Contents:

Summary: Nursing is a growing area of higher education, in which an introduction to statistics is an essential component. There is currently a gap in the market for a 'user-friendly' book which is contextulised and targeted for nursing. Practical Statistics for Nursing and Health Care introduces statistical techniques in such a way that readers will easily grasp the fundamentals to enable them to gain the confidence and understanding to perform their own analysis. It also provides sufficient advice in areas such as clinical trials and epidemiology to enable the reader to critically appraise work published in journals such as the Lancet and British Medical Journal. * Covers all basic statistical concepts and tests * Is user-friendly - avoids excessive jargon * Includes relevant examples for nurses, including case studies and data sets * Provides information on further reading * Starts from first principles and progresses step by step * Includes 'advice on' sections for all of the tests described.
Cover -- Title Page -- Contents -- Preface -- 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 Calculators and computers in statistics -- 1.5 The purpose of this teXt -- 2 HEALTH CARE INVESTIGATIONS: MEASUREMENT AND SAMPLING CONCEPTS -- 2.1 Introduction -- 2.2 Populations -- 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 -- 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 -- 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 -- 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 -- 4.5 Frequency polygon and frequency curve -- 4.6 Scattergram -- 4.7 Circle or 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 (ECT) study -- 5.8 Summary -- 6 INTRODUCTION TO EPIDEMIOLOGY -- 6.1 Introduction -- 6.2 Measuring disease -- 6.3 Study designs - cohort studies.

6.4 Study designs - case-control studies -- 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 -- 7.3 Calculating the mean of grouped data -- 7.4 The median - a resistant statistic -- 7.5 The median of a frequency distribution -- 7.6 The 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 -- 8.5 Calculating the standard deviation from grouped data -- 8.6 Variance -- 8.7 An alternative formula for calculating the variance and standard deviation -- 8.8 Obtaining the standard deviation and sum of squares from a calculator -- 8.9 Degrees of freedom -- 8.10 The Coefficient of Variation (CV) -- 9 PROBABILITY AND THE NORMAL CURVE -- 9.1The meaning of probability -- 9.2 Compound probabilities -- 9.3 Critical probability -- 9.4 Probability distribution -- 9.5 The normal curve -- 9.6 Some properties of the normal curve -- 9.7 Standardizing the normal curve -- 9.8 Two-tailed or one-tailed? -- 9.9 Small samples: the t-distribution -- 9.10 Are our data 'normal ? -- 9.11 Dealing with 'non-normal data -- 10 HOW GOOD ARE OUR ESTIMATES?95 -- 10.1 Sampling error -- 10.2 The distribution of a sample mean -- 10.3 The confidence interval of a mean of a large sample -- 10.4 The confidence interval of a mean of a small sample -- 10.5 The difference between the means of two large samples -- 10.6 The difference between the means of two small samples -- 10.7 Estimating a proportion -- 10.8 The finite population correction -- 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-squared test -- 12.2 Calculating the test statistic -- 12.3 A practical eXample of a test for homogeneous frequencies -- 12.4 One degree of freedom - Yates correction -- 12.5 Goodness of fit tests -- 12.6 The contingency table - tests for association -- 12.7 The 'rows by columns (r x c) contingency table -- 12.8 Larger contingency tables -- 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 -- 13.5 The coefficient of determination r2 -- 13.6 The Spearman Rank Correlation Coefficient rs -- 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 -- 14.5 The line of least squares -- 14.6 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 -- 14.11 How we can '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 -- 15.4 Advice on using the Mann-Whitney U-test -- 15.5 More than two samples - the Kruskal-Wallace test -- 15.6 Advice on using the Kruskal-Wallace test -- 15.7 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 -- 15.11 The t-test for comparing the means of two small samples -- 15.12 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 -- 16.3 Procedure for computing ANOVA -- 16.4 The Tukey test -- 16.5 Further applications of ANOVA -- 16.6 Advice on using ANOVA -- APPENDICES -- AppendiX 1: Table of random numbers -- AppendiX 2: t-distribution -- AppendiX 3: χ2 -distribution -- AppendiX 4: Critical values of Spearman s Rank Correlation Coefficient -- AppendiX 5: Critical values of the product moment correlation coefficient -- AppendiX 6: Mann-Whitney U-test values (two-tailed test) -- AppendiX 7: Critical values of T in the WilcoXon test for matched pairs -- AppendiX 8: F-distribution -- AppendiX 9: Tukey test -- AppendiX 10: Symbols -- AppendiX 11: Leicestershire ECT study data -- AppendiX 12: How large should our samples be? -- Bibliography -- Index.

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Cover -- Title Page -- Contents -- Preface -- 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 Calculators and computers in statistics -- 1.5 The purpose of this teXt -- 2 HEALTH CARE INVESTIGATIONS: MEASUREMENT AND SAMPLING CONCEPTS -- 2.1 Introduction -- 2.2 Populations -- 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 -- 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 -- 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 -- 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 -- 4.5 Frequency polygon and frequency curve -- 4.6 Scattergram -- 4.7 Circle or 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 (ECT) study -- 5.8 Summary -- 6 INTRODUCTION TO EPIDEMIOLOGY -- 6.1 Introduction -- 6.2 Measuring disease -- 6.3 Study designs - cohort studies.

6.4 Study designs - case-control studies -- 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 -- 7.3 Calculating the mean of grouped data -- 7.4 The median - a resistant statistic -- 7.5 The median of a frequency distribution -- 7.6 The 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 -- 8.5 Calculating the standard deviation from grouped data -- 8.6 Variance -- 8.7 An alternative formula for calculating the variance and standard deviation -- 8.8 Obtaining the standard deviation and sum of squares from a calculator -- 8.9 Degrees of freedom -- 8.10 The Coefficient of Variation (CV) -- 9 PROBABILITY AND THE NORMAL CURVE -- 9.1The meaning of probability -- 9.2 Compound probabilities -- 9.3 Critical probability -- 9.4 Probability distribution -- 9.5 The normal curve -- 9.6 Some properties of the normal curve -- 9.7 Standardizing the normal curve -- 9.8 Two-tailed or one-tailed? -- 9.9 Small samples: the t-distribution -- 9.10 Are our data 'normal ? -- 9.11 Dealing with 'non-normal data -- 10 HOW GOOD ARE OUR ESTIMATES?95 -- 10.1 Sampling error -- 10.2 The distribution of a sample mean -- 10.3 The confidence interval of a mean of a large sample -- 10.4 The confidence interval of a mean of a small sample -- 10.5 The difference between the means of two large samples -- 10.6 The difference between the means of two small samples -- 10.7 Estimating a proportion -- 10.8 The finite population correction -- 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-squared test -- 12.2 Calculating the test statistic -- 12.3 A practical eXample of a test for homogeneous frequencies -- 12.4 One degree of freedom - Yates correction -- 12.5 Goodness of fit tests -- 12.6 The contingency table - tests for association -- 12.7 The 'rows by columns (r x c) contingency table -- 12.8 Larger contingency tables -- 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 -- 13.5 The coefficient of determination r2 -- 13.6 The Spearman Rank Correlation Coefficient rs -- 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 -- 14.5 The line of least squares -- 14.6 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 -- 14.11 How we can '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 -- 15.4 Advice on using the Mann-Whitney U-test -- 15.5 More than two samples - the Kruskal-Wallace test -- 15.6 Advice on using the Kruskal-Wallace test -- 15.7 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 -- 15.11 The t-test for comparing the means of two small samples -- 15.12 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 -- 16.3 Procedure for computing ANOVA -- 16.4 The Tukey test -- 16.5 Further applications of ANOVA -- 16.6 Advice on using ANOVA -- APPENDICES -- AppendiX 1: Table of random numbers -- AppendiX 2: t-distribution -- AppendiX 3: χ2 -distribution -- AppendiX 4: Critical values of Spearman s Rank Correlation Coefficient -- AppendiX 5: Critical values of the product moment correlation coefficient -- AppendiX 6: Mann-Whitney U-test values (two-tailed test) -- AppendiX 7: Critical values of T in the WilcoXon test for matched pairs -- AppendiX 8: F-distribution -- AppendiX 9: Tukey test -- AppendiX 10: Symbols -- AppendiX 11: Leicestershire ECT study data -- AppendiX 12: How large should our samples be? -- Bibliography -- Index.

Nursing is a growing area of higher education, in which an introduction to statistics is an essential component. There is currently a gap in the market for a 'user-friendly' book which is contextulised and targeted for nursing. Practical Statistics for Nursing and Health Care introduces statistical techniques in such a way that readers will easily grasp the fundamentals to enable them to gain the confidence and understanding to perform their own analysis. It also provides sufficient advice in areas such as clinical trials and epidemiology to enable the reader to critically appraise work published in journals such as the Lancet and British Medical Journal. * Covers all basic statistical concepts and tests * Is user-friendly - avoids excessive jargon * Includes relevant examples for nurses, including case studies and data sets * Provides information on further reading * Starts from first principles and progresses step by step * Includes 'advice on' sections for all of the tests described.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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