When should we choose qualitative research?
Chapter 13: Qualitative and Mixed Methods — Video 1
https://ghrbook.com/videos/when-to-choose-qualitative-research/

[Slide 1] This chapter is about qualitative and mixed methods research: what qualitative research is, when we should choose it, and how it can be combined with quantitative work.
[Slide 2] Here's a scenario you might find familiar. You're reading a research article about a community health worker program in rural Kenya, aimed at improving maternal health outcomes. The numbers look good. Clinic visits increased by 37 percent. Births with skilled attendants rose from 42 to 61 percent. Maternal mortality declined. Success, right?
[Slide 3] But then you visit that community. The gains are real, and they're also unevenly shared. The women still missing from those averages are the poorest and the most remote. Some walk past the new clinic to a traditional birth attendant three villages away, because the health workers aren't trusted: they don't speak the local dialect. And a rising rate of skilled attendance says nothing about the woman who reached the clinic, only to die after it referred her to a distant hospital her family couldn't afford. The numbers aren't wrong. They're incomplete.
[Slide 4] This gap between what we can count and what we need to understand drives the need for qualitative research in global health. Don't get me wrong: numbers matter enormously. We need to know if interventions reduce mortality and if programs reach their intended beneficiaries. But when we're working across cultures, within complex social systems, and with vulnerable populations, numbers alone can't capture the full story. We need approaches that can illuminate how and why things work, or don't work, and that can capture the lived experiences of the people our research is meant to serve.
[Slide 5] I promise to keep the philosophy brief, but it shapes everything from design to analysis to how we judge quality. Quantitative research generally operates within a positivist or post-positivist paradigm: there's an objective reality out there, and we can measure it, though perhaps imperfectly. When we conduct randomized controlled trials or analyze survey data, we're usually working within this paradigm. Qualitative research typically operates within interpretive or constructivist paradigms. Reality is socially constructed, meaning emerges through interaction and interpretation, and multiple valid perspectives exist on any phenomenon.
[Slide 6] What does this mean practically? In qualitative research we're not trying to eliminate our influence as researchers to achieve objectivity. We're trying to be reflexive about that influence, acknowledging how our own background, our assumptions, and our relationship with participants shape what we see and hear. Good qualitative research doesn't pretend the researcher is invisible. It makes the researcher's positionality explicit and thinks carefully about how it might affect the findings.
[Slide 7] Qualitative traditions vary, but they share several characteristics. They study phenomena in natural settings. They reason inductively, building understanding from observations. They take a holistic perspective. And the design is emergent: it adapts as we learn. Maybe our first interviews reveal that a factor we didn't anticipate, say transportation costs, is crucial. Good qualitative design lets us adjust the interview guide to explore it more deeply in later interviews.
[Slide 8] The last characteristic is the one that matters most for what comes later in this chapter. In quantitative research, measurement instruments like surveys, tests, and diagnostic tools collect the data. In qualitative research, you are the primary instrument, working through your observations, your interview questions, and your analytical choices. That makes researcher training, reflexivity, and quality processes crucial.
[Slide 9] With these foundations in mind, the practical question becomes: when should we choose qualitative research over, or in addition to, quantitative approaches? Qualitative research excels when we're asking how, why, or what does this mean, and not how many or how much. What percentage of pregnant women in rural Tanzania attend at least four antenatal care visits? That's a quantitative question. What factors influence pregnant women's decisions about whether to attend antenatal care in rural Tanzania, and how do they weigh competing demands on their time and resources? That's a qualitative question. Does task-shifting mental health counseling to peer health workers improve depression outcomes? How do patients and community members perceive peer health workers delivering mental health interventions? Both kinds of questions matter, but they require different approaches.
[Slide 10] To make this concrete, let's look at how quantitative and qualitative researchers have tackled the same question. How should perinatal mental health support be delivered to women in low-resource settings, and who should deliver it?
[Slide 11] The first team used a discrete choice experiment with 153 pregnant adolescents, ages 14 to 18, recruited from two primary healthcare facilities in Nairobi's informal settlements. Each participant completed 10 choice tasks, picking between two hypothetical depression treatment packages that varied across eight attributes. Rating features one at a time lets people endorse everything as important. Because each package bundled several features, choosing one meant giving up something in the other.
[Slide 12] The modeled choices expose which features participants actually trade off for which. Adolescents preferred facility nurses over community health volunteers, eight sessions over four, parenting-skills content over peer support, and adolescent-friendly services over clinics mixed with older mothers. The number is precise, the direction is clear, and the finding plugs straight into program design.
[Slide 13] A second team asked a closely related question in Lilongwe, Malawi. They conducted 20 in-depth interviews, 3 focus group discussions, and 10 social-support mapping sessions with 42 participants: adolescents living with HIV who were experiencing depression, their caregivers, healthcare providers, and people who had delivered or received a similar lay-counseling intervention. Participants answered open-ended questions about where, when, how, and from whom adolescents would want counseling.
[Slide 14] Across groups, participants converged on a small set of counselor qualities: trustworthy, youth-friendly, non-judgmental, and able to keep confidences. They clearly preferred healthcare providers over other community authority figures such as village chiefs or teachers. One adolescent explained the reasoning, and the slide shows it in their words. The institutional structure of the clinic, more than any individual attribute, made trust possible.
[Slide 15] But the same conversations surfaced things a forced-choice survey would not. A minority of adolescents wanted counselors who were themselves living with HIV, valuing lived experience over credentials. Preferences for session timing and frequency varied widely. And a structural tension emerged. Adolescents and caregivers wanted frequent, flexible, relational counseling, while counselors and implementers emphasized workload, staffing limits, and the need to maintain professional boundaries.
[Slide 16] The two approaches point in the same broad direction, but we learn something different from each one. The discrete choice experiment gives us relative preference weights across many participants, and it can be analyzed at the subgroup level. The qualitative study's adolescents also preferred clinic-based providers, and from them we learn why: clinics are convenient, and the staff are generally competent and trained in how to maintain confidentiality.
[Slide 17] This is what qualitative research uniquely offers: an understanding of why and how the choice is constructed, and visibility into the dissent, exceptions, and operational tensions that a quantitative summary smooths over.