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.
Table of Contents
About the Companion Website xv
1 Introduction 1
1.1 At Whom is This Book Aimed? 1
1.2 At What Scale of Project is This Book Aimed? 2
1.3 Why Might This Book be Useful for You? 2
1.4 How to Use This Book 3
1.5 Computer Based Statistics Packages 4
1.6 Relevant Videos etc. 5
2 Data Types 7
2.1 What Types of Data are There and Why Does it Matter? 7
2.2 Continuous Measured Data 7
2.2.1 Continuous Measured Data – Normal and Non‐Normal Distribution 8
2.2.2 Transforming Non‐Normal Data 13
2.3 Ordinal Data 13
2.4 Categorical Data 14
2.5 Ambiguous Cases 14
2.5.1 A Continuously Varying Measure that has been Divided into a Small Number of Ranges 14
2.5.2 Composite Scores with a Wide Range of Possible Values 15
2.6 Relevant Videos etc. 15
3 Presenting and Summarizing Data 17
3.1 Continuous Measured Data 17
3.1.1 Normally Distributed Data – Using the Mean and Standard Deviation 18
3.1.2 Data With Outliers, e.g. Skewed Data – Using Quartiles and the Median 18
3.1.3 Polymodal Data – Using the Modes 20
3.2 Ordinal Data 21
3.2.1 Ordinal Scales With a Narrow Range of Possible Values 22
3.2.2 Ordinal Scales With a Wide Range of Possible Values 22
3.2.3 Dividing an Ordinal Scale Into a Small Number of Ranges (e.g. Satisfactory/Unsatisfactory or Poor/Acceptable/Good) 22
3.2.4 Summary for Ordinal Data 23
3.3 Categorical Data 23
3.4 Relevant Videos etc. 24
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 25
4 Choosing a Statistical Test 27
4.1 Identify the Factor and Outcome 27
4.2 Identify the Type of Data Used to Record the Relevant Factor 29
4.3 Statistical Methods Where the Factor is Categorical 30
4.3.1 Identify the Type of Data Used to Record the Outcome 30
4.3.2 Is Continuous Measured Outcome Data Normally Distributed or Can It Be Transformed to Normality? 30
4.3.3 Identify Whether Your Sets of Outcome Data Are Related or Independent 31
4.3.4 For the Factor, How Many Levels Are Being Studied? 32
4.3.5 Determine the Appropriate Statistical Method for Studies with a Categorical Factor 32
4.4 Correlation and Regression with a Measured Factor 34
4.4.1 What Type of Data Was Used to Record Your Factor and Outcome? 34
4.4.2 When Both the Factor and the Outcome Consist of Continuous Measured Values, Select Between Pearson and Spearman Correlation 34
4.5 Relevant Additional Material 38
5 Multiple Testing 39
5.1 What Is Multiple Testing and Why Does It Matter? 39
5.2 What Can We Do to Avoid an Excessive Risk of False Positives? 40
5.2.1 Use of Omnibus Tests 40
5.2.2 Distinguishing Between Primary and Secondary/ Exploratory Analyses 40
5.2.3 Bonferroni Correction 41
6 Common Issues and Pitfalls 43
6.1 Determining Equality of Standard Deviations 43
6.2 How Do I Know, in Advance, How Large My SD Will Be? 43
6.3 One‐Sided Versus Two‐Sided Testing 44
6.4 Pitfalls That Make Data Look More Meaningful Than It Really Is 45
6.4.1 Too Many Decimal Places 45
6.4.2 Percentages with Small Sample Sizes 47
6.5 Discussion of Statistically Significant Results 47
6.6 Discussion of Non‐Significant Results 50
6.7 Describing Effect Sizes with Non‐Parametric Tests 51
6.8 Confusing Association with a Cause and Effect Relationship 52
7 Contingency Chi‐Square Test 55
7.1 When Is the Test Appropriate? 55
7.2 An Example 55
7.3 Presenting the Data 57
7.3.1 Contingency Tables 57
7.3.2 Clustered or Stacked Bar Charts 57
7.4 Data Requirements 59
7.5 An Outline of the Test 59
7.6 Planning Sample Sizes 59
7.7 Carrying Out the Test 60
7.8 Special Issues 61
7.8.1 Yates Correction 61
7.8.2 Low Expected Frequencies – Fisher’s Exact Test 61
7.9 Describing the Effect Size 61
7.9.1 Absolute Risk Difference (ARD) 62
7.9.2 Number Needed to Treat (NNT) 63
7.9.3 Risk Ratio (RR) 63
7.9.4 Odds Ratio (OR) 64
7.9.5 Case: Control Studies 65
7.10 How to Report the Analysis 65
7.10.1 Methods 65
7.10.2 Results 66
7.10.3 Discussion 67
7.11 Confounding and Logistic Regression 67
7.11.1 Reporting the Detection of Confounding 68
7.12 Larger Tables 69
7.12.1 Collapsing Tables 69
7 12.2 Reducing Tables 70
7.13 Relevant Videos etc. 71
8 Independent Samples (Two‐Sample) T‐Test 73
8.1 When Is the Test Applied? 73
8.2 An Example 73
8.3 Presenting the Data 75
8.3.1 Numerically 75
8.3.2 Graphically 75
8.4 Data Requirements 75
8.4.1 Variables Required 75
8.4.2 Normal Distribution of the Outcome Variable Within the Two Samples 75
8.4.3 Equal Standard Deviations 78
8.4.4 Equal Sample Sizes 78
8.5 An Outline of the Test 78
8.6 Planning Sample Sizes 79
8.7 Carrying Out the Test 79
8.8 Describing the Effect Size 79
8.9 How to Describe the Test, the Statistical and Practical Significance of Your Findings in Your Report 80
8.9.1 Methods Section 80
8.9.2 Results Section 80
8.9.3 Discussion Section 81
8.10 Relevant Videos etc. 81
9 Mann–Whitney Test 83
9.1 When Is the Test Applied? 83
9.2 An Example 83
9.3 Presenting the Data 85
9.3.1 Numerically 85
9.3.2 Graphically 85
9.3.3 Divide the Outcomes into Low and High Ranges 85
9.4 Data Requirements 86
9.4.1 Variables Required 86
9.4.2 Normal Distributions and Equality of Standard Deviations 87
9.4.3 Equal Sample Sizes 87
9.5 An Outline of the Test 87
9.6 Statistical Significance 87
9.7 Planning Sample Sizes 87
9.8 Carrying Out the Test 88
9.9 Describing the Effect Size 88
9.10 How to Report the Test 89
9.10.1 Methods Section 89
9.10.2 Results Section 89
9.10.3 Discussion Section 90
9.11 Relevant Videos etc. 91
10 One‐Way Analysis of Variance (ANOVA) – Including Dunnett’s and Tukey’s Follow Up Tests 93
10.1 When Is the Test Applied? 93
10.2 An Example 93
10.3 Presenting the Data 94
10.3.1 Numerically 94
10.3.2 Graphically 94
10.4 Data Requirements 94
10.4.1 Variables Required 94
10.4.2 Normality of Distribution for the Outcome Variable Within the Three Samples 95
10.4.3 Standard Deviations 96
10.4.4 Sample Sizes 98
10.5 An Outline of the Test 98
10.6 Follow Up Tests 98
10.7 Planning Sample Sizes 99
10.8 Carrying Out the Test 100
10.9 Describing the Effect Size 101
10.10 How to Report the Test 101
10.10.1 Methods 101
10.10.2 Results Section 102
10.10.3 Discussion Section 102
10.11 Relevant Videos etc. 103
11 Kruskal–Wallis 105
11.1 When Is the Test Applied? 105
11.2 An Example 105
11.3 Presenting the Data 106
11.3.1 Numerically 106
11.3.2 Graphically 107
11.4 Data Requirements 109
11.4.1 Variables Required 109
11.4.2 Normal Distributions and Standard Deviations 109
11.4.3 Equal Sample Sizes 110
11.5 An Outline of the Test 110
11.6 Planning Sample Sizes 110
11.7 Carrying Out the Test 110
11.8 Describing the Effect Size 111
11.9 Determining Which Group Differs from Which Other 111
11.10 How to Report the Test 111
11.10.1 Methods Section 111
11.10.2 Results Section 112
11.10.3 Discussion Section 113
11.11 Relevant Videos etc. 114
12 McNemar’s Test 115
12.1 When Is the Test Applied? 115
12.2 An Example 115
12.3 Presenting the Data 116
12.4 Data Requirements 116
12.5 An Outline of the Test 118
12.6 Planning Sample Sizes 118
12.7 Carrying Out the Test 119
12.8 Describing the Effect Size 119
12.9 How to Report the Test 119
12.9.1 Methods Section 119
12.9.2 Results Section 120
12.9.3 Discussion Section 120
12.10 Relevant Videos etc. 121
13 Paired T‐Test 123
13.1 When Is the Test Applied? 123
13.2 An Example 125
13.3 Presenting the Data 125
13.3.1 Numerically 125
13.3.2 Graphically 125
13.4 Data Requirements 126
13.4.1 Variables Required 126
13.4.2 Normal Distribution of the Outcome Data 126
13.4.3 Equal Standard Deviations 128
13.4.4 Equal Sample Sizes 128
13.5 An Outline of the Test 128
13.6 Planning Sample Sizes 129
13.7 Carrying Out the Test 129
13.8 Describing the Effect Size 129
13.9 How to Report the Test 130
13.9.1 Methods Section 130
13.9.2 Results Section 130
13.9.3 Discussion Section 131
13.10 Relevant Videos etc. 131
14 Wilcoxon Signed Rank Test 133
14.1 When Is the Test Applied? 133
14.2 An Example 134
14.3 Presenting the Data 134
14.3.1 Numerically 134
14.3.2 Graphically 136
14.4 Data Requirements 136
14.4.1 Variables Required 136
14.4.2 Normal Distributions and Equal Standard Deviations 137
14.4.3 Equal Sample Sizes 137
14.5 An Outline of the Test 137
14.6 Planning Sample Sizes 138
14.7 Carrying Out the Test 139
14.8 Describing the Effect Size 139
14.9 How to Report the Test 140
14.9.1 Methods Section 140
14.9.2 Results Section 140
14.9.3 Discussion Section 141
14.10 Relevant Videos etc. 141
15 Repeated Measures Analysis of Variance 143
15.1 When Is the Test Applied? 143
15.2 An Example 144
15.3 Presenting the Data 144
15.3.1 Numerical Presentation of the Data 145
15.3.2 Graphical Presentation of the Data 145
15.4 Data Requirements 146
15.4.1 Variables Required 146
15.4.2 Normal Distribution of the Outcome Data 148
15.4.3 Equal Standard Deviations 148
15.4.4 Equal Sample Sizes 148
15.5 An Outline of the Test 148
15.6 Planning Sample Sizes 149
15.7 Carrying Out the Test 150
15.8 Describing the Effect Size 150
15.9 How to Report the Test 151
15.9.1 Methods Section 151
15.9.2 Results Section 151
15.9.3 Discussion Section 152
15.10 Relevant Videos etc. 153
16 Friedman Test 155
16.1 When Is the Test Applied? 155
16.2 An Example 157
16.3 Presenting the Data 157
16.3.1 Bar Charts of the Outcomes at Various Stages 157
16.3.2 Summarizing the Data via Medians or Means 157
16.3.3 Splitting the Data at Some Critical Point in the Scale 159
16.4 Data Requirements 160
16.4.1 Variables Required 160
16.4.2 Normal Distribution and Standard Deviations in the Outcome Data 160
16.4.3 Equal Sample Sizes 160
16.5 An Outline of the Test 160
16.6 Planning Sample Sizes 161
16.7 Follow Up Tests 161
16.8 Carrying Out the Tests 162
16.9 Describing the Effect Size 162
16.9.1 Median or Mean Values Among the Individual Changes 162
16.9.2 Split the Scale 162
16.10 How to Report the Test 162
16.10.1 Methods Section 162
16.10.2 Results Section 163
16.10.3 Discussion Section 164
16.11 Relevant Videos etc. 164
17 Pearson Correlation 165
17.1 Presenting the Data 165
17.2 Correlation Coefficient and Statistical Significance 166
17.3 Planning Sample Sizes 167
17.4 Effect Size and Practical Relevance 167
17.5 Regression 169
17.6 How to Report the Analysis 170
17.6.1 Methods 170
17.6.2 Results 170
17.6.3 Discussion 171
17.7 Relevant Videos etc. 171
18 Spearman Correlation 173
18.1 Presenting the Data 173
18.2 Testing for Evidence of Inappropriate Distributions 174
18.3 Rho and Statistical Significance 174
18.4 An Outline of the Significance Test 175
18.5 Planning Sample Sizes 175
18.6 Effect Size 176
18.7 Where Both Measures Are Ordinal 176
18.7.1 Educational Level and Willingness to Undertake Internet Research – An Example Where Both Measures Are Ordinal 176
18.7.2 Presenting the Data 177
18.7.3 Rho and Statistical Significance 177
18.7.4 Effect Size 178
18.8 How to Report Spearman Correlation Analyses 178
18.8.1 Methods 178
18.8.2 Results 179
18.8.3 Discussion 180
18.9 Relevant Videos etc. 180
19 Logistic Regression 181
19.1 Use of Logistic Regression with Categorical Outcomes 181
19.2 An Outline of the Significance Test 182
19.3 Planning Sample Sizes 182
19.4 Results of the Analysis 184
19.5 Describing the Effect Size 184
19.6 How to Report the Analysis 185
19.6.1 Methods 185
19.6.2 Results 186
19.6.3 Discussion 186
19.7 Relevant Videos etc. 187
20 Cronbach’s Alpha 189
20.1 Appropriate Situations for the Use of Cronbach’s Alpha 189
20.2 Inappropriate Uses of Alpha 190
20.3 Interpretation 190
20.4 Reverse Scoring 191
20.5 An Example 191
20.6 Performing and Interpreting the Analysis 192
20.7 How to Report Cronbach’s Alpha Analyses 193
20.7.1 Methods Section 193
20.7.2 Results 194
20.7.3 Discussion 194
20.7 Relevant Videos etc. 195
Glossary 197
Videos 209
Index 211
