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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
A hands-on guide to using statistics in health research, from planning, through analysis, and on to reporting A Practical Approach to Using Statistics in Health Research offers an easy to use, step-by-step guide for using statistics in health research. The authors use their experience of statistics and health research to explain how statistics fit in to all stages of the research process. They explain how to determine necessary sample sizes, interpret whether there are statistically significant difference in outcomes between groups, and use measured effect sizes to decide whether any changes are large enough to be relevant to professional practice. The text walks you through how to identify the main outcome measure for your study and the factor which you think may influence that outcome and then determine what type of data will be used to record both of these. It then describes how this information is used to select the most appropriate methods to report and analyze your data. A step-by-step guide on how to use a range of common statistical procedures are then presented in separate chapters. To help you make sure that you are using statistics robustly, the authors also explore topics such as multiple testing and how to check whether measured data follows a normal distribution. Videos showing how to use computer packages to carry out all the various methods mentioned in the book are available on our companion web site. This book: * Covers statistical aspects of all the stages of health research from planning to final reporting * Explains how to report statistical planning, how analyses were performed, and the results and conclusion * Puts the spotlight on consideration of clinical significance and not just statistical significance * Explains the importance of reporting 95% confidence intervals for effect size * Includes a systematic guide for selection of statistical tests and uses example data sets and videos to help you understand exactly how to use statistics Written as an introductory guide to statistics for healthcare professionals, students and lecturers in the fields of pharmacy, nursing, medicine, dentistry, physiotherapy, and occupational therapy, A Practical Approach to Using Statistics in Health Research:From Planning to Reporting is a handy reference that focuses on the application of statistical methods within the health research context.
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Table of Contents
Cover
Title Page
Copyright
About the Companion Website
Chapter 1: Introduction
1.1 At Whom is This Book Aimed?
1.2 At What Scale of Project is This Book Aimed?
1.3 Why Might This Book be Useful for You?
1.4 How to Use This Book
1.5 Computer Based Statistics Packages
1.6 Relevant Videos etc.
Chapter 2: Data Types
2.1 What Types of Data are There and Why Does it Matter?
2.2 Continuous Measured Data
2.3 Ordinal Data
2.4 Categorical Data
2.5 Ambiguous Cases
2.6 Relevant Videos etc.
Chapter 3: Presenting and Summarizing Data
3.1 Continuous Measured Data
3.2 Ordinal Data
3.3 Categorical Data
3.4 Relevant Videos etc.
Appendix 1: An Example of the Insensitivity of the Median When Used to Describe Data from an Ordinal Scale With a Narrow Range of Possible Values
Chapter 4: Choosing a Statistical Test
4.1 Identify the Factor and Outcome
4.2 Identify the Type of Data Used to Record the Relevant Factor
4.3 Statistical Methods Where the Factor is Categorical
4.4 Correlation and Regression with a Measured Factor
4.5 Relevant Additional Material
Chapter 5: Multiple Testing
5.1 What Is Multiple Testing and Why Does It Matter?
5.2 What Can We Do to Avoid an Excessive Risk of False Positives?
Chapter 6: Common Issues and Pitfalls
6.1 Determining Equality of Standard Deviations
6.2 How Do I Know, in Advance, How Large My SD Will Be?
6.3 One‐Sided Versus Two‐Sided Testing
6.4 Pitfalls That Make Data Look More Meaningful Than It Really Is
6.5 Discussion of Statistically Significant Results
6.6 Discussion of Non‐Significant Results
6.7 Describing Effect Sizes with Non‐Parametric Tests
6.8 Confusing Association with a Cause and Effect Relationship
Chapter 7: Contingency Chi‐Square Test
7.1 When Is the Test Appropriate?
7.2 An Example
7.3 Presenting the Data
7.4 Data Requirements
7.5 An Outline of the Test
7.6 Planning Sample Sizes
7.7 Carrying Out the Test
7.8 Special Issues
7.9 Describing the Effect Size
7.10 How to Report the Analysis
7.11 Confounding and Logistic Regression
7.12 Larger Tables
7 12.2 Reducing Tables
7.13 Relevant Videos etc.
Chapter 8: Independent Samples (Two‐Sample) T‐Test
8.1 When Is the Test Applied?
8.3 Presenting the Data
8.4 Data Requirements
8.5 An Outline of the Test
8.6 Planning Sample Sizes
8.7 Carrying Out the Test
8.8 Describing the Effect Size
8.9 How to Describe the Test, the Statistical and Practical Significance of Your Findings in Your Report
8.10 Relevant Videos etc.
Chapter 9: Mann–Whitney Test
9.1 When Is the Test Applied?
9.2 An Example
9.3 Presenting the Data
9.4 Data Requirements
9.5 An Outline of the Test
9.6 Statistical Significance
9.7 Planning Sample Sizes
9.8 Carrying Out the Test
9.9 Describing the Effect Size
9.10 How to Report the Test
9.11 Relevant Videos etc.
Chapter 10: One‐Way Analysis of Variance (ANOVA) – Including Dunnett's and Tukey's Follow Up Tests
10.1 When Is the Test Applied?
10.2 An Example
10.3 Presenting the Data
10.4 Data Requirements
10.5 An Outline of the Test
10.6 Follow Up Tests
10.7 Planning Sample Sizes
10.8 Carrying Out the Test
10.9 Describing the Effect Size
10.10 How to Report the Test
10.11 Relevant Videos etc.
Chapter 11: Kruskal–Wallis
11.1 When Is the Test Applied?
11.2 An Example
11.3 Presenting the Data
11.4 Data Requirements
11.5 An Outline of the Test
11.6 Planning Sample Sizes
11.7 Carrying Out the Test
11.8 Describing the Effect Size
11.9 Determining Which Group Differs from Which Other
11.10 How to Report the Test
11.11 Relevant Videos etc.
Chapter 12: McNemar's Test
12.1 When Is the Test Applied?
12.2 An Example
12.3 Presenting the Data
12.4 Data Requirements
12.5 An Outline of the Test
12.6 Planning Sample Sizes
12.7 Carrying Out the Test
12.8 Describing the Effect Size
12.9 How to Report the Test
12.10 Relevant Videos etc.
Chapter 13: Paired T‐Test
13.1 When Is the Test Applied?
13.2 An Example
13.3 Presenting the Data
13.4 Data Requirements
13.5 An Outline of the Test
13.6 Planning Sample Sizes
13.7 Carrying Out the Test
13.8 Describing the Effect Size
13.9 How to Report the Test
13.10 Relevant Videos etc.
Chapter 14: Wilcoxon Signed Rank Test
14.1 When Is the Test Applied?
14.2 An Example
14.3 Presenting the Data
14.4 Data Requirements
14.5 An Outline of the Test
14.6 Planning Sample Sizes
14.7 Carrying Out the Test
14.8 Describing the Effect Size
14.9 How to Report the Test
14.10 Relevant Videos etc.
Chapter 15: Repeated Measures Analysis of Variance
15.1 When Is the Test Applied?
15.2 An Example
15.3 Presenting the Data
15.4 Data Requirements
15.5 An Outline of the Test
15.6 Planning Sample Sizes
15.7 Carrying Out the Test
15.8 Describing the Effect Size
15.9 How to Report the Test
15.10 Relevant Videos etc.
Chapter 16: Friedman Test
16.1 When Is the Test Applied?
16.2 An Example
16.3 Presenting the Data
16.4 Data Requirements
16.5 An Outline of the Test
16.6 Planning Sample Sizes
16.7 Follow Up Tests
16.8 Carrying Out the Tests
16.9 Describing the Effect Size
16.10 How to Report the Test
16.11 Relevant Videos etc.
Chapter 17: Pearson Correlation
17.1 Presenting the Data
17.2 Correlation Coefficient and Statistical Significance
17.3 Planning Sample Sizes
17.4 Effect Size and Practical Relevance
17.5 Regression
17.6 How to Report the Analysis
17.7 Relevant Videos etc.
Chapter 18: Spearman Correlation
18.1 Presenting the Data
18.2 Testing for Evidence of Inappropriate Distributions
18.3 Rho and Statistical Significance
18.4 An Outline of the Significance Test
18.5 Planning Sample Sizes
18.6 Effect Size
18.7 Where Both Measures Are Ordinal
18.8 How to Report Spearman Correlation Analyses
18.9 Relevant Videos etc.
Chapter 19: Logistic Regression
19.1 Use of Logistic Regression with Categorical Outcomes
19.2 An Outline of the Significance Test
19.3 Planning Sample Sizes
19.4 Results of the Analysis
19.5 Describing the Effect Size
19.6 How to Report the Analysis
19.7 Relevant Videos etc.
Chapter 20: Cronbach's Alpha
20.1 Appropriate Situations for the Use of Cronbach's Alpha
20.2 Inappropriate Uses of Alpha
20.3 Interpretation
20.4 Reverse Scoring
20.5 An Example
20.6 Performing and Interpreting the Analysis
20.7 How to Report Cronbach's Alpha Analyses
20.7 Relevant Videos etc.
Glossary
Videos
Index
End User License Agreement
List of Tables
Chapter 1: Introduction
Table 1.1 The ideal stage‐by‐stage flow of events for a research program.
Chapter 3: Presenting and Summarizing Data
Table 3.1 Levels of agreement among younger and older patients with the statement that “I would be happy to book future appointments electronically.” Higher values reflect stronger agreement. Medians are emboldened.
Chapter 4: Choosing a Statistical Test
Table 4.1 Examples of factors and outcomes.
Table 4.2 Identification of appropriate statistical tests for studies with a categorical factor.
Table 4.3 Selection of correlation or regression techniques depending on type of data used to record the factor and outcome.
Chapter 6: Common Issues and Pitfalls
Table 6.1 Characteristics of one and two‐sided questions and testing procedures.
Table 6.2 Rounding to three significant digits.
Chapter 7: Contingency Chi‐Square Test
Table 7.1 Examples of studies that would be analyzed by a contingency chi‐square test.
Table 7.2 Contingency table showing numbers and percentages with and without pneumonia among patients who do or do not receive physiotherapy and exercise in addition to normal care.
Chapter 8: Independent Samples (Two‐Sample) T‐Test
Table 8.1 Examples of studies that would be analyzed by an independent samples (two‐sample) t‐test.
Chapter 9: Mann–Whitney Test
Table 9.1 Examples of studies that would be analyzed by a Mann–Whitney test.
Chapter 10: One‐Way Analysis of Variance (ANOVA) – Including Dunnett's and Tukey's Follow Up Tests
Table 10.1 Examples of studies that would be analyzed by a one‐way analysis of variance.
Table 10.2 Necessary sample sizes per group for a one‐way analysis of variance requiring 90% power.
Chapter 11: Kruskal–Wallis
Table 11.1 Examples of studies that would be analyzed by a Kruskal–Wallis test.
Table 11.2 Number of women reporting various levels of menstrual problems with different forms of contraception.
Table 11.3 Descriptive statistics for levels of menstrual problems in women using different methods of contraception.
Table 11.4 Numbers (and percentages) of women reporting lower or higher levels of menstrual problems with different forms of contraception.
Chapter 12: McNemar's Test
Table 12.1 Examples of studies that would be analyzed by McNemar's test.
Table 12.2 Responses to the question “Have you ever smoked cannabis?” when posed as part of a questionnaire or in a face‐to‐face interview.
Chapter 13: Paired T‐Test
Table 13.1 Examples of studies that would be analyzed by a paired t‐test.
Chapter 14: Wilcoxon Signed Rank Test
Table 14.1 Examples of studies that would be analyzed by a Wilcoxon signed rank test.
Table 14.2 Numbers of individuals expressing various levels of satisfaction with an appointment booking system, before and after change to system. Higher scores represent greater levels of satisfaction.
Chapter 15: Repeated Measures Analysis of Variance
Table 15.1 Examples of studies that would be analyzed by a repeated measures analysis of variance.
Table 15.2 Weights of participants (kg) at the three time points, and the differences in weight comparing all possible pairs of time points (first ten participants only). Each row represents one participant.
Table 15.3 Appropriate sample sizes for a repeated measures analysis of variance.
Table 15.4 Changes in weight (kg) when comparing various pairs of time points. Means, SDs and 95% confidence intervals (Bonferroni corrected) are shown.
Chapter 16: Friedman Test
Table 16.1 Examples of studies that would be analyzed by a Friedman test.
Table 16.2 Descriptive statistics for dental hygiene scores prior to, one week after, and three months after training in tooth brushing technique.
Table 16.3 Descriptive statistics for the individual changes in dental hygiene scores when comparing the pre‐training period to the one week and three month time points.
Chapter 18: Spearman Correlation
Table 18.1 Numbers and proportions with various levels of likelihood of using the internet to research psoriasis among groups separated by level of education (1 Lowest; 4 Highest).
Chapter 20: Cronbach's Alpha
Table 20.1 Cronbach's Alpha values when each question was omitted one at a time.
List of Illustrations
Chapter 2: Data Types
Figure 2.1 A continuously varying measure with (a) normal, (b) skewed, and (c) bimodal distribution. In (d) the highest and lowest values (tails) from an otherwise normal distribution are missing.
Figure 2.2 Histogram of “Long‐tailed” data, i.e. data that includes both low and high outlying values.
Figure 2.3 Normal probability plots of (a) normally distributed data and (b) long‐tailed data.
Chapter 3: Presenting and Summarizing Data
Figure 3.1 The quartiles for a data set with ranked values ranging from 1 to 447. The quartiles, median, and interquartile range (IQR) are indicated. The mean and SD are also included.
Figure 3.2 Histogram of body temperatures among individuals exposed to the common cold virus.
Figure 3.3 Bar chart of opinions concerning changes to an appointment booking system.
Chapter 4: Choosing a Statistical Test
Figure 4.1 Approximately linear relationship – (a) positive correlation and (b) negative correlation.
Figure 4.2 A cluster of points with outlier(s).
Figure 4.3 Relationships that are clearly not monotonic.
Figure 4.4 Monotonic, but clearly non‐linear relationships – (a) positive and (b) negative.
Figure 4.5 Data with distinct clusters.
Chapter 6: Common Issues and Pitfalls
Figure 6.1 Hypothetical confidence intervals for the difference in blood pressure between actively treated and control patients. All are statistically significant (the null hypothesis figure of zero is excluded from the confidence interval), but their interpretations differ. Vertical broken lines indicate limits beyond which differences would be of practical/clinical significance.
Figure 6.2 Hypothetical confidence intervals for the relative risk of death for actively treated patients compared to controls. All are statistically significant (the null hypothesis figure of one is excluded from the confidence interval), but their interpretations differ. Vertical broken lines indicate limits beyond which differences would be of practical/clinical significance.
Figure 6.3 Hypothetical confidence intervals for the difference in blood pressure between actively treated and control patients. All are statistically non‐significant (the null hypothesis figure of zero is included by the confidence interval), but their interpretations differ. Vertical broken lines indicate limits beyond which differences would be of practical/clinical significance.
Figure 6.4 Hypothetical confidence intervals for the relative risk of death for actively treated patients compared to controls. All are statistically non‐significant significant (the null hypothesis figure of one is included by the confidence interval), but their interpretations differ. Vertical broken lines indicate limits beyond which differences would be of practical/clinical significance.
Chapter 7: Contingency Chi‐Square Test
Figure 7.1 Experimental structure where a contingency chi‐square test is appropriate.
Figure 7.2 Clustered bar chart showing numbers with and without pneumonia among cardiac surgery patients who do or do not receive physiotherapy and exercise in addition to normal care.
Figure 7.3 Stacked bar chart showing proportions with and without pneumonia among cardiac surgery patients who do or do not receive physiotherapy and exercise in addition to normal care.
Chapter 8: Independent Samples (Two‐Sample) T‐Test
Figure 8.1 Circumstances when an independent samples t‐test is used.
Figure 8.2 Histograms of visual analog scores for pain 24 hours after abdominal or laparoscopic hysterectomy. (a) Abdominal, (b) Laparoscopic.
Figure 8.3 Normal probability plots of pain data following (a) Abdominal or (b) Laparoscopic hysterectomy.
Chapter 9: Mann–Whitney Test
Figure 9.1 Circumstances when a Mann–Whitney test is used.
Figure 9.2 Bar charts showing numbers of individuals reporting various levels of agreement with the statement “I would prefer to get stopping smoking advice face‐to‐face rather than some other way” displayed by respondents' sex.
Chapter 10: One‐Way Analysis of Variance (ANOVA) – Including Dunnett's and Tukey's Follow Up Tests
Figure 10.1 Structure of an experiment or survey where a one‐way analysis of variance would be appropriate.
Figure 10.2 Histograms of changes in blood cholesterol levels (mmol/l) over the three month study period for participants (a) making no dietary change, (b) using a cholesterol lowering spread in place of their normal spread, and (c) adding 35 g of nuts per day to their diet.
Chapter 11: Kruskal–Wallis
Figure 11.1 The structure of a study suitable for analysis by a Kruskal–Wallis test.
Figure 11.2 Stacked bar chart showing percentages of individuals reporting various levels of menstrual problems displayed by contraceptive type. Higher grades indicate greater problems.
Chapter 12: McNemar's Test
Figure 12.1 Structure of a study where a McNemar's test would be used.
Chapter 13: Paired T‐Test
Figure 13.1 Structure of a study where a paired t‐test would be used.
Figure 13.2 Dot plot showing changes in systolic blood pressure (mmHg) after one month's use of a combined oral contraceptive.
Figure 13.3 A ladder plot of systolic blood pressures (mmHg) before and after one month's use of a combined oral contraceptive.
Figure 13.4 Normal probability plot of the individual changes in systolic blood pressure used to test for any evidence of a long‐tailed distribution.
Chapter 14: Wilcoxon Signed Rank Test
Figure 14.1 Structure of a study where a Wilcoxon signed rank test would be used.
Figure 14.2 Proportions of participants expressing various levels of satisfaction with the appointment booking system before and after introduction of the new system. Higher scores represent higher levels of satisfaction.
Figure 14.3 Bar chart of individual changes in satisfaction scores following introduction of the new appointment booking system. Negative and positive figures represent reduced or increased satisfaction, respectively.
Chapter 15: Repeated Measures Analysis of Variance
Figure 15.1 Structure of a study where a repeated measures analysis of variance would be used.
Figure 15.2 Ladder plot showing individual weights (kg) for participants at the three time points in the study (first ten individuals only).
Figure 15.3 Weights (kg) of participants at the three time points in the study (mean ± SD). (Data for all 106 participants are described.)
Chapter 16: Friedman Test
Figure 16.1 Structure of a study where a Friedman test would be used.
Figure 16.2 Stacked bar chart showing dental hygiene scores prior to, one week after, and three months after training in tooth brushing technique.
Figure 16.3 Bar chart showing individual changes in dental hygiene scores one week after and three months after training in tooth brushing technique.
Figure 16.4 Mean dental hygiene scores (± SD) at the three time points in the study.
Chapter 17: Pearson Correlation
Figure 17.1 FEV1 values (liters) among residents versus number of years employment in a local cotton mill.
Figure 17.2 Possible relationships between FEV1 and period of employment in a cotton factory.
Chapter 18: Spearman Correlation
Figure 18.1 Proportions of new mothers in four age bands who provided varioussatisfaction ratings for the maternity service (1 = Strongly dissatisfied; 2 = Dissatisfied; 3 = Neutral; 4 = Generally satisfied; 5 = Very satisfied).
Figure 18.2 Data relationships that would not be suitable for Spearman correlation.
Figure 18.3 Proportions with various levels of willingness to research psoriasis on the Internet among four groups separated according to educational level (1 Lowest; 4 Highest).
Chapter 19: Logistic Regression
Figure 19.1 (a) A strong relationship between a binary outcome and a continuously varying measured factor, which is likely to be statistically significant. (b) A weaker relationship that is less likely to be significant.
Figure 19.2 Percentage of patients developing a new case of gallstones during the two years of the study. Patients have been divided into four groups based on dosage of drug received.
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A Practical Approach to Using Statistics in Health Research
From Planning to Reporting
Adam Mackridge
Philip Rowe
This edition first published 2018
© 2018 John Wiley & Sons, Inc.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Adam Mackridge and Philip Rowe to be identified as the authors of this work has been asserted in accordance with law.
Registered Office
John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
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For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats.
Limit of Liability/Disclaimer of Warranty
The publisher and the authors make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties; including without limitation any implied warranties of fitness for a particular purpose. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for every situation. In view of on-going research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. The fact that an organization or website is referred to in this work as a citation and/or potential source of further information does not mean that the author or the publisher endorses the information the organization or website may provide or recommendations it may make. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this works was written and when it is read. No warranty may be created or extended by any promotional statements for this work. Neither the publisher nor the author shall be liable for any damages arising here from.
Library of Congress Cataloguing-in-Publication Data
Names: Mackridge, Adam (Adam John), 1979- author. | Rowe, Philip, author.
Title: A practical guide to statistics for health research / by Adam Mackridge, Philip Rowe.
Description: Hoboken, NJ : Wiley, 2018. | Includes bibliographical references and index. |
Identifiers: LCCN 2017055955 (print) | LCCN 2018001635 (ebook) | ISBN 9781119383598 (pdf) | ISBN 9781119383611 (epub) | ISBN 9781119383574 (hardback)
Subjects: LCSH: Medical statistics. | Medicine–Research–Statistical methods. | BISAC: MEDICAL / Epidemiology. | MATHEMATICS / Probability & Statistics / General.
Classification: LCC R853.S7 (ebook) | LCC R853.S7 M33 2018 (print) | DDC 610.2/1–dc23
LC record available at https://lccn.loc.gov/2017055955
Cover Design: Wiley
Cover Image: © Tetra Images/Getty Images
About the Companion Website
This book is accompanied by Student companion websites:
www.wiley.com/go/Mackridge/APracticalApproachtoUsingStatisticsinHealthResearch
The website is aimed at helping readers of our book to understand how to use statistical tests correctly in their research. It is aimed at people working in health or social care who are interested in carrying out research and recognise the importance of statistical testing to provide robustness to the analysis and credibility to the findings. The book describes how to tackle the statistics for most common scenarios where the study design is fairly simple. The book is intended to help you use statistics in practice-focussed research and will not attempt to provide a full theoretical background to statistical methods. For that, you can turn to our sister publication (Rowe, 2015).
The book, supported by the materials on the website, set out the basic rules for using statistical tests, guides the reader through the process of deciding which test is most appropriate to their project and then provides a stepwise description of how to use the test.
The website contains three main components:
1. A checklist to help you determine which is the most appropriate test you're your project
2. Videos showing how to use key software (G*Power and SPSS) to determine sample sizes and carry out statistical analysis
3. The data files that we have taken examples from, so that you can see the raw data and try to replicate the tests that we have applied – if you get the same results as us, it's an excellent indication that you've got the hang of using the test
We have also provided SPSS data files where this software has been used in our examples. If you do not have access to this software, the instructions should still be useful; all packages work in essentially similar ways. The choice of statistical routine, the information you have to supply to allow the method to run correctly and the key pieces of output that you have to identify will not vary from package to package.
To allow you to do this, we have also provided the data in MS Excel format so that you can access this and copy it into the statistical programme of your choice. If you do not have access to MS Excel, you can open the files in MS Excel Viewer (available from www.microsoft.com).
Chapter 1Introduction
1.1 At Whom is This Book Aimed?
There are countless people working in areas related to health who are, or could be, involved in research. This certainly includes doctors, dentists, nurses, pharmacists, physiotherapists, midwives, and health visitors, but there are many other groups where this is equally true. The types of useful research they could be carrying out range from simple descriptions of the frequency of a particular condition in a specific location or describing local adherence to a health guideline through to more complex work involving comparisons between groups of patients, organizations, or geographical locales, etc. Based on our experience, one hurdle to involvement in carrying out this type of research is a lack of confidence in using statistics. This book is aimed at that group of health workers who are interested in building the evidence base to underpin excellent practice in their area, but who are struggling to design good quality analyses that stand up to scrutiny. It focuses on what you need to know to use statistics correctly to improve the robustness of your project without all of the theory and complex mathematics. It is not intended for anybody who already has significant research experience or for those who aim to become expert statisticians.
Our assumption is that any project our would‐be researcher undertakes will be fairly simple. We use the word “simple” advisedly. We do not use it to imply triviality or that such work is necessarily easy. By “simple,” we mean the opposite of complex. It can be very tempting to investigate simultaneously six different factors that might influence a particular clinical outcome or indeed to look at numerous outcomes for a given factor. Such complexity all too often leads to a tangled mass of data that defies clear interpretation. In order to produce clear and robust evidence, it is important to keep it simple and look at questions such as, whether people living in the more deprived part of your local town suffer increased levels of a particular condition, or whether patients counseled by nurses have a better understanding of their medication than those counseled by doctors. By keeping your design simple, as in these latter cases, any positive finding will be easily and unambiguously interpretable and much more likely to help develop best practice. Our motto is “Keep it simple – keep it clear.” In line with this philosophy, the statistical methods covered in this book are deliberately limited to those that consider the possibility that a single factor might have some influence upon a single clinical outcome.
1.2 At What Scale of Project is This Book Aimed?
The type of research project for which we envisage this book being useful is quite small: typically involving one or two researchers or something handled by a small team, with you, the reader, as the leader or a prominent member of the project team. Large, complex studies that involve significant funding (e.g. those funded by the UK's National Institute for Health Research) would almost certainly require the services of a specialist statistician, at which point this book becomes more of a guide to help you understand the techniques that may be used and the reasons for this, but it would be unlikely to cover all the statistical aspects of your project.
1.3 Why Might This Book be Useful for You?
The intention is to provide a handbook – something you can pick up, read the bit you need, and put down. You do not need to read it from cover to cover. It provides “how to” advice that covers the complete journey through a research project. How to:
Work out how much data you need to collect in order to provide a reliable answer to the question you have asked (sample size).
Identify an appropriate measure of effect size, and use that to determine whether any difference you have detected is large enough to be of practical significance (i.e. is a change in public policy or professional practice required?)
Identify appropriate statistical methods.
Apply the relevant statistical methods to your data using statistical software, mainly using SPSS.
Identify which bits of the software output you need to focus on and how to interpret them.
Determine whether your data indicates statistical significance (i.e. is there adequate evidence that outcomes really do differ between the groups studied?)
Determine whether your data indicates practical/clinical significance (i.e. is any difference between study groups big enough to be of practical consequence?)
Make sure any publications you write contain all the necessary statistical details.
This book is intended to help you use statistics in practice‐focused research and will not attempt to provide a full theoretical background to statistical methods. For that, you can turn to our sister publication (Rowe, 2015).1
1.4 How to Use This Book
Table 1.1 shows the ideal flow of events from first planning stages through to final analysis and reporting of your experimental data. It may not always be possible to adhere to every detail, but this describes an ideal approach, at which to aim.
Table 1.1 The ideal stage‐by‐stage flow of events for a research program.
Stage
Actions
Chapters to read
1
Identify the research question that is to be answered.
2
Make an outline plan of an experiment/trial/survey that will answer the question.
3 4 5
Decide which statistical test you will use. Determine the smallest effect size you want to be able to detect. Using the results from steps three and four, calculate appropriate sample sizes.
2, 3, and 4 Relevant chapter from 7 to 20
6
Perform the survey/experiment etc.
7
Describe the data obtained.
Chapter 3
and relevant
8
Carry out the test selected at step three, and draw your conclusions as to statistical and practical/clinical significance.
chapter from 7 to 20
9
If other interesting features emerged within the results, analyze these, but report them as exploratory (or secondary) analyses and do not place undue reliance on any conclusions.
Relevant chapter from 7 to 20
10
Consider whether you have increased the risk of generating false positive findings by carrying out multiple statistical tests.
5
11
Report your findings.
Relevant chapter from 7 to 20
Everybody should read the first six chapters of this book.
You can than select the appropriate chapter from the remainder of the book, which will talk you through sample size planning, execution of the statistical test, and interpretation and reporting of the results.
Chapter 20
