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E-grāmata: Successful Statistics for Nursing and Healthcare

  • Formāts: 232 pages
  • Izdošanas datums: 16-Sep-2017
  • Izdevniecība: Red Globe Press
  • Valoda: eng
  • ISBN-13: 9780230211599
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  • Formāts: 232 pages
  • Izdošanas datums: 16-Sep-2017
  • Izdevniecība: Red Globe Press
  • Valoda: eng
  • ISBN-13: 9780230211599
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Successful Statistics For Nursing and Healthcare helps students to gain an understanding of statistical methods within the evidence-based health care context. It builds confidence in descriptive statistics, concentrating on appropriate statistical tools and the techniques used in research, whilst gently introducing the mathematics required.

Papildus informācija

'This text is interesting and readable, possibly due to the number and nature of the healthcare examples used. It would be suitable for students and experienced healthcare professionals alike, as the accessible format allows the reader to target areas for indepth study, or make quick reference.' Susan Totty, Journal of Community Nursing '...this is a useful introductory text for students in the nursing and healthcare professions, and I would recommend in to anyone embarking on an introductory statistics course.' - Barbara Mullan, Journal of Advanced Nursing
Foreword ix
A Word About This Book xi
Setting the Scene - using Statistics in nursing and healthcare
1(6)
Monitoring bed use in hospital
2(1)
Hip replacement by keyhole surgery
3(1)
The effectiveness of nicotine replacement therapy
3(1)
Supporting older people in their own homes
4(1)
Judging if a body temperature is normal
5(1)
Exercise and health
5(1)
The uptake of cervical smear tests
6(1)
Using statistics
6(1)
Levels of Measurement - using numbers to measure qualities that vary
7(18)
Variables
7(2)
Measurement
9(3)
Collecting data, a practical example
12(4)
Tabulating data
16(3)
Some measurements in healthcare
19(6)
Populations and Samples - using sampling techniques to generate data
25(9)
Populations
26(1)
Samples
27(1)
Random samples
27(4)
Other sampling procedures
31(2)
How big should a sample be?
33(1)
Presenting Data - using tables and diagrams to summarise and display
34(25)
Frequency tables
35(6)
Diagrams for data
41(13)
Graphical presentations: informing or misleading?
54(5)
Summarising Data - using numbers to represent key features of data sets
59(15)
The key summary statistics
60(1)
Central location: mode, median and mean
61(5)
Dispersion: ranges, variance and standard deviation
66(6)
Parameters and statistics
72(1)
Sufficient levels of measurement
73(1)
Distributions and their Curves - using z-scores and the standard normal curve
74(20)
Frequency curves from histograms
75(1)
Describing frequency curves
76(5)
The normal distribution
81(3)
Standardised scores
84(10)
Is it Likely? - using ideas of probability and significance testing
94(17)
Calculating probability
95(4)
Combining probabilities
99(4)
Probabilities from z-scores
103(2)
Hypothesis testing
105(4)
Possible errors
109(2)
Estimating with Confidence - using sample data to estimate the mean of a population
111(11)
Confidence
112(1)
Unbiased estimators
113(2)
The distribution of sample means
115(2)
Confidence intervals for population means
117(1)
Calculating a confidence interval for a population mean
117(5)
Normal or Not? - using z-scores and t-scores to test if samples come from known populations
122(16)
A sample-of-one: is it normal?
123(3)
Differences between groups of people
126(3)
Testing if a `large' sample comes from a known population
129(2)
Testing if `large' samples come from the same population
131(4)
Testing if a `small' sample comes from a known normal population
135(1)
Two-tailed tests and one-tailed tests
136(2)
Different or Not? - using t-scores and ANOVA to compare samples, and techniques to judge clinical significance
138(14)
Small paired samples: are they significantly different?
139(2)
Degrees of freedom
141(2)
Comparing more than two samples at a time - ANOVA
143(4)
Other tests for differences between samples
147(1)
Deciding on clinically important differences: effect size
148(2)
Deciding on clinically important differences: confidence intervals
150(2)
The Significance of Proportions - using contingency tables and the chi-squared (χ2) test to compare proportions
152(11)
Contingency tables
153(4)
Calculating the χ2-score from a contingency table
157(2)
Applying the chi-squared test (χ2) for proportions
159(2)
Improving the result from a χ2 test
161(2)
The Strength of Relationships - using scatter diagrams, correlation and regression to quantify relationships between variables
163(16)
What correlation is
164(1)
Showing correlation graphically
164(2)
Correlation is not causation
166(2)
Calculating the level of correlation
168(3)
The significance of a correlation level
171(1)
Another way to calculate a level of correlation
171(3)
Regression
174(5)
Large Numbers of Variables - using multivariate methods of analysis
179(30)
Multiple regression
180(1)
Analysis of covariance
181(1)
Factor analysis
182(2)
Other methods
184(1)
APPENDICES
Appendix 1 Further Recommended Reading
185(3)
Books which include introductions to general statistical concepts and techniques
185(1)
Books which discuss statistical techniques in the context of nursing and healthcare
186(1)
Papers containing specific research which is referred to in the text
186(2)
Appendix 2 Those Greek Symbols...
188(1)
Appendix 3 Glossary
189(10)
Appendix 4 The ANOVA Estimates for Variance
199(1)
The within-sample estimate for σ2
199(1)
The between-sample estimate for σ2
199(1)
Appendix 5 Statistical Tables
200(9)
Random numbers
201(1)
The standard normal distribution
202(1)
The t-distribution
203(1)
The chi-squared (χ2) distribution
204(1)
Pearson's correlation coefficient (r)
205(1)
Spearman's rank-correlation coefficient (ρ)
206(1)
The F-distribution
207(2)
Index 209


ROGER WATSON is Director of the Graduate School of Nursing and Midwifery at the University of Sheffield, UK. His research interests include older people, nurse education and employment.

IAN ATKINSON is Senior Lecturer in Research Methods at the School of Health and Social Care, University of Teesside, UK. He is involved in teaching Quantitative Methods on pre and post-registration modules for health care professionals. He has many years of experience as a researcher in nursing and other health care topics.

PATRICIA EGERTON was, until recently, Head of Mathematics and Statistics at the University of Teesside, UK. She taught Mathematics undergraduates, as well as those from other disciplines (including Sport Science, Business, Science, Engineering and Computer Graphics). She was a University Teaching Fellow and was awarded a National Teaching Fellowship in 2000.