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Statistics for the Health Sciences
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Statistics for the Health Sciences
A Non-Mathematical Introduction



March 2012 | 584 pages | SAGE Publications Ltd

A companion website is available for this text

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.

 
PART ONE: AN INTRODUCTION TO THE RESEARCH PROCESS
 
Overview
 
The Research Process
 
Concepts and Variables
 
Levels of Measurement
 
Hypothesis Testing
 
Evidence-Based Practice
 
Research Designs
 
Multiple-Choice Questions
 
PART TWO: COMPUTER-ASSISTED ANALYSIS
 
Overview
 
Overview of the Three Statistical Packages
 
Introduction to SPSS
 
Setting out Your Variables for within - and between-Group Designs
 
Introduction to R
 
Introduction to SAS
 
Summary
 
Exercises
 
PART THREE: DESCRIPTIVE STATISTICS
 
Overview
 
Anaylsing Data
 
Descriptive Statistics
 
Numerical Descriptive Statistics
 
Choosing a Measure of Central Tendency
 
Measures of Variation or Dispersion
 
Deviations from the Mean
 
Numerical Descriptives in SPSS
 
Graphical Statistics
 
Bar Charts
 
Line Graphs
 
Incorporating Variability into Graphs
 
Generating Graphs with Standard Deviations in SPSS
 
Graphs Showing Dispersion - Frequency Histogram
 
Box-Plots
 
Summary
 
SPSS Exercise
 
Multiple Choice Questions
 
PART FOUR: THE BASIS OF STATISTICAL TESTING
 
Overview
 
Introduction
 
Samples and Populations
 
Distributions
 
Statistical Significance
 
Criticisms of NHST
 
Generating Confidence Intervals in SPSS
 
Summary
 
SPSS Exercise
 
Multiple Choice Questions
 
PART FIVE: EPIDEMIOLOGY
 
Overview
 
Introduction
 
Estimating the Prevalence of Disease
 
Difficulties in Estimating Prevalence
 
Beyond Prevalence: Identifying Risk Factors for Disease
 
Risk Ratios
 
The Odds-Ratio
 
Establishing Causality
 
Case-Control Studies
 
Cohort Studies
 
Experimental Designs
 
Summary
 
Multiple Choice Questions
 
PART SIX: INTRODUCTION TO DATA SCREENING AND CLEANING
 
Overview
 
Introduction
 
Minimising Problems at the Design Stage
 
Entering Data into Databases/Statistical Packages
 
The Dirty Dataset
 
Accuracy
 
Using Descriptive Statistics to Help Identify Errors
 
Missing Data
 
Spotting Missing Data
 
Normality
 
Screening Groups Separately
 
Reporting Data Screning and Cleaning Procedures
 
Summary
 
Multiple Choice Questions
 
PART SEVEN: DIFFERENCES BETWEEN TWO GROUPS
 
Overview
 
Introduction
 
Conceptual Description of the t-Tests
 
Generalising to the Population
 
Independent Groups t-Test in SPSS
 
Cohen's d
 
Paired t-Test in SPSS
 
Two-Sample z-Test
 
Non-Parametric Tests
 
Mann-Whitney: for Independent Groups
 
Mann-Whitney Test in SPSS
 
Wilcoxon Signed Rank Test: For Repeated Measures
 
Wilcoxon Signed Rank Test in SPSS
 
Adjusting for Multiple Tests
 
Summary
 
Multiple Choice Questions
 
PART EIGHT: DIFFERENCES BETWEEN THREE OR MORE CONDITIONS
 
Overview
 
Introduction
 
Conceptual Description of the (Parametric) ANOVA
 
One-Way ANOVA
 
One-way ANOVA in SPSS
 
ANOVA Models for Repeated-Measures Designs
 
Repeated Measures ANOVA in SPSS
 
Non-parametric Equivalents
 
The Kruskal-Wallis Test
 
Kruskal-Wallis and the Median Test in SPSS
 
The Median Test
 
Friedman's ANOVA for Repeated Measures
 
Friedman's ANOVA in SPSS
 
Summary
 
Multiple Choice Questions
 
PART NINE: TESTING ASSOCIATIONS BETWEEN CATEGORICAL VARIABLES
 
Overview
 
Introduction
 
Rationale of Contingency Table Analysis
 
Running the Analysis in SPSS
 
Measuring Effect Size in Contingency Table Analysis
 
Larger Contingency Tables
 
Contingency Table Analysis Assumptions
 
The X2 Goodness of Fit Test
 
Running the X2 Goodness of Fit Test Using SPSS
 
Summary
 
Multiple Choice Questions
 
PART TEN: MEASURING AGREEMENT: CORRELATIONAL TECHNIQUES
 
Overview
 
Introduction
 
Bivariate Relationships
 
Perfect Correlations
 
Calculating the Correlation Pearson's R Using SPSS.
 
How to obtain Scatterplots
 
Variance Explanation of R
 
Obtaining Correlational Analysis in SPSS: Exercise
 
Partial Correlations
 
Shared and Unique Variance: Conceptual Understanding Relating to Partial Corrections
 
Spearman's Rho
 
Other uses for Correlational Techniques
 
Reliability of Measures
 
Internal Consistency
 
Inter Rater Reliability
 
Validity
 
Percentage Agreement
 
Cohen's Kappa
 
Summary
 
Multiple Choice Questions
 
PART 11: LINEAR REGRESSION
 
Overview
 
Introduction
 
Linear Regression in SPSS
 
Obtaining teh Scatterplot with Regression Line and Confidence Intervals in SPSS
 
Assumptions Underlying Linear Regression
 
Dealing with Outliers
 
What happens if the Correlation Between X and Y is Near Zero?
 
Using Regression to Predict Missing Data in SPSS
 
Prediction of Missing Scores on Cognitive Failures in SPSS
 
Summary
 
Multiple-Choice Questions
 
PART TWELVE: STANDARD MULTIPLE REGRESSION
 
Overview
 
Introduction
 
Multiple Regression in SPSS
 
Variables in the Equation
 
The Regression Equation
 
Predicting an Individual's Score
 
Hypothesis Testing
 
Other Types of Multiple Regression
 
Hierarchical Multiple Regression
 
Summary
 
Multiple Choice Questions
 
PART THIRTEEN: LOGISTIC REGRESSION
 
Overview
 
Introduction
 
The Conceptual Basis of Logistic Regression
 
Writing up the Result
 
Logistic Regression with Multiple Predictor Variables
 
Logistic Regression with Categorical Predictors
 
Categorical Predictors with Three or More Levels
 
Summary
 
Multiple Choice Questions
 
Interventions and Analysis of Change
 
Overview
 
Interventions
 
How do we Know Whether Interventions are Effective?
 
Randomised Control Trials (RCTs)
 
Designing an RCT: CONSORT
 
The CONSORT Flow Chart
 
Important Features of an RCT
 
Blinding
 
Analysis of RCTs
 
Running an ANCOVA in SPSS
 
McNemar's Test of Change
 
Running McNemar's Test in SPSS
 
The Sign Test
 
Running the Sign Test using SPSS
 
Intention to Treat Analysis
 
Crossover Designs
 
Single Case Designs (N= 1)
 
Generating Single Case Design Graphs Using SPSS
 
Summary
 
SPSS Exercise
 
Multiple Choice Questions
 
PART FIFTEEN: SURVIVAL ANALYSIS: AN INTRODUCTION
 
Overview
 
Introduction
 
Survival Curves
 
The Kaplan-Meier Survival Function
 
Kaplan-Meier Survival Analyses in SPSS
 
Comparing Two Survival Curves - the Mantel-Cox test
 
Mantel-Cox using SPSS
 
Hazard
 
Hazard Curves
 
Hazard Functions in SPSS
 
Writing up a Survival Analysis
 
Summary
 
SPSS Exercise
 
Multiple Choice Questions

Well-written textbook about statistics for the Health Sciences. The Statistical Methods presented in this book are highly relevant even for other academic disciplines. The Chapters about Epidemiology, Interventions and Survival analysis are of particular interest.

Dr Knut Boge
Department of Health, Nutrition and Management, Oslo and Akershus Universitey College
November 24, 2013

Useful for analysis guidance using SPSS or just for handy statistical pointers in general.

Mr Philip Bright
Research Department, European School of Osteopathy
October 21, 2013

I really like this book which is easy to read and links statistics to the research process... demystifying along the way. I have recommended it to colleagues & will certainly guide students to this book.

Mr Ian Hamilton
School of Health, Community and Education Studies, Northumbria University
August 9, 2013

Statistics can terrify those who aren't confident with numbers; and this guide should help even the most nervous of students to grasp essential concepts and practice. The book is well-designed, clearly written and accessible. It is useful for tutors who also want to refresh their understanding of statistics.

Mr John Gough
Social and Community Studies, Coventry University
May 23, 2013

A useful text.

Janice Rattray
School of Nursing & Midwifery, Dundee University
May 21, 2013

A very good text to aid the students with data analysis.

Miss Zoe Taylor
Health , North Lindsey College
March 25, 2013

The following review regards the book “Statistics for the health sciences a non-mathematical introduction” by Christine P. Dancey, John G. Reidy and Richard Rowe.
There are many textbooks about statistics. The first question we should answer is whether all these books are necessary or not. To my point of view the answer is YES and I will explain why.
Statistic, as many others disciplines, can be approached in several ways and for different “population”. There are texts for beginners, who are commonly afraid of statistic, for students who have average knowledge of the discipline and for advanced ones. In addition some books are focus on particular statistical techniques which are used for ad hoc analysis and are not discussed in essential books. Crossing all the above conditions it would produce a multi dimensions table, with several boxes, each of these “designed” for specific subjects with particular areas of interest, levels of knowledge, approach to the problems, use of tools.
The aim of all the textbooks remain common: to explain the concepts, to support the decision of the readers and to facilitate the interpretation of the results. In other words, making a different example, it is like driving a car: a driver does not need to know how the motor works to drive the car from a place to another. He needs to have a clear idea of the rules of the street, to avoid accident, to know the road to reach the destination, to use the acceleration and the braking pedal etc. Once the driver knows how to drive and the general rules, he does not need to learn it again when he changes the car.
For beginners, especially for those who approach statistic for the first time, or for those who are not very confident with math, it is mainly important to explain the idea of the analysis, clarifying the meaning of an approach instead of another, and only secondly, if it is necessary, to explain the formula which “sustain” the idea. The most important thing is to maintain a clear idea of what to do and how to do. In addition it is useful to provide examples after the notions/concepts are explained.
Statistics for the health sciences a non-mathematical introduction is a book which beginners and intermediate students could find valuable for several reasons:
1) The text maintains what it says in the title “Statistics for the health sciences a non-mathematical introduction”. Beginners will not be afraid of math, in fact, formula are avoided. Readers will be focus on the ideas and on what to do in conducting a correct analysis.
2) The book introduces statistical terms and concepts using common words; moreover it explains how to conduct the analysis, step by step, with the support of pictures taken during the use of SPSS. This statistical software is getting very used not only by professional statistician but also by students who want to conduct some analysis. The program is easy to use because of the possibility to have menu and windows in the selection of the data analysis. The examples presented in the book guide the reader in experiencing, by hand, what it is said in the text, in an efficient way. The reader gets skilled in conducting analysis and becomes more confident with statistic.
3) The book provides also a short introduction of two statistical packages: R and SAS. R is a free statistical software which is getting quite common, especially in those people who are confident with statistic. The attempt of presenting it to beginners could stimulate the interest in those who would like to make a further step in statistic. SAS is a very famous and long traditional statistical software which is normally used by skilled statistician.
4) In the book it is refereed also to external references for specific aspects which are not included in the book but may be useful for those readers who want to experiment other statistical tools.
5) All the chapters have a nice but short overview that makes the readers more confident of what it is expected to find in the following pages. This helps the reader to be more confident in what to focus in reading the chapter.
6) There are many examples taken from the literature. This aspect will prove the readers how useful is statistic in the real world. In addition, the reader will have the possibility to identify, in the examples, similarities with its studies/analysis and to repeat them following the example of the book.
7) In the book there are exercises which can help the student to revise the materials. The online resources, for lecturers and students, expand the concepts treated in the text and provide other useful material for a better understanding.
8) The glossary provides an easy access to definition that sometime can be obscure or cause uncertainly. Readers could benefit from it when they need to revise the definition of a concept, for example when they read a paper and statistical terms are presented.
There are some suggestions which could be useful for improving the book in future revisions:
1) The authors did not mention other statistical programs which are quite common such as Stata and Epinfo. The first one has a large community of users, there are many online resources and textbooks which describe analysis and approach how to use the program. Epiinfo, the latest version is the 7th, can handle basic statistical techniques but has the advantage to be free and it is easy to use. It could be useful to introduce both these programs in the chapter where there Authors presented R and SAS.
2) The authors could introduce how to conduct the analysis using other software, but with the same examples. This would increase the possibility to find readers who could be interested in the book, but have experience with other statistical package.
3) The authors could introduce some summary tables or diagrams for the choice of a technique instead of another. They could draw guided diagrams in which the directions are based on the answers expected by the readers.

Dr. Gabriele Messina
Research Professor of Public Health
University of Siena

Professor Gabriele Messina
Molecular and Developmental Medicine, University of Siena
March 19, 2013

I really liked this text. I have used a lot of statistics textbooks but this one stands out as being very well designed. Firstly it is health based which suits my groups of students, covering things like testing interventions and survival analysis. Secondly it is not overly focussed on how to do stuff in SPSS or Excel as many texts are. Rather it explains the statistics and why different approaches are used and only afterwards shows students how to use SPSS to do the analysis. The screenshots and explanations for SPSS are very clear, particularly those that explain the complex output that SPSS generates and which many students find intimidating. The exercises are good - not too long or over complex and use of self study test questions at the end of each chapter will be helpful for students. It is quite detailed so will take students a long way, although some might be put off by that. Overall a great addition that I will be using in my Reserach Methods modules.

Dr Ivan Gee
Faculty of Health and Social Sciences, Liverpool John Moores University
March 5, 2013

A clearly presented text with lots of relevant examples used to illustrate the statistical methods

Mr Ben Jane
Faculty of Sport Media and Management, College of St Mark and St John
February 27, 2013

This is an excellent text which is clear in its approach. The screen shots of SPSS guide the student through the examples and the simplistic blue and grey colour scheme does not distract the reader, but rather makes the information clear and accessible.

Dr Peter Thain
Sport & Exercise Science & Sports Ther, Hertfordshire University
December 27, 2012

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Sample Materials & Chapters

Chapter 1