Whilst the ‘health sciences’ are a broad and diverse area, and includes public health, primary care, health psychology, psychiatry and epidemiology, the research methods and data analysis skills required to analyse them are very similar. Moreover, the ability to appraise and conduct research is emphasised within the health sciences – and students are expected increasingly to do both.
Introduction to Research Methods and Data Analysis in the Health Sciences presents a balanced blend of quantitative research methods, and the most widely used techniques for collecting and analysing data in the health sciences. Highly practical in nature, the book guides you, step-by-step, through the research process, and covers both the consumption and the production of research and data analysis. Divided into the three strands that run throughout quantitative health science research – critical numbers, critical appraisal of existing research, and conducting new research – this accessible textbook introduces:
- Descriptive statistics
- Measures of association for categorical and continuous outcomes
- Confounding, effect modification, mediation and causal inference
- Critical appraisal
- Searching the literature
- Randomised controlled trials
- Cohort studies
- Case-control studies
- Research ethics and data management
- Dissemination and publication
- Linear regression for continuous outcomes
- Logistic regression for categorical outcomes.
A dedicated companion website offers additional teaching and learning resources for students and lecturers, including screenshots, R programming code, and extensive self-assessment material linked to the book’s exercises and activities.
Clear and accessible with a comprehensive coverage to equip the reader with an understanding of the research process and the practical skills they need to collect and analyse data, it is essential reading for all undergraduate and postgraduate students in the health and medical sciences.
Table of Contents
Introduction 1. Evidence-Based Health Research Section 1: Critical Numbers 2. Descriptive Statistics Part 1: Levels of Measurement and Measures of Central Tendency 3. Descriptive Statistics Part 2: Measures of Dispersion 4. Measures of Association for Categorical Outcomes 5. Measures of Association for Continuous Outcomes 6. Confounding, Effect Modification, Mediation and Causal Inference Section 2: Critical Appraisal of Existing Research 7. Searching the Literature 8. Randomised Controlled Trials 9. Cohort Studies 10. Case-Control Studies 11. Research Ethics and Data Management Section 3: Conducting New Research 12. Dissemination and Publication 13. Linear Regression for Continuous Outcomes 14. Logistic Regression for Categorical Outcomes Appendices
Gareth Hagger-Johnson is a Senior Research Associate in the Department of Epidemiology and Public Health at University College London, UK.