Designing a research instrument

Piloting a research instrument

Validating a research instrument

  • Presentation: Validating tools – Dr. Matt Kwan and Jeffrey Graham, Research Knowledge and Skill Builder [Video | Slides]

Data Collection

Data Collection at McMaster Family Health Team

Data Analysis

Research data analysis is the process of systematically examining, organizing, and interpreting data to uncover meaningful patterns, relationships, and insights. It transforms raw information into evidence that can inform clinical practice, policy decisions, and equity-driven interventions.

In clinical research, data analysis helps answer critical questions about patient outcomes, treatment efficacy, and healthcare delivery. Whether you’re working with quantitative data from surveys and electronic health records or qualitative data from interviews and focus groups, the goal is the same: to make sense of complexity and generate actionable knowledge.

For mixed methods research, analysis becomes even more dynamic. It involves integrating statistical rigour with narrative depth, i.e. quantitative findings might reveal trends, while qualitative insights explain the “why” behind those trends. This approach is compelling in community-based research, where understanding lived experiences is just as vital as measuring disparities.

Data Cleaning and Descriptive Analysis

Inferential Analysis of Quantitative Data

Qualitative Data Analysis

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