Making Sense Of Statistics In Healthcare

Book is unique in being written for people who want to be able to make sense of published studies, or embark on their own studies, without getting bogged down by the details of how to use specific methods.

Do you want to know what a parametric test is and when not to perform one? Do you get confused between odds ratios and relative risks? Want to understand the difference between sensitivity and specificity? Would like to find out what the fuss is about Bayes' theorem? Then this book is for you! Physicians need to understand the principles behind medical statistics. They don't need to learn the formula. The software knows it already! This book explains the fundamental concepts of medical statistics so that the learner will become confident in performing the most commonly used statistical tests. Each chapter is rich in anecdotes, illustrations, questions, and answers. Not enough? There is more material online with links to free statistical software, webpages, multimedia content, a practice dataset to get hands-on with data analysis, and a Single Best Answer questionnaire for the exam.

• An overview of descriptive and inferential statistics without formulas and computations. • Clear and to-the-point narrative makes this short book perfect for all courses in which statistics are discussed. • Helps statistics students who are struggling with the concepts. Shows them the meanings of the statistics they are computing. • This book is easy to digest because it is divided into short sections with review questions at the end of each section. • Running sidebars draw students’ attention to important concepts.

Americans are bombarded with statistical data each and every day, and healthcare professionals are no exception. All segments of healthcare rely on data provided by insurance companies, consultants, research firms, and the federal government to help them make a host of decisions regarding the delivery of medical services. But while these health pro

Many health care practitioners and researchers are aware of the need to employ factor analysis in order to develop more sensitive instruments for data collection. Unfortunately, factor analysis is not a unidimensional approach that is easily understood by even the most experienced of researchers. Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research presents a straightforward explanation of the complex statistical procedures involved in factor analysis. Authors Marjorie A. Pett, Nancy M. Lackey, and John J. Sullivan provide a step-by-step approach to analyzing data using statistical computer packages like SPSS and SAS. Emphasizing the interrelationship between factor analysis and test construction, the authors examine numerous practical and theoretical decisions that must be made to efficiently run and accurately interpret the outcomes of these sophisticated computer programs. This accessible volume will help both novice and experienced health care professionals to Increase their knowledge of the use of factor analysis in health care research Understand journal articles that report the use of factor analysis in test construction and instrument development Create new data collection instruments Examine the reliability and structure of existing health care instruments Interpret and report computer-generated output from a factor analysis run Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research offers a practical method for developing tests, validating instruments, and reporting outcomes through the use of factor analysis. To facilitate learning, the authors provide concrete testing examples, three appendices of additional information, and a glossary of key terms. Ideal for graduate level nursing students, this book is also an invaluable resource for health care researchers.

We are bombarded with statistical data each and every day, and healthcare professionals are no exception. All sectors of healthcare rely on data provided by insurance companies, consultants, research firms, and government to help them make a host of decisions regarding the delivery of medical services. But while these health professionals rely on data, do they really make the best use of the information? Not if they fail to understand whether the assumptions behind the formulas generating the numbers make sense. Not if they don’t understand that the world of healthcare is flooded with inaccurate, misleading, and even dangerous statistics. The purpose of this book is to provide members of medical and other professions, including scientists and engineers, with a basic understanding of statistics and probability together with an explanation and worked examples of the techniques. It does not seek to confuse the reader with in-depth mathematics but provides basic methods for interpreting data and making inferences. The worked examples are medically based, but the principles apply to the analysis of any numerical data.

'I couldn't imagine a better guidebook for making sense of a tragic and momentous time in our lives. Covid by Numbers is comprehensive yet concise, impeccably clear and always humane' Tim Harford How many people have died because of COVID-19? Which countries have been hit hardest by the virus? What are the benefits and harms of different vaccines? How does COVID-19 compare to the Spanish flu? How have the lockdown measures affected the economy, mental health and crime? This year we have been bombarded by statistics - seven day rolling averages, rates of infection, excess deaths. Never have numbers been more central to our national conversation, and never has it been more important that we think about them clearly. In the media and in their Observer column, Professor Sir David Spiegelhalter and RSS Statistical Ambassador Anthony Masters have interpreted these statistics, offering a vital public service by giving us the tools we need to make sense of the virus for ourselves and holding the government to account. In Covid by Numbers, they crunch the data on a year like no other, exposing the leading misconceptions about the virus and the vaccine, and answering our essential questions. This timely, concise and approachable book offers a rare depth of insight into one of the greatest upheavals in history, and a trustworthy guide to these most uncertain of times.

Focusing on quantative approaches to investigating problems, this title introduces the basics rules and principles of statistics, encouraging the reader to think critically about data analysis and research design, and how these factors can impact upon evidence-based practice.

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This book stems from the need I felt to learn, use and explain some fundamental concepts of medical statistics. Unfortunately, this topic is often hostile and poorly digested, and it is not easy to explain clinical decisions based on statistical concepts. But it is right that the attending physician tries to explain the possible therapeutic options with their pros and cons in clear language. I decided to avoid using complex mathematical formulas in writing the book since they are nothing more than a language. If this language is not known to the reader, the message cannot be clearly understood. Furthermore, there is a vast pool of available statistical software platforms that can return the test results. The user needs to know how to collect the data, which test to use and what significance to give to the results. Unlike other medical statistics books on the market, the text also deals with heuristics, biases and noise in judgment, which is fundamental to understanding the factors that influence our judgments when mathematical measurements and their analysis are impossible. In this text, I address the problems inherent in the composition of the samples compared, their homogeneity and how these obstacles can be mitigated by randomized sampling. In the book, I explain how there are various levels of knowledge related to that of the individual - doctor/patient - a large group of individuals - related to medical statistics - or a large population - the risk management of the population. Finally, a lesson from reading this text concerns the risks of attributing statistical data from a population to the individual. For example, suppose an individual contributes to the average group or population they are part of. In that case, it is essential to remember that the attribution of the characteristics of the average to the individual must be avoided. The content of the book is aimed at students of medicine, physiotherapy, nursing and pharmaceutical sciences. However, I am also sure that many doctors and healthcare professionals can benefit from reading.