Statistics for the Health Sciences A Non-Mathematical Introduction

This is a highly accessible textbook on understanding statistics for the health sciences, both conceptually and via SPSS. The authors give clear explanations of the concepts underlying statistical analyzes and descriptions of how these analyzes are applied in health sciences research without complex statistical formulae. The book takes students from the basics of research design, hypothesis testing, and descriptive statistical techniques through to more advanced inferential statistical tests that health sciences students are likely to encounter. Exercises and tips throughout the book allow students to practice using SPSS.

An Introduction to the Research Process
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 of the Three Statistical Packages
Introduction to SPSS
Setting out Your Variable for within - and between - Group Designs.
Introduction to R
Introduction to SAS
Summary
PART THREE: DESCRIPTIVE STATISTICS
Descriptive Statistics
Numerical Descriptive Statistics
Choosing a Measure of Central Tendency
Measures of Variation or Dispersion
Deviations from the Mean
Numerical Descriptives in SPSS
Frequencies
Graphical Statistics
Bar Charts
Line Graphs
Incorporating Variability into Graphs
Generating Graphs with Standard Deviations in SPSS
Graphs Showing Dispersion - Frequency Histogram
Box-Plots
Multiple Choice Questions
PART FOUR: THE BASIS OF STATISTICAL TESTING
Overview
Introduction
Samples and Populations
Distributions
Statistical Significance
Criticisms of NHST
Summary
MCQs
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
Accuracy
Using Descriptive Statistics to Help Identify Errors
Missing Data
Spotting Missing Data
Normality
Reporting Data Screening 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
Cohen's d
Paired-T Test in SPSS
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
Partial Correlations
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
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
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
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
MCQs
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
Summary
Multiple Choice Questions