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Jim Fowler. Practical Statistics for Nursing and Health Care
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Practical Statistics for Nursing and Health Care
Preface. Background
Changes in the Context of Health Care
Technological Imperatives
Team Working
Modern Ways of Working to Make a Difference
Foreword to Students
1 Introduction. 1.1 What Do we Mean by Statistics?
1.2 Why Is Statistics Necessary?
1.3 The Limitations of Statistics
1.4 Performing Statistical Calculations
1.5 The Purpose of this Text
2 Health Care Investigations: Measurement and Sampling Concepts. 2.1 Introduction
2.2 Populations, Samples and Observations
2.3 Counting Things – The Sampling Unit
2.4 Sampling Strategy
2.5 Target and Study Populations
2.6 Sample Designs
2.7 Simple Random Sampling
2.8 Systematic Sampling
Example 2.1 Systematic Sampling Interval Calculation
2.9 Stratified Sampling
2.10 Quota Sampling
2.11 Cluster Sampling
2.12 Sampling Designs – Summary
2.13 Statistics and Parameters
2.14 Descriptive and Inferential Statistics
2.15 Parametric and Non‐Parametric Statistics
3 Processing Data. 3.1 Scales of Measurement
3.2 The Nominal Scale
3.3 The Ordinal Scale
3.4 The Interval Scale
3.5 The Ratio Scale
3.6 Conversion of Interval Observations to an Ordinal Scale
3.7 Derived Variables
Example 3.1 Proportions (Blood type Example)
3.8 Logarithms
3.9 The Precision of Observations
3.10 How Precise Should We Be?
3.11 The Frequency Table
3.12 Aggregating Frequency Classes
Example 3.2 Frequency Table
3.13 Frequency Distribution of Count Observations
3.14 Bivariate Data
4 Presenting Data. 4.1 Introduction
4.2 Dot Plot or Line Plot
4.3 Bar Graph
4.4 Histogram
Example 4.1 Histogram
4.5 Frequency Polygon and Frequency Curve
4.6 Centiles and Growth Charts
4.7 Scattergram
4.8 Circle or Pie Graph
Example 4.2 Pie graph
5 Clinical Trials. 5.1 Introduction
5.2 The Nature of Clinical Trials
5.3 Clinical Trial Designs
5.4 Psychological Effects and Blind Trials
5.5 Historical Controls
5.6 Ethical Issues
5.7 Case Study: Leicestershire Electroconvulsive Therapy Study
5.8 Summary
6 Introduction to Epidemiology. 6.1 Introduction
6.2 Measuring Disease
Example 6.1 Prevalence (Cataracts Example)
Example 6.2 Incidence (Breast Cancer Example)
6.3 Study Designs – Cohort Studies
Example 6.3 Cohort Study (Smoking and Heart Disease Example)
6.4 Study Designs – Case‐Control Studies
Example 6.4 Control Study (Smoking and Mouth Cancer Example)
6.5 Cohort or Case‐Control Study?
6.6 Choice of Comparison Group
6.7 Confounding
6.8 Summary
7 Measuring the Average. 7.1 What Is an Average?
7.2 The Mean
Example 7.1 Arithmetic Mean (Abdominal Cancer Example)
Example 7.2 Arithmetic Mean (Number of Casualties in Accident and Emergency Departments)
7.3 Calculating the Mean of Grouped Data
Example 7.3 Arithmetic Mean of Grouped Data (Birth Weights Example)
7.4 The Median – A Resistant Statistic
Example 7.4 Median (Gun Injury Example)
Example 7.5 Median Example
7.5 The Median of a Frequency Distribution
Example 7.6 Median when Several Observations have the Same Value
7.6 The Mode
Example 7.7 Mode
7.7 Relationship between Mean, Median and Mode
8 Measuring Variability. 8.1 Variability
8.2 The Range
8.3 The Standard Deviation
8.4 Calculating the Standard Deviation
Example 8.1 Standard Deviation
8.5 Calculating the Standard Deviation from Grouped Data
Example 8.2 Standard Deviation from Grouped Data (Baby Weights Example)
8.6 Variance
8.7 An Alternative Formula for Calculating the Variance and Standard Deviation
8.8 Degrees of Freedom
8.9 The Coefficient of Variation
Example 8.3 The Coefficient of Variation
9 Probability and the Normal Curve. 9.1 The Meaning of Probability
Example 9.1 Probability
9.2 Compound Probabilities
Example 9.2 Probability Distribution
Example 9.3 Calculating Compound Probability
9.3 Critical Probability
9.4 Probability Distribution
Example 9.4 Expected Frequencies
9.5 The Normal Curve
9.6 Some Properties of the Normal Curve
9.7 Standardizing the Normal Curve
Example 9.5 The Normal Distribution (Weights Example)
9.8 Two‐Tailed or One‐Tailed?
9.9 Small Samples: The t‐Distribution
Example 9.6 The t‐Distribution
Example 9.7 The t‐Distribution (Temperature Example)
9.10 Are our Data Normally Distributed?
Example 9.8 Assessing Normal Distribution Assumptions
9.11 Dealing with ‘Non‐normal’ Data
10 How Good Are our Estimates?
10.1 Sampling Error
10.2 The Distribution of a Sample Mean
Example 10.1 Standard Error (Baby Weights Example)
10.3 The Confidence Interval of a Mean of a Large Sample
Example 10.2 Confidence Interval of a Mean of a Large Sample (Baby Weights Example)
10.4 The Confidence Interval of a Mean of a Small Sample
Example 10.3 Confidence Interval of a Mean of a Small Sample
10.5 The Difference between the Means of Two Large Samples
Example 10.4 Confidence Interval of the Difference between the Means of Two Large Samples
10.6 The Difference between the Means of Two Small Samples
Example 10.5 Confidence Interval of the Difference between the Means of Two Small Samples
10.7 Estimating a Proportion
Example 10.6 Confidence Interval for a Proportion
10.8 The Finite Population Correction
Example 10.7 Confidence Interval for a Proportion from a Finite Population
11 The Basis of Statistical Testing. 11.1 Introduction
11.2 The Experimental Hypothesis
11.3 The Statistical Hypothesis
11.4 Test Statistics
11.5 One‐Tailed and Two‐Tailed Tests
11.6 Hypothesis Testing and the Normal Curve
11.7 Type 1 and Type 2 Errors
11.8 Parametric and Non‐parametric Statistics: Some Further Observations
11.9 The Power of a Test
12 Analysing Frequencies. 12.1 The Chi‐Square Test
12.2 Calculating the Test Statistic
Example 12.1 Chi‐Squared Test Single Classification
12.3 A Practical Example of a Test for Homogeneous Frequencies
Example 12.2 Chi‐Squared Test for Homogenous Frequencies
12.4 One Degree of Freedom – Yates' Correction
Example 12.3 Chi‐Squared Test with Yates' Correction
12.5 Goodness of Fit Tests
Example 12.4 Chi‐Squared Test (Blood Group Example)
12.6 The Contingency Table – Tests for Association
Example 12.5 Chi‐Squared Test for 2 × 2 Table
Example 12.6 Chi‐Squared Test for 2 × 2 Table (Wearing Helmets on a Bicycle and Head Injury)
Example 12.7 Chi‐Squared Test for 2 × 2 Table (Smoking and Mouth Cancer)
12.7 The ‘Rows by Columns’ (r × c) Contingency Table
Example 12.8 Chi‐Squared Test for 2 × 3 Table
12.8 Larger Contingency Tables
Example 12.9 Chi‐Squared Test for 3 × 3 Table
Example 12.10 Chi‐Squared Test for 2 × 6 Table
12.9 Advice on Analysing Frequencies
13 Measuring Correlations. 13.1 The Meaning of Correlation
13.2 Investigating Correlation
13.3 The Strength and Significance of a Correlation
13.4 The Product Moment Correlation Coefficient
Example 13.1 The Product Moment Correlation Coefficient
13.5 The Coefficient of Determination r2
13.6 The Spearman Rank Correlation Coefficient rs
Example 13.2 The Spearman Rank Correlation Coefficient
13.7 Advice on Measuring Correlations
14 Regression Analysis. 14.1 Introduction
14.2 Gradients and Triangles
14.3 Dependent and Independent Variables
14.4 A Perfect Rectilinear Relationship
Example 14.1 Linear Relationship
14.5 The Line of Least Squares
14.6 Simple Linear Regression
Example 14.2 Simple Linear Regression
14.7 Fitting the Regression Line to the Scattergram
14.8 Regression for Estimation
14.9 The Coefficient of Determination in Regression
14.10 Dealing with Curved Relationships
Example 14.3 A Curved Relationship
14.11 How Can We ‘Straighten Up’ Curved Relationships?
14.12 Advice on Using Regression Analysis
15 Comparing Averages. 15.1 Introduction
15.2 Matched and Unmatched Observations
15.3 The Mann–Whitney U‐Test for Unmatched Samples
Example 15.1 The Mann–Whitney U‐test
15.4 Advice on Using the Mann–Whitney U‐Test
15.5 More than Two Samples – The Kruskal–Wallis Test
Example 15.2 The Kruskal‐Wallis Test
15.6 Advice on Using the Kruskal–Wallis Test
15.7 The Wilcoxon Test for Matched Pairs
Example 15.3 The Wilcoxon Test for Matched Pairs
15.8 Advice on Using the Wilcoxon Test for Matched Pairs
15.9 Comparing Means – Parametric Tests
15.10 The z‐Test for Comparing the Means of Two Large Samples
Example 15.4 The z‐Test for Comparing the Means of Two Large Samples
15.11 The t‐Test for Comparing the Means of Two Small Samples
Example 15.5 The t‐Test for Comparing the Means of Two Small Samples
15.12 The t‐Test for Matched Pairs
Example 15.6 The t‐Test for Matched Pairs
15.13 Advice on Comparing Means
16 Analysis of Variance – ANOVA
16.1 Why Do We Need ANOVA?
16.2 How ANOVA Works
Example 16.1 Analysis of Variance (ANOVA)
16.3 Procedure for Computing ANOVA
Example 16.2 Analysis of Variance (ANOVA – Blood Pressure Example)
16.4 The Tukey Test
16.5 Further Applications of ANOVA
16.6 Advice on Using ANOVA
Appendix A Table of Random Numbers
Appendix B t‐Distribution
Appendix C χ2‐Distribution
Appendix D Critical Values of Spearman's Rank Correlation Coefficient
Appendix E Critical Values of the Product Moment Correlation Coefficient
Appendix F Mann–Whitney U ‐test Values (Two‐Tailed Test) P = 0.05
Appendix G Critical Values of T in the Wilcoxon Test for Matched Pairs
Appendix H F‐Distribution
Appendix I Tukey Test
Appendix J Symbols
Appendix K. Leicestershire ECT Study Data: Subgroup with Depressive Illness
Appendix L How Large Should Our Samples Be? L.1 Introduction
L.2 Proportions
Example L.1 Calculating a Sample Size for a Proportion
L.3 Calculating Sample Size for a Quantitative Measure
Example L.2 Calculating Sample Size for a Quantitative Measurement
Bibliography
Index. a
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