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.
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
June 17, 2027.
Template
Data Management
January 17, 2023. Presenter: Rebecca Clark, Research Coordinator
December 1, 2020. Presenters: Isaac Pratt and Jay Brodeur, McMaster University Library
Examples
Data Sources & Collection
January 22, 2019. Presenters: Steve Dragos and Larkin Lamarche (DFM research staff)
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.
April 23, 2019. Presenters: Matt Kwan and Jeff Graham (INCH research group)
Presentation
Michelle Howard, Associate Professor
DFM Resource
July 24, 2026.
October 15, 2021.
Journal Article
Journal article: Kallio, H., Pietilä, A. M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: developing a framework for a qualitative semi‐structured interview guide. Journal of advanced nursing, 72(12), 2954-2965.*
Artino Jr, A. R., La Rochelle, J. S., Dezee, K. J., & Gehlbach, H. (2014). Developing questionnaires for educational research: AMEE Guide No. 87. Medical teacher, 36(6), 463-474.
Journal article: Rickards, G., Magee, C., & Artino Jr, A. R. (2012). You can’t fix by analysis what you’ve spoiled by design: developing survey instruments and collecting validity evidence. Journal of graduate medical education, 4(4), 407-410.
Use data collection methods and instruments that are accessible, culturally appropriate, and validated for the populations involved. Where appropriate, collect and analyze data related to factors such as sex, gender, age, race, ethnicity, disability, socioeconomic status, language, and geography to better understand differences in experiences and outcomes. Consider sex and gender not only as demographic characteristics but also as factors that may influence findings.
Implement recruitment and retention strategies that support the inclusion of underrepresented groups and diverse lived experiences. When appropriate and feasible, examine findings across relevant population groups to identify potential inequities or disparities. Avoid overgeneralizing results and clearly acknowledge limitations related to representation and data availability.
When working with First Nations communities, ensure data collection, management, analysis, and reporting align with the principles of Ownership, Control, Access, and Possession (OCAP®) and respect Indigenous data sovereignty.
Data collection in primary care involves gathering clinical, administrative, and patient-reported data in ways that are feasible within routine practice.
October 22, 2019. Presenters: Steve Dragos, Ric Angeles and Larkin Lamarche (DFM research staff)
November 26, 2019. Presenters: Steve Dragos, Ric Angeles and Larkin Lamarche (DFM research staff)
Community Engagement
December 19, 2017. Presenters: Laura Cleghorn, Jessica Gaber (DFM research staff)
McGrath, C., Palmgren, P. J., & Liljedahl, M. (2019). Twelve tips for conducting qualitative research interviews. Medical teacher, 41(9), 1002-1006.
May 17, 2018. Jessica Jurgutis, Interviews and Focus Groups.
Retrospective chart reviews involve the systematic examination of data originally collected for clinical care in medical records to answer specific research questions. It requires careful attention to data quality, privacy, and ethical considerations.
Design & Methods
October 26, 2021. Presenters: Michelle Howard, Associate Professor and Jeff Templeton, Research Coordinator
Journal article
Matt, V., & Matthew, H. (2013). The retrospective chart review: important methodological considerations. Journal of educational evaluation for health professions, 10.
January 16, 2024. Jen Lawson, Business Analyst and Shuaib Hafid, Statistical Analyst
Tutorial
Research with health administrative data involves the use of routinely collected information generated through healthcare system operations, such as physician billings, prescription claims, and registries. These data enable researchers to examine health service use, population health trends, care quality, and outcomes across large groups and over time. Because administrative data reflect real‑world healthcare delivery, this approach is particularly valuable for evaluating system‑level interventions, informing policy, and identifying disparities.
March 23, 2021. Presenters: Michelle Howard, Associate Professor and Shuaib Hafid, Statistical Analyst
Resource
This guide introduces Canadian data sources for secondary analyses to answer research questions related to primary care,
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. 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 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).
Qualitative critical questions explore the perspectives and experiences of patients, providers, or communities.