March 29, 2024
Updated May 12, 2025
16 minute read
Embarking on a Career as a Public Health Researcher
Public health research stands as a critical field dedicated to understanding and improving the health of populations. Professionals in this area investigate the patterns, causes, and effects of health and disease conditions within defined groups. They use this knowledge to inform public policy, develop health programs, and ultimately prevent illness and promote well-being on a large scale, moving beyond individual patient care to address the health of entire communities.
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Find a path to becoming a Public Health Researcher. Learn more at:
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Reading list
We've selected some books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Public Health Researcher.
This ambitious textbook presents a unified framework for understanding all of statistics, from basic concepts to advanced topics. It covers a wide range of topics, from probability theory to statistical inference to Bayesian statistics, and includes many solved problems and exercises.
Widely used textbook that provides a comprehensive introduction to the three major approaches to research design: qualitative, quantitative, and mixed methods. It is excellent for gaining a broad understanding and is often used as a foundational text in academic programs. The latest edition includes updates reflecting the 7th edition of the APA Publication Manual, making it a current and valuable reference.
This highly influential textbook provides a comprehensive introduction to statistical learning methods, with a focus on supervised learning. It covers a wide range of topics, from linear regression to support vector machines, and includes many real-world examples and exercises.
Is highly regarded for its practical approach to causal inference, particularly within epidemiology and health sciences. It covers essential concepts and methods like propensity scores and outcome modeling in detail. It's a valuable resource for both students and practitioners, often used as a textbook in public health and statistics programs. The online version is freely available, making it highly accessible.
Offers a contemporary and accessible introduction to causal inference, particularly aimed at social scientists. It includes code examples in R and Stata and covers a range of modern techniques. It's a good resource for students and researchers looking for practical guidance and intuition. The free online version makes it highly accessible.
Classic guide to the research process, focusing on formulating research questions, developing arguments, and communicating findings effectively. It is highly recommended for students at all levels who are learning how to approach and structure a research project. It is particularly useful for solidifying an understanding of the fundamental principles of research.
A practical guide to coding qualitative data, this book provides detailed instructions and examples for various coding methods. It is an essential tool for anyone undertaking qualitative data analysis, from beginners to experienced researchers. It helps solidify understanding of a crucial step in qualitative research.
This sourcebook detailed guide for analyzing qualitative data. It offers a wide range of methods and techniques, making it a valuable reference for students and researchers undertaking qualitative studies. The fourth edition includes new visual display models and updated content, ensuring its relevance for contemporary research.
This practical textbook provides a comprehensive introduction to statistical analysis using the R programming language, making it a great choice for students and professionals who need to learn how to use R to analyze data. It covers a wide range of topics, from basic concepts to advanced topics, and includes many solved problems and exercises.
Written for a broad audience, this book provides a conceptual overview of causal inference and its history, emphasizing the power of causal diagrams. It's an excellent resource for gaining a broad understanding and appreciating the 'causal revolution.' While not a technical textbook, it's a must-read for anyone wanting to grasp the intuition behind causal thinking and its implications across various fields.
This engaging and practical book teaches students how to think critically about data and make sound statistical inferences. It covers a wide range of topics, including data exploration, hypothesis testing, and regression analysis, and includes many real-world examples.
Provides a comprehensive overview of statistical methods used in psychology, making it a valuable resource for students and professionals alike. It covers a wide range of topics, from basic concepts to advanced techniques, and includes many worked examples and exercises.
A comprehensive guide to mixed methods research, this book covers the design, implementation, and analysis of studies that combine qualitative and quantitative approaches. It is essential for researchers interested in this growing area of research methodology and provides detailed guidance.
This comprehensive textbook provides a thorough introduction to causal inference based on the potential outcomes framework. It covers a wide range of topics and is suitable for graduate students and researchers in statistics, social sciences, and biomedical sciences. It is considered a foundational text in the potential outcomes tradition.
This online textbook and accompanying website provide a comprehensive overview of research methods concepts and procedures. It widely used resource for learning and teaching research methods, offering clear explanations and interactive exercises. It good resource for both foundational knowledge and review.
Delves into modern causal machine learning techniques and their implementation in Python using libraries like DoWhy and EconML. It's aimed at practitioners and researchers interested in the cutting edge of causal inference and its integration with machine learning.
This foundational and comprehensive text by a pioneer in the field of causal inference. It delves deeply into the theoretical underpinnings of causal reasoning, graphical models, and counterfactuals. It is considered a classic and a crucial reference for researchers and graduate students seeking a deep understanding of the theory.
A definitive guide to conducting case study research, this book covers the design, collection, and analysis of case study data. It is essential for researchers utilizing this specific qualitative approach and provides in-depth knowledge for mastering the method.
Provides a thorough treatment of causal inference methods with a strong emphasis on applications in the social sciences. It covers various approaches, including the potential outcomes framework and graphical models, with detailed examples. It's a valuable resource for graduate students and researchers in sociology, political science, and other social disciplines.
This practical book focuses on applying causal inference techniques using Python, with examples relevant to the tech industry. It's a great resource for data scientists and practitioners who want to implement causal inference methods in a popular programming language. It bridges the gap between theory and application.
Explores the application of HIA to assess the health impacts of climate change. It provides guidance on how to identify and quantify the health risks associated with climate change and discusses strategies for adaptation and mitigation.
Provides a rigorous foundation in causal inference with a focus on the relationship between causality and machine learning. It's particularly relevant for those with a background in computer science or statistics interested in causal discovery methods. While it can be more technical, it offers valuable insights into modern approaches.
Provides practical guidance on conducting survey research, covering questionnaire design, sampling, data collection, and analysis. It valuable resource for those focusing on quantitative methods, particularly in social sciences and market research. It helps deepen understanding of a specific research methodology.
The official guide for APA Style, this manual is essential for researchers in psychology and many other social and behavioral sciences for formatting manuscripts, citing sources, and ensuring ethical reporting. It crucial reference tool for academic writing and publication.
Focuses specifically on the design and analysis of observational studies for causal inference. It provides a deep understanding of the challenges and methods involved in drawing causal conclusions from non-randomized data. It's an important resource for researchers in various fields who work with observational data.
Focuses on advanced topics in causal inference, specifically mediation and interaction analysis. It provides a comprehensive overview of recent methodological developments in these areas from a causal inference perspective. It's a valuable resource for researchers interested in exploring the mechanisms and effect modification in causal pathways.
Provides a hands-on approach to causal inference using the R programming language. It covers a range of techniques with practical examples and code snippets. It's a valuable resource for students and practitioners who prefer to work with R for causal analysis.
Offers a step-by-step guide to conducting a research project, covering all stages from planning to writing up findings. It is particularly helpful for undergraduate and postgraduate students undertaking their first research project, providing practical advice and avoiding common mistakes.
This guide offers a practical and jargon-free approach to undertaking a research project. It breaks down the process into manageable steps and provides clear advice, making it ideal for students who are new to research or feeling overwhelmed. It good resource for gaining a broad understanding and practical skills.
Aims to make causal inference accessible to data scientists, focusing on practical applications and using causal graphs. It provides techniques for identifying causes from data, even without experiments. It's a good resource for data professionals looking to incorporate causal thinking into their work.
Provides a clear and concise introduction to statistical methods used in research, making it a great choice for beginners. It covers the basics of statistical analysis, including hypothesis testing, regression, and ANOVA.
This introductory textbook provides an accessible entry point into qualitative research, focusing on analyzing life experiences. It is suitable for beginners and those seeking a more narrative approach to qualitative inquiry.
This open-source material offers a light-hearted yet rigorous introduction to causal inference using Python. It covers core concepts and models with a focus on accessibility and practical application. It's a good resource for students and practitioners, particularly those with a data science background.
Considered a classic in qualitative research, this book introduces the principles and practices of naturalistic inquiry. It foundational text for understanding the philosophical underpinnings of qualitative research. It is more theoretical and suitable for graduate-level study and beyond.
Introduces the theory and methods of causal inference, which are essential for understanding the etiology of diseases.
Provides a comprehensive overview of the etiology and pathogenesis of chronic kidney disease, which major cause of morbidity and mortality worldwide.
Offers a more gentle introduction to causal inference compared to Rosenbaum's 'Design of Observational Studies.' It uses minimal mathematics and statistics, explaining key concepts through scientific examples. It's suitable for students and researchers looking for an accessible entry point into causal inference in observational studies.
While focused on A/B testing, this book is highly relevant as A/B testing key method for establishing causality in online settings. It provides practical guidance for designing, implementing, and analyzing online experiments. It's a must-read for anyone involved in online controlled experiments and a valuable resource for understanding the practical application of causal principles.
Examines causal inference specifically within the context of public health and social determinants of health. It discusses the application of causal concepts to complex social phenomena and offers a critical perspective on dominant frameworks. It's particularly relevant for students and researchers in public health, sociology, and related fields.
Provides an introduction to research methods specifically within the field of sociology. It covers a range of methods and their application in sociological research, making it relevant for students in this discipline. It helps provide context for research methods within a specific social science.
Provides an overview of causal analysis methods, likely covering a range of statistical and econometric techniques used for causal inference. It's a valuable resource for students and researchers seeking to understand different approaches to causal analysis.
Provides a comprehensive overview of epidemiology, which is the study of the distribution and determinants of diseases.
This classic text in the field of causal discovery, focusing on algorithms for learning causal structures from data. It's a more advanced and theoretical book, suitable for graduate students and researchers interested in the philosophical and algorithmic aspects of causal inference and discovery.
While not strictly a research methods book, this classic guide to writing style is invaluable for anyone writing research papers, theses, or dissertations. Clear and concise writing is crucial for effectively communicating research findings, making thmust-read for all students and researchers. It serves as a foundational text for academic writing.
Explores the integration of HIA into Environmental Impact Assessment (EIA). It provides guidance on how to assess the health impacts of proposed developments and projects, ensuring that health concerns are considered alongside environmental factors.
For more information about how these books relate to this course, visit:
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