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Statistics for the Health Sciences: A Non-Mathematical Introduction [Minkštas viršelis]

  • Formatas: Paperback / softback, 584 pages, aukštis x plotis: 232x186 mm, weight: 1040 g
  • Išleidimo metai: 19-Mar-2012
  • Leidėjas: Sage Publications Ltd
  • ISBN-10: 1849203369
  • ISBN-13: 9781849203364
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 584 pages, aukštis x plotis: 232x186 mm, weight: 1040 g
  • Išleidimo metai: 19-Mar-2012
  • Leidėjas: Sage Publications Ltd
  • ISBN-10: 1849203369
  • ISBN-13: 9781849203364
Kitos knygos pagal šią temą:
Statistics for the Health Sciences is a highly readable and accessible textbook on understanding statistics for the health sciences, both conceptually and via the SPSS programme. The authors give clear explanations of the concepts underlying statistical analyses and descriptions of how these analyses are applied in health science research without complex maths formulae.





The textbook takes students from the basics of research design, hypothesis testing and descriptive statistical techniques through to more advanced inferential statistical tests that health science students are likely to encounter. The strengths and weaknesses of different techniques are critically appraised throughout, and the authors emphasise how they may be used both in research and to inform best practice care in health settings.





Exercises and tips throughout the book allow students to practice using SPSS. The companion website provides further practical experience of conducting statistical analyses. Features include:





multiple choice questions for both student and lecturer use

full Powerpoint slides for lecturers

practical exercises using SPSS

additional practical exercises using SAS and R





This is an essential textbook for students studying beginner and intermediate level statistics across the health sciences.

Recenzijos

Statistics for the Health Sciences engagingly presents the key analytic issues that students and professionals need to understand in the most accessible and vivid way possible. Full of real examples and practical exercises, the book successfully avoids getting bogged down with complex maths and formulae - Dennis Howitt at Loughborough University





The chapter overviews, absence of statistical formulae and use of appropriate examples and student exercises make this a very hands on and practical text -





Merryl E Harvey, Birmingham City University

About the Authors xiii
Preface xv
Acknowledgements xvii
Companion Website xix
1 An Introduction to the Research Process
1(20)
Overview
1(2)
The Research Process
3(2)
Concepts and Variables
5(3)
Levels of Measurement
8(2)
Hypothesis Testing
10(1)
Evidence-Based Practice
11(1)
Research Designs
11(7)
Summary
18(1)
Multiple Choice Questions
18(3)
2 Computer-Assisted Analysis
21(54)
Overview
21(1)
Overview of the Three Statistical Packages
22(4)
Introduction to SPSS
26(12)
Setting out Your Variables for Within- and Between-Group Designs
38(7)
Introduction to R
45(13)
Introduction to SAS
58(13)
Summary
71(1)
Exercises
72(3)
3 Descriptive Statistics
75(47)
Overview
75(1)
Analysing Data
76(1)
Descriptive Statistics
77(1)
Numerical Descriptive Statistics
78(4)
Choosing a Measure of Central Tendency
82(1)
Measures of Variation or Dispersion
82(3)
Deviations from the Mean
85(2)
Numerical Descriptives in SPSS
87(5)
Graphical Statistics
92(1)
Bar Charts
93(8)
Line Graphs
101(3)
Incorporating Variability into Graphs
104(1)
Generating Graphs with Standard Deviations in SPSS
105(1)
Graphs Showing Dispersion - Frequency Histogram
105(7)
Box-Plots
112(5)
Summary
117(1)
SPSS Exercise
118(1)
Multiple Choice Questions
118(4)
4 The Basis of Statistical Testing
122(43)
Overview
122(1)
Introduction
123(1)
Samples and Populations
124(14)
Distributions
138(11)
Statistical Significance
149(2)
Criticisms of NHST
151(5)
Generating Confidence Intervals in SPSS
156(5)
Summary
161(1)
SPSS Exercise
161(1)
Multiple Choice Questions
162(3)
5 Epidemiology
165(17)
Overview
165(1)
Introduction
166(1)
Estimating the Prevalence of Disease
167(1)
Difficulties in Estimating Prevalence
167(2)
Beyond Prevalence: Identifying Risk Factors for Disease
169(1)
Risk Ratios
170(1)
The Odds-Ratio
171(2)
Establishing Causality
173(1)
Case-Control Studies
174(2)
Cohort Studies
176(2)
Experimental Designs
178(1)
Summary
179(1)
Multiple Choice Questions
179(3)
6 Introduction to Data Screening and Cleaning
182(25)
Overview
182(1)
Introduction
183(1)
Minimising Problems at the Design Stage
184(1)
Entering Data into Databases/Statistical Packages
185(1)
The Dirty Dataset
186(1)
Accuracy
186(1)
Using Descriptive Statistics to Help Identify Errors
186(3)
Missing Data
189(5)
Spotting Missing Data
194(5)
Normality
199(3)
Screening Groups Separately
202(1)
Reporting Data Screening and Cleaning Procedures
202(2)
Summary
204(1)
Multiple Choice Questions
204(3)
7 Differences Between Two Groups
207(39)
Overview
207(1)
Introduction
208(2)
Conceptual Description of the t-Tests
210(3)
Generalising to the Population
213(1)
Independent Groups t-Test in SPSS
214(6)
Cohen's d
220(2)
Paired t-Test in SPSS
222(6)
Two-Sample z-Test
228(2)
Non-Parametric Tests
230(1)
Mann-Whitney: for Independent Groups
230(1)
Mann-Whitney Test in SPSS
230(7)
Wilcoxon Signed Rank Test: for Repeated Measures
237(1)
Wilcoxon Signed Rank Test in SPSS
237(4)
Adjusting for Multiple Tests
241(1)
Summary
241(1)
Multiple Choice Questions
241(5)
8 Differences Between Three or More Conditions
246(38)
Overview
246(1)
Introduction
247(2)
Conceptual Description of the (Parametric) ANOVA
249(1)
One-Way ANOVA
250(2)
One-way ANOVA in SPSS
252(6)
ANOVA Models for Repeated-Measures Designs
258(1)
Repeated-Measures ANOVA in SPSS
259(7)
Non-Parametric Equivalents
266(1)
The Kruskal-Wallis Test
266(1)
Kruskal-Wallis and the Median Test in SPSS
267(4)
The Median Test
271(2)
Friedman's ANOVA for Repeated Measures
273(1)
Friedman's ANOVA in SPSS
273(5)
Summary
278(1)
Multiple Choice Questions
278(6)
9 Testing Associations Between Categorical Variables
284(23)
Overview
284(1)
Introduction
285(2)
Rationale of Contingency Table Analysis
287(1)
Running the Analysis in SPSS
288(6)
Measuring Effect Size in Contingency Table Analysis
294(1)
Larger Contingency Tables
295(1)
Contingency Table Analysis Assumptions
296(2)
The Χ2 Goodness-of-Fit Test
298(2)
Running the Χ2 Goodness-of-Fit Test Using SPSS
300(3)
Summary
303(1)
Multiple Choice Questions
303(4)
10 Measuring Agreement: Correlational Techniques
307(38)
Overview
307(1)
Introduction
308(1)
Bivariate Relationships
309(5)
Perfect Correlations
314(3)
Calculating the Correlation Pearson's r Using SPSS
317(3)
How to Obtain Scatterplots
320(5)
Variance Explanation of r
325(2)
Obtaining Correlational Analysis in SPSS: Exercise
327(1)
Partial Correlations
328(3)
Shared and Unique Variance: Conceptual Understanding Relating to Partial Correlations
331(2)
Spearman's rho
333(2)
Other Uses for Correlational Techniques
335(1)
Reliability of Measures
336(1)
Internal Consistency
336(1)
Inter-Rater Reliability
337(1)
Validity
337(1)
Percentage Agreement
337(1)
Cohen's Kappa
338(1)
Summary
338(1)
Multiple Choice Questions
339(6)
11 Linear Regression
345(34)
Overview
345(1)
Introduction
346(5)
Linear Regression in SPSS
351(4)
Obtaining the Scatterplot with Regression Line and Confidence Intervals in SPSS
355(8)
Assumptions Underlying Linear Regression
363(1)
Dealing with Outliers
363(5)
What Happens if the Correlation between X and Y is Near Zero?
368(1)
Using Regression to Predict Missing Data in SPSS
369(3)
Prediction of Missing Scores on Cognitive Failures in SPSS
372(2)
Summary
374(1)
Multiple Choice Questions
375(4)
12 Standard Multiple Regression
379(23)
Overview
379(1)
Introduction
380(1)
Multiple Regression in SPSS
381(3)
Variables in the Equation
384(3)
The Regression Equation
387(1)
Predicting an Individual's Score
388(1)
Hypothesis Testing
388(3)
Other Types of Multiple Regression
391(3)
Hierarchical Multiple Regression
394(2)
Summary
396(1)
Multiple Choice Questions
397(5)
13 Logistic Regression
402(26)
Overview
402(1)
Introduction
403(1)
The Conceptual Basis of Logistic Regression
403(10)
Writing up the Result
413(1)
Logistic Regression with Multiple Predictor Variables
413(6)
Logistic Regression with Categorical Predictors
419(2)
Categorical Predictors with Three or More Levels
421(3)
Summary
424(1)
Multiple Choice Questions
424(4)
14 Interventions and Analysis of Change
428(44)
Overview
428(1)
Interventions
429(1)
How Do We Know Whether Interventions Are Effective?
429(3)
Randomised Control Trials (RCTs)
432(1)
Designing an RCT: CONSORT
433(3)
The CONSORT Flow Chart
436(3)
Important Features of an RCT
439(4)
Blinding
443(1)
Analysis of RCTs
444(2)
Running an ANCOVA in SPSS
446(2)
McNemar's Test of Change
448(1)
Running McNemar's Test in SPSS
449(3)
The Sign Test
452(1)
Running the Sign Test Using SPSS
453(1)
Intention to Treat Analysis
453(3)
Crossover Designs
456(1)
Single-Case Designs (N = 1)
457(6)
Generating Single-Case Design Graphs Using SPSS
463(5)
Summary
468(1)
SPSS Exercise
468(1)
Multiple Choice Questions
468(4)
15 Survival Analysis: An Introduction
472(30)
Overview
472(1)
Introduction
473(3)
Survival Curves
476(6)
The Kaplan-Meier Survival Function
482(2)
Kaplan-Meier Survival Analyses in SPSS
484(4)
Comparing Two Survival Curves - the Mantel-Cox Test
488(2)
Mantel-Cox Using SPSS
490(3)
Hazard
493(1)
Hazard Curves
493(1)
Hazard Functions in SPSS
494(1)
Writing Up a Survival Analysis
494(1)
Summary
495(1)
SPSS Exercise
496(1)
Multiple Choice Questions
496(6)
Answers to Activities and Exercises 502(40)
Glossary 542(10)
References 552(6)
Index 558