1st Edition
Evaluating What Works An Intuitive Guide to Intervention Research for Practitioners
Those who work in allied health professions and education aim to make people’s lives better. Often, however, it is hard to know how effective this work has been: would change have occurred if there was no intervention? Is it possible we are doing more harm than good? To answer these questions and develop a body of knowledge about what works, we need to evaluate interventions. Objective intervention research is vital to improve outcomes, but this is a complex area, where it is all too easy to misinterpret evidence. This book uses practical examples to increase awareness of the numerous sources of bias that can lead to mistaken conclusions when evaluating interventions. The focus is on quantitative research methods, and exploration of the reasons why those both receiving and implementing intervention behave in the ways they do. Evaluating What Works: Intuitive Guide to Intervention Research for Practitioners illustrates how different research designs can overcome these issues, and points the reader to sources with more in-depth information. This book is intended for those with little or no background in statistics, to give them the confidence to approach statistics in published literature with a more critical eye, recognise when more specialist advice is needed, and give them the ability to communicate more effectively with statisticians.
Key Features:
- Strong focus on quantitative research methods
- Complements more technical introductions to statistics
- Provides a good explanation of how quantitative studies are designed, and what biases and pitfalls they can involve
1. Introduction
2. Why observational studies can be misleading
3. How to select an outcome measure
4. Improvement due to nonspecific effects of intervention
5. Limitations of the pre-post design: biases related to systematic change
6. Estimating unwanted effects with a control group
7. Controlling for selection bias: randomized assignment to intervention
8. The researcher as a source of bias
9. Further potential for bias: volunteers, dropouts, and missing data
10. The randomized controlled trial as a method for controlling biases
11. The importance of variation
12. Analysis of a two-group RCT
13. How big a sample do I need? Statistical power and type II errors
14. False positives, p-hacking and multiple comparisons
15. Drawbacks of the two-arm RCT
16. Moderators and mediators of intervention effects
17. Adaptive Designs
18. Cluster Randomized Controlled Trials
19. Cross-over designs
20. Single case designs
21. Can you trust the published literature?
22. Pre-registration and Registered Reports
23. Reviewing the literature before you start
24. Putting it all together
25. Comments on exercises
26. References
Biography
Dorothy Bishop was Professor of Developmental Neuropsychology at the University of Oxford from 1998 to 2022. Dorothy is a Fellow of the Academy of Medical Sciences, a Fellow of the British Academy, and a Fellow of the Royal Society. She been recognised with Honorary Fellowships from the Royal College of Speech and Language Therapists, the British Psychological Society, and the Royal College of Pediatrics and Child Health. She has Honorary Doctorates from the Universities of Newcastle upon Tyne, UK, Western Australia, Lund, Sweden, École Normale Supérieure, Paris, and Liège, Belgium. She is an Honorary Fellow of St John’s College, Oxford.
Paul Thompson is an Assistant Professor in Applied Statistics and the department lead for statistics and quantitative methods at the Centre for Educational Development, Appraisal and Research (CEDAR) at the University of Warwick. Between 2014 and 2021 he worked at Oxford University within the Department of Experimental Psychology, working on a wide range of projects including behavioural, genetics, and neuroimaging (brain scanning) studies in developmental language disorders such as Dyslexia, and Developmental Language Disorder, and language development in those with learning and developmental disabilities, such as Down Syndrome and Autism.
