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Introduction to Research Methods and Data Analysis in the Health Sciences [Hardback]

  • Formāts: Hardback, 328 pages, height x width: 246x174 mm, weight: 770 g, 71 Tables, black and white; 82 Line drawings, black and white
  • Izdošanas datums: 20-Jun-2014
  • Izdevniecība: Routledge
  • ISBN-10: 0415734088
  • ISBN-13: 9780415734080
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  • Formāts: Hardback, 328 pages, height x width: 246x174 mm, weight: 770 g, 71 Tables, black and white; 82 Line drawings, black and white
  • Izdošanas datums: 20-Jun-2014
  • Izdevniecība: Routledge
  • ISBN-10: 0415734088
  • ISBN-13: 9780415734080
Citas grāmatas par šo tēmu:
Whilst the health sciences are a broad and diverse area, and includes public health, primary care, health psychology, psychiatry and epidemiology, the research methods and data analysis skills required to analyse them are very similar. Moreover, the ability to appraise and conduct research is emphasised within the health sciences - and students are expected increasingly to do both. Introduction to Research Methods and Data Analysis in the Health Sciences presents a balanced blend of quantitative research methods, and the most widely used techniques for collecting and analysing data in the health sciences. Highly practical in nature, the book guides you, step-by-step, through the research process, and covers both the consumption and the production of research and data analysis. Divided into the three strands that run throughout quantitative health science research - critical numbers, critical appraisal of existing research, and conducting new research--Provided by publisher. Whilst the ‘health sciences’ are a broad and diverse area, and includes public health, primary care, health psychology, psychiatry and epidemiology, the research methods and data analysis skills required to analyse them are very similar. Moreover, the ability to appraise and conduct research is emphasised within the health sciences – and students are expected increasingly to do both. Introduction to Research Methods and Data Analysis in the Health Sciences presents a balanced blend of quantitative research methods, and the most widely used techniques for collecting and analysing data in the health sciences. Highly practical in nature, the book guides you, step-by-step, through the research process, and covers both the consumption and the production of research and data analysis. Divided into the three strands that run throughout quantitative health science research – critical numbers, critical appraisal of existing research, and conducting new research – this accessible textbook introduces:Descriptive statistics Measures of association for categorical and continuous outcomes Confounding, effect modification, mediation and causal inference Critical appraisal Searching the literature Randomised controlled trials Cohort studies Case-control studies Research ethics and data management Dissemination and publication Linear regression for continuous outcomes Logistic regression for categorical outcomes. A dedicated companion website offers additional teaching and learning resources for students and lecturers, including screenshots, R programming code, and extensive self-assessment material linked to the book’s exercises and activities.Clear and accessible with a comprehensive coverage to equip the reader with an understanding of the research process and the practical skills they need to collect and analyse data, it is essential reading for all undergraduate and postgraduate students in the health and medical sciences.
List of figures
xi
List of tables
xiv
List of boxes
xvi
Acknowledgements xviii
PART I Introduction
1(14)
1 Evidence-based health research
3(12)
Preliminary exercise and installing R
5(1)
How to install the R program
6(5)
Answers to the formative numeracy exercise using R
11(1)
Further reading
12(3)
PART II Critical numbers
15(88)
2 Descriptive statistics part 1: levels of measurement and measures of central tendency
17(7)
Intended learning outcomes
17(1)
Introducing key terms
17(1)
Levels of measurement
18(4)
Why is determining the level of measurement important?
22(1)
Are levels of measurement potentially misleading?
23(1)
3 Descriptive statistics part 2: measures of dispersion
24(23)
Intended learning outcomes
24(1)
Mean
25(1)
Sample and population mean
26(1)
Median
26(2)
Mode
28(1)
Range
28(2)
The normal distribution
30(1)
Other kinds of distributions
31(1)
Measures of dispersion
32(11)
Transformations
43(3)
Summary
46(1)
4 Measures of association for categorical outcomes
47(19)
Introduction
48(1)
Intended learning outcomes
48(1)
Risk and odds
49(2)
Calculating absolute risk
51(2)
Number needed to treat (NNT) and number needed to harm (NNH)
53(5)
Odds ratios
58(6)
Summary
64(2)
5 Measures of association for continuous outcomes
66(17)
Intended learning outcomes
66(1)
Differences between two means
67(15)
Summary
82(1)
6 Confounding, effect modification, mediation and causal inference
83(20)
Intended learning outcomes
83(1)
Introducing key terms
83(1)
Confounding variables
84(5)
Effect modifying variables
89(6)
Mediating variables
95(3)
Antecedent variables
98(1)
Causal variables
99(4)
PART III Critical appraisal of existing research
103(110)
7 Literature reviewing and database searching
105(26)
Intended learning outcomes
105(1)
Narrative reviews of the literature
106(1)
Systematic reviews of the literature
106(16)
Social networking approaches to searching the literature
122(4)
Bibliographic software
126(3)
Summary
129(1)
Web links
130(1)
8 Randomised controlled trials
131(21)
Intended learning outcomes
132(1)
Introducing key terms
132(2)
A cognitive behavioural intervention to reduce sexually transmitted infections among gay men: randomised trial
134(8)
Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised controlled trial
142(4)
Effect of physical activity on cognitive function in older adults at risk for Alzheimer's disease: a randomised trial
146(5)
Summary
151(1)
9 Cohort studies
152(21)
Intended learning outcomes
153(1)
What are the essential features of a cohort study?
153(1)
Environmental tobacco smoke and tobacco related mortality in a prospective study of Californians, 1960--98
154(6)
Joint effect of cigarette smoking and alcohol consumption on mortality
160(6)
Institutional risk factors for norovirus outbreaks in Hong Kong elderly homes: a retrospective cohort study
166(6)
Cohorts do not need to represent populations: a note about internal and external validity of cohort studies
172(1)
Summary
172(1)
10 Case-control studies
173(16)
Intended learning outcomes
174(1)
What are the essential features of a case-control study?
174(1)
Introducing key terms
175(1)
Pet birds and risk of lung cancer in Sweden: a case-control study
176(4)
Association between maternal sleep practices and risk of late stillbirth: a case-control study
180(5)
Mobile phone use and brain tumors in children and adolescents: a multicentre case-control study
185(3)
Summary
188(1)
11 Research ethics and data management
189(24)
Intended learning outcomes
189(1)
Introducing key terms
190(1)
Practical example of an IRAS application
191(15)
Questionnaires
206(1)
Data management
207(3)
Summary
210(3)
PART IV Conducting new research
213(62)
12 Dissemination and publication
215(18)
Intended learning outcomes
215(1)
Types of published writing
215(3)
The structure of original research articles
218(12)
Choosing a journal
230(3)
13 Linear regression for continuous outcomes
233(27)
Intended learning outcomes
233(1)
Introducing key terms
234(1)
The linear regression equation
234(1)
The method of least squares
235(1)
Linear regression in R
235(2)
What do the regression coefficients mean?
237(1)
Interpreting the output
237(2)
The F-statistic and equivalence to ANOVA
239(1)
Standardising predictor variables
240(1)
Regression using centred age
240(2)
Multiple linear regression
242(1)
Running a multiple regression in R
243(2)
Hierarchical multiple regression
245(1)
Categorical predictor variables
246(1)
Adjusting for possible mediators to evaluate attenuation of an effect
247(2)
Adding interaction terms to evaluate effect modification
249(1)
R-squared
250(1)
Checking assumptions
250(5)
Transforming variables
255(1)
Outcome variables with a preponderance of zeros
256(1)
Non-normality in outcome variables
256(3)
Standardised regression coefficients
259(1)
Summary
259(1)
Further reading
259(1)
14 Logistic regression for categorical outcomes
260(15)
Intended learning outcomes
260(1)
Why linear regression is not suitable for dichotomous outcomes
260(2)
Introducing logistic regression
262(1)
Preparing for logistic regression
263(1)
The logistic regression equation
264(1)
Recap: log odds, odds, probability
264(1)
Getting started with logistic regression
265(1)
Results from a simple logistic regression model
266(5)
Comparing the goodness of fit of two logistic regression models: loglikelihood test
271(1)
Identifying whether a variable significantly improves fit of the model: Wald test
272(1)
Obtaining predicted probabilities
273(1)
Summary
274(1)
Further reading
274(1)
Appendix 1 Critical values for the t-test 275(1)
Appendix 2 Critical values for the F-test 276(1)
Appendix 3 Table of z-values 277(1)
Appendix 4 T/u table for Mann Whitney U test 278(1)
Appendix 5 Critical values for the Wilcoxon test 279(1)
Appendix 6 Consent form 280(1)
Appendix 7 Statistical power 281(1)
Appendix 8 Validity and bias 282(7)
Glossary 289(5)
References 294(6)
Index 300
Gareth Hagger-Johnson is a Senior Research Associate in the Department of Epidemiology and Public Health at University College London, UK.