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Research Idea
Review the Literature
Form and Refine the Research Question
Plan your Research Design
Gather and Analyze your Research Data
Share your Research Findings
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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, data analysis and secure deletion.

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.

Define Research Objectives

Choose the appropriate instrument type: Qualitative or Quantitative

Ensure Validity and Reliability of the Instrument

Pilot test for clarity and accessibility

Document the process

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 in primary care involves gathering clinical, administrative, and patient-reported data in ways that are feasible within routine practice.

Gathering data directly from the participant

Other data sources include Medical records and administrative databases. Click below to explore these topics.

Research with medical records
Research with health administrative data

Data Collection at McMaster Family Health Team

Data Analysis

Research data analysis is the process of systematically examining, organizing, analyzing, and interpreting data to uncover meaningful patterns, relationships, and insights. It transforms raw information into evidence or new knowledge 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. Quantitative critical questions may focus on the prevalence or patterns of a health condition or clinical practice (descriptive analysis), or on relationships between factors, such as the impact of a new care model on outcomes (inferential analysis). In contrast, qualitative critical questions explore the perspectives and experiences of patients, providers, or communities. 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 to improve patient outcomes and healthcare delivery.

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.

Quantitative Data Analysis

Descriptive Analysis

Critical questions that are quantitative in nature can ask about the presence or pattern of a health condition or clinical practice, for example, the pattern of COVID-19 requiring a descriptive analysis.

  • Presentation: Data visualization in research by Subhanya Sivajothi, Research Knowledge and Skill Builder [Slides | Video | Libguide]
  • Presentation: SPSS – Dr. Larkin Lamarche and Melissa Pirrie, Research Knowledge and Skill Builder [Video part 1 | Slides 1| Slides 2]
  • Presentation: Just the basics: Learning about the essential steps to do some simple things in SPSS by Dr. Larkin Lamarche, Research Knowledge and Skill Builder [Video | Slides]

Inferential Analysis of Quantitative Data

Critical questions that are quantitative in nature can ask about the relationship between factors, for example, the benefits of a new model of care on patient outcomes, requiring an inferential analysis.

Qualitative Data Analysis

Critical questions that are qualitative in nature can ask about the perspectives and experiences of patients, providers or community members.

Qualitative Description

Qualitative Description is a methodological approach, not just an analysis technique. It aims to provide a straightforward, low‑inference summary of participants’ experiences in everyday language. It focuses on what participants said rather than interpreting underlying meanings.

Thematic Analysis

It focuses on identifying and interpreting patterns of meaning (themes) across data. It aims to understand underlying meanings, experiences, and concepts. It is commonly used to explore how and why people perceive or experience something.

Content Analysis

It focuses on systematically categorizing elements of data (e.g., words, concepts, codes). It is more structured and descriptive, often used to examine what is present in the data and how often.

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