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A practical approach to using statistics in health research : from planning to reporting /
"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 investigat...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
Wiley,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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?