About the Book

Global Health Research in Practice provides researchers, students, and practitioners with the knowledge and skills needed to conduct meaningful, ethical, and impactful health research in diverse settings around the world.

Author

Eric Green

Pages

~400 pages

Format

Print & Digital

Level

Undergraduate & Early Graduate

Learning Objectives

After reading this book, you will be able to:

Critically appraise scientific research to distinguish strong evidence from weak

Develop focused research questions grounded in theory and gaps in the literature

Apply causal inference frameworks to build defensible arguments from observational data

Evaluate when and why research findings generalize to new populations and settings

Select and justify research designs appropriate for diverse global health contexts

Plan rigorous sampling, measurement, and data collection procedures

Navigate ethical challenges in research with human participants

Translate research findings into actionable recommendations for policy and practice

Part I: Get Started with Global Health Research

Global Health Research

Understanding the landscape of global health research, what distinguishes it from other approaches, and the tension between discovery and application.

Collaborations

Building ethical research partnerships, navigating power dynamics, and moving from extraction to genuine collaboration.

Develop Research Ideas and Questions

Finding research problems worth studying, crafting focused questions using FINER and PICO frameworks, and developing falsifiable hypotheses.

Searching the Literature

Conducting systematic searches, using Boolean logic and controlled vocabulary, and distinguishing rigorous reviews from predatory publications.

How to Read Scientific Articles

Critically appraising research, interpreting effect sizes and confidence intervals, and evaluating both quantitative and qualitative studies for rigor.

Part II: Think About Validity

Statistical Inference

Learning from samples, interpreting confidence intervals and p-values correctly, and understanding the replication crisis.

Causal Inference

Thinking in counterfactuals, using DAGs to represent causal assumptions, and identifying effects through confounder-control and quasi-experimental designs.

External Validity and Generalizability

Distinguishing generalizability from transportability, recognizing effect modification, and applying the UTOS framework.

Measurement and Construct Validation

Capturing latent constructs, selecting indicators using DREAMY criteria, and validating instruments for reliability and validity.

Part III: Select a Research Design

Experimental Designs

Understanding the logic of randomization, designing randomized controlled trials, and addressing threats to internal validity.

Quasi-Experimental Designs

Exploiting natural variation through regression discontinuity, difference-in-differences, instrumental variables, and interrupted time series.

Qualitative and Mixed Methods

Designing rigorous qualitative research, ensuring trustworthiness, and integrating qualitative and quantitative approaches.

Part IV: Plan Your Methods

Sampling

Selecting study participants, probability and non-probability sampling strategies, and matching sampling approach to study objectives.

Sample Size and Power

Calculating sample size for adequate power, understanding the determinants of precision, and planning for attrition.

Pilot Studies

Testing feasibility, refining procedures, and knowing what pilots can and cannot tell you.

Fieldwork and Data Collection

Planning data collection, training teams, ensuring data quality, and managing the realities of research in the field.

Part V: Do the Work

Research Ethics

Navigating ethical review, obtaining informed consent, protecting vulnerable populations, and conducting research with integrity.

Open Science

Preregistration, data sharing, reproducible workflows, and the movement toward transparent research practices.

Research to Policy

Translating findings into action, engaging stakeholders, and understanding how evidence shapes (and doesn't shape) policy decisions.