Practical Statistics for Nursing and Health Care. Jim Fowler

Practical Statistics for Nursing and Health Care - Jim Fowler


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a growth chart as an example of centiles in practice, based on WHO child growth standards (United Kingdom World Health Organisation 2009 ).

      This book on practical statistics for nurse and other health care professionals is an introductory text produced to assist with the growing demand for user‐friendly worked examples that are clear and easily understood, not only by students, but also by busy practitioners. We aim to provide an introduction to the statistical techniques that are in common use in order to provide a solid foundation for those who wish to develop their understanding and nursing proficiency with the possibility of pursuing an interest in research. A glossary of all the statistical symbols used throughout this book can be found in Appendix J.

      We acknowledge the contribution made by Professor Lou Cohen through his guidance over the years and whose original style persists in this book.

       Jim FowlerPhilip JarvisMel Chevannes

      Foreword to Students

      It is possible that you have recently enrolled on a nursing, midwifery or other health care course. It is likely that you did so with high ideals and a motivation to help humankind in the relief of suffering. It is also possible that you did not enjoy handling ‘numbers’ at school, and you have sought to escape from them. And now you have in your hands a textbook that might, at first sight, look terrifying.

      We wish to offer a word of reassurance. Modern health care is a ‘science’, and as such, involvement with numbers is inescapable. It was, after all, Florence Nightingale herself who recognized the importance of maintaining accurate numerical records. However, it might not be as bad as you fear. This book seeks to guide you through the subject ‘from scratch’, and we make no assumptions about your previous learning. The chapters develop in difficulty progressively through the book, and the material is extensively cross‐referenced. If you wish to ‘dip in’ to the text, you will find guidance on where to look back for under‐pinning explanations.

      It is very unlikely that you will ever need to know all that is contained in this book. In the first instance, it may be simply a supporting text to help you through a statistical element of your course. Later, you may be involved in a project of some kind, when this book can help you plan correctly the gathering, presentation and analysis of your data. Some of you may then venture into an area of research, in which case the more advanced chapters in this book will give you a sound foundation in the quantitative techniques that are required.

      We plead with you not to feel intimidated by the formulae that you see by flicking through these pages. How they are used is carefully described in each case. By persisting with the book from ‘square one’, reworking some of our own examples to make sure that you get the same answer, you will rapidly become sufficiently confident to apply them to your own data. And, who knows, you may even come to enjoy statistics!

      1.1 What Do we Mean by Statistics?

      Statistics are a familiar and accepted part of the modern world, and already intrude into the life of every nurse and health care worker. We have statistics in the form of patients registered at a GP practice or outpatient clinic; hospital measurements and records of weight, temperature, blood pressure and pulse rate; data collected from various surveys, censuses and clinical trials. It is impossible to imagine life without some form of statistical information being readily at hand.

      The word statistics is used in two senses. It refers to collections of quantitative information, and methods of handling that sort of data. A hospital's database listing the names, addresses and medical history records of all its registered patients, is an example of the first sense in which the word is used. Statistics also refers to the drawing of inferences about large groups on the basis of observations made on smaller ones. Estimating the relationship between smoking and the incidence of lung cancer illustrates the second sense in which the word is used.

      Statistics looks at ways of organizing, summarizing and describing quantifiable data, and methods of drawing inferences and generalizing upon them.

      There are two reasons why some knowledge of statistics is an important part of the competence of health care workers. First, statistical literacy is necessary if they are to read and evaluate reports and other literature critically and intelligently. Statements like ‘the reduction in mean number of migraine days was 3.5 (SD 2.5) for manual versus 2.4 (3.4) for sham (adjusted difference −1.4, 95% confidence interval −2.4 to −0.3; P = 0.005) at weeks 13 to 16’ (Xu et al. 2020), enable the reader to decide the justification of the claims made by the particular author.

      Statistics can help an investigator describe data, design experiments, and test hunches about relationships between things or events of interest. Statistics is a tool that helps acceptance or rejection of the hunches within recognized degrees of confidence. They help to answer questions like, ‘If my assertion is challenged, can I offer a reasonable defence?’

      It should be noted that statistics never prove anything. Rather, they indicate the likelihood of the results of an investigation being the product of chance.

      Today there is an abundance of internet sites and free statistical software packages that are capable of performing all the statistical analyses described in this book. However, we suggest caution against jumping straight into using these programs and internet sites without first understanding the underlying background and principles of a particular statistical technique. These tools can perform any analysis that you ask of them, but cannot provide the intelligent reasoning about whether the test is appropriate for the kind of data you are using. Moreover, a ‘screen shot’ of the analysis can be ambiguous and confusing if you do not understand the underlying principles. We feel this is best achieved by first familiarizing yourself with the techniques ‘long hand’, using a simple spreadsheet package working through our own examples and applying them to your own data. When using any statistical software for the first time it is best practice to ensure that you are able to replicate text book examples to check that you are using the program correctly and that the software is working correctly. All the calculations and worked examples in this book were performed using the LibreOffice 6.3.3.2 downloaded from https://www.libreoffice.org.

      Full


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