A practical approach to using statistics in health research : from planning to reporting / by Adam Mackridge, Philip Rowe.

Includes bibliographical references and index.

Chapter 1. Introduction
Chapter 2. Data types
Chapter 3. Presenting and summarizing data
Chapter 4. Choosing a statistical test
Chapter 5. Multiple testing
Chapter 6. Common issues and pitfalls
Chapter 7. Contingency chi-square test
Chapter 8. Independent samples (two-sample) t-test
Chapter 9. Mann-Whitney test
Chapter 10. One-way analysis of variance (ANOVA); Including Dunnett's and Tukey's follow up tests
Chapter 11. Kruskal-Wallis
Chapter 12. McNemar's test
Chapter 13. Paired t-test
Chapter 14. Wilcoxon signed rank test
Chapter 15. Repeated mesures analysis of variance
Chapter 16. Friedman test
Chapter 17. Pearson correlation
Chapter 18. Spearman correlation
Chapter 19. Logistic regression
Chapter 20. Cronbach's alpha. Cover; Title Page; Copyright; Contents; 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; 2.2.1 Continuous Measured Data
Normal and Non-Normal Distribution; 2.2.2 Transforming Non-Normal Data; 2.3 Ordinal Data; 2.4 Categorical Data; 2.5 Ambiguous Cases. 2.5.1 A Continuously Varying Measure that has been Divided into a Small Number of Ranges2.5.2 Composite Scores with a Wide Range of Possible Values; 2.6 Relevant Videos etc.; Chapter 3 Presenting and Summarizing Data; 3.1 Continuous Measured Data; 3.1.1 Normally Distributed Data
Using the Mean and Standard Deviation; 3.1.2 Data With Outliers, e.g. Skewed Data
Using Quartiles and the Median; 3.1.3 Polymodal Data
Using the Modes; 3.2 Ordinal Data; 3.2.1 Ordinal Scales With a Narrow Range of Possible Values; 3.2.2 Ordinal Scales With a Wide Range of Possible Values. 3.2.3 Dividing an Ordinal Scale Into a Small Number of Ranges (e.g. Satisfactory/Unsatisfactory or Poor/Acceptable/Good)3.2.4 Summary for 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. 4.3.1 Identify the Type of Data Used to Record the Outcome4.3.2 Is Continuous Measured Outcome Data Normally Distributed or Can It Be Transformed to Normality?; 4.3.3 Identify Whether Your Sets of Outcome Data Are Related or Independent; 4.3.4 For the Factor, How Many Levels Are Being Studied?; 4.3.5 Determine the Appropriate Statistical Method for Studies with a Categorical Factor; 4.4 Correlation and Regression with a Measured Factor; 4.4.1 What Type of Data Was Used to Record Your Factor and Outcome? 4.4.2 When Both the Factor and the Outcome Consist of Continuous Measured Values, Select Between Pearson and Spearman Correlation4.5 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?; 5.2.1 Use of Omnibus Tests; 5.2.2 Distinguishing Between Primary and Secondary/Exploratory Analyses; 5.2.3 Bonferroni Correction; 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?

"This book provides an outline with methodological steps of how to use statistics to analyze your research data. The book begins with a general introduction, which discusses what you should be trying to achieve with your statistical analysis. This involves describing the subjects you investigated and their outcomes, determining whether there is statistically significant evidence of differences in outcomes between groups of subjects, quantitatively describing effect sizes, and also determining whether any changes are large enough to be of clinical significance. Next, the authors cover data types and choosing statistical tests. This includes identifying the factor and outcome, and also identifying the type of data used to record the outcome. Readers are then introduced to multiple testing, the Chi-square test, and independent samples and the two-sample t-test. The Man-Whitney test is discussed, as well as the One-way ANOVA. Readers are taught how to Carrying out the Kruskal-Wallis test and the McNemar's test. The Paired t-test is covered, as well as how to carry out the Wilcoxon paired samples test. Readers are shown how to carry out the repeated measures ANOVA and the Friedman test. This includes discussion of merits of change in median, change in proportions in categories, and changes in high/low categories. The book concludes with a discussion on correlation and regression methods, and a detailed analysis on Cronbach's alpha"--

eng.