Evaluating What Works

An Intuitive Guide to Intervention Research for Practitioners

Dorothy V. M. Bishop, Paul Thompson

• Publisher: Chapman and Hall/CRC
• Year: 2023
• Print ISBN: 9781032591209
• eBook ISBN: 9781003830115
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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

Table of contents

• Cover Page
• Half-Title Page
• Title Page
• Copyright Page
• Contents
• Preface
• Authors
• List of Figures
• List of Tables
• 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
• Comments on exercises
• References
• Index