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In public health practice, the method you choose to collect data fundamentally shapes what you can learn—and what actions you can take. You're not just being tested on whether you can name these methods; you're being evaluated on your ability to match the right data collection approach to specific research questions, populations, and resource constraints. Understanding the distinction between quantitative vs. qualitative data, self-reported vs. objective measures, and individual vs. community-level approaches will determine how well you can design studies and critique existing research.
Each method in this guide represents a deliberate trade-off between depth and breadth, between what people say they do and what they actually do, between efficiency and engagement. When you encounter these methods on exams or in practice, ask yourself: What kind of data does this produce? What biases might it introduce? When would this be the best choice? Don't just memorize definitions—know what each method reveals and what it obscures.
These methods efficiently collect standardized data from large populations, relying on participants to accurately report their own behaviors, attitudes, and experiences. The trade-off is scale versus depth—you gain statistical power but lose nuance.
Compare: Surveys vs. Ecological Momentary Assessment—both rely on self-report, but surveys capture retrospective snapshots while EMA captures experiences in the moment. If an exam question asks about minimizing recall bias, EMA is your answer; if it asks about population-level prevalence, traditional surveys win.
These approaches prioritize depth over breadth, generating rich narrative data that reveals the "why" behind behaviors and experiences. The mechanism here is human conversation—structured to varying degrees—that allows participants to explain their perspectives in their own words.
Compare: Individual Interviews vs. Focus Groups—interviews reveal personal depth and are better for sensitive topics, while focus groups expose social dynamics and shared community perspectives. Choose interviews when confidentiality matters; choose focus groups when you need to understand how ideas circulate within groups.
These methods capture what people actually do rather than what they say they do, addressing the fundamental gap between self-reported and actual behavior. The underlying principle is that observation accesses behavior directly, bypassing the filters of memory, social desirability, and self-perception.
Compare: Direct Observation vs. Participant Observation—direct observation maintains researcher distance for objectivity, while participant observation sacrifices some objectivity for deeper contextual understanding. Use direct observation for behavioral counts; use participant observation for understanding meaning and culture.
These approaches collect biological or physical data that doesn't depend on participant reporting, providing the most objective evidence of health status. The principle is that bodies don't lie—physiological measures bypass cognitive and social biases entirely.
Compare: Physical Measurements vs. Biospecimen Collection—both provide objective data, but physical measurements are non-invasive and yield immediate results, while biospecimens require laboratory analysis but reveal internal biological states. Physical measurements work for field settings; biospecimens require infrastructure.
These methods shift power toward community members, positioning them as active contributors to knowledge generation rather than passive subjects. The mechanism is empowerment—community participation increases relevance, validity, and likelihood of action on findings.
Compare: Photovoice vs. Community Mapping—both are participatory and produce visual data, but photovoice captures individual experiences and perspectives while community mapping creates collective spatial knowledge. Use photovoice for advocacy and storytelling; use community mapping for resource assessment and intervention planning.
| Concept | Best Examples |
|---|---|
| Large-scale quantitative data | Surveys, Questionnaires |
| Minimizing recall bias | Ecological Momentary Assessment |
| In-depth individual perspectives | Semi-structured Interviews, Unstructured Interviews |
| Group dynamics and social norms | Focus Groups |
| Actual vs. self-reported behavior | Direct Observation, Participant Observation |
| Objective health measurement | Physical Measurements, Biospecimen Collection |
| Community empowerment and engagement | Photovoice, Community Mapping |
| Cultural context and meaning | Participant Observation, Focus Groups |
A researcher wants to understand why adolescents in a specific neighborhood don't use the local health clinic. Which two methods would best capture both individual barriers and community-level perceptions, and why?
Compare and contrast ecological momentary assessment with traditional surveys. In what specific research scenario would EMA be clearly superior, and what trade-offs would you accept?
You're studying hand hygiene practices among food service workers. Why might direct observation yield different results than a survey, and what threats to validity would you need to address with the observational approach?
An FRQ asks you to design a community needs assessment using participatory methods. Which methods from this guide would you select, and how would you sequence them to build community engagement?
A study uses biospecimen collection to measure cotinine levels (a nicotine metabolite) alongside self-reported smoking status. What is the methodological rationale for including both measures, and what might discrepancies between them reveal?