In social sciences, education, and public health research, researchers often conduct small pilot studies (or may have planned for a larger sample but lost too many cases due to attrition or missingness), leaving them with a smaller sample than they expected and thus less power for their statistical analyses. Similarly, researchers may find that their data are not normally distributed -- especially in clinical samples -- or that the data may not meet other assumptions required for parametric analyses. In these situations, nonparametric analytic strategies can be especially useful, though they are likely unfamiliar. A clearly written reference book, Data Analysis with Small Samples and Non-Normal Data offers step-by-step instructions for each analytic technique in these situations. Researchers can easily find what they need, matching their situation to the case-based scenarios that illustrate the many uses of nonparametric strategies. Unlike most statistics books, this text is written in straightforward language (thereby making it accessible for nonstatisticians) while providing useful information for those already familiar with nonparametric tests. Screenshots of the software and output allow readers to follow along with each step of an analysis. Assumptions for each of the tests, typical situations in which to use each test, and descriptions of how to explain the findings in both statistical and everyday language are all included for each nonparametric strategy. Additionally, a useful companion website provides SPSS syntax for each test, along with the data set used for the scenarios in the book. Researchers can use the data set, following the steps in the book, to practice each technique before using it with their own data. Ultimately, the many helpful features of this book make it an ideal long-term reference for researchers to keep in their personal libraries.
Proven Material for a Course on the Introduction to the Theory and/or on the Applications of Classical Nonparametric Methods Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametric statistics. The fifth edition carries on this tradition while thoroughly revising at least 50 percent of the material. New to the Fifth Edition Updated and revised contents based on recent journal articles in the literature A new section in the chapter on goodness-of-fit tests A new chapter that offers practical guidance on how to choose among the various nonparametric procedures covered Additional problems and examples Improved computer figures This classic, best-selling statistics book continues to cover the most commonly used nonparametric procedures. The authors carefully state the assumptions, develop the theory behind the procedures, and illustrate the techniques using realistic research examples from the social, behavioral, and life sciences. For most procedures, they present the tests of hypotheses, confidence interval estimation, sample size determination, power, and comparisons of other relevant procedures. The text also gives examples of computer applications based on Minitab, SAS, and StatXact and compares these examples with corresponding hand calculations. The appendix includes a collection of tables required for solving the data-oriented problems. Nonparametric Statistical Inference, Fifth Edition provides in-depth yet accessible coverage of the theory and methods of nonparametric statistical inference procedures. It takes a practical approach that draws on scores of examples and problems and minimizes the theorem-proof format. Jean Dickinson Gibbons was recently interviewed regarding her generous pledge to Virginia Tech.
Written by a well-known lecturer and consultant to thepharmaceutical industry, this book focuses on the pharmaceuticalnon-statistician working within a very strict regulatoryenvironment. Statistical Thinking for Clinical Trials inDrug Regulation presents the concepts and statisticalthinking behind medical studies with a direct connection to theregulatory environment so that readers can be clear where thestatistical methodology fits in with industry requirements.Pharmaceutical-related examples are used throughout to set theinformation in context. As a result, this book provides adetailed overview of the statistical aspects of the design,conduct, analysis and presentation of data from clinical trialswithin drug regulation. Statistical Thinking for Clinical Trials in DrugRegulation: Assists pharmaceutical personnel in communicating effectivelywith statisticians using statistical language Improves the ability to read and understand statisticalmethodology in papers and reports and to critically appraisethat methodology Helps to understand the statistical aspects of the regulatoryframework better quoting extensively from regulatory guidelinesissued by the EMEA (European Medicines Evaluation Agency), ICH(International Committee on Harmonization and the FDA (Food andDrug Administration)
Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation. Drawing on data sets from the author’s many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author’s website.
Vital Statistics: an introduction to health science statistics e-book is a new Australian publication. This textbook draws on real world, health-related and local examples, with a broad appeal to the Health Sciences student. It demonstrates how an understanding of statistics is useful in the real world, as well as in statistics exams. Vital Statistics: an introduction to health science statistics e-book is a relatively easy-to-read book that will painlessly introduce or re-introduce you to the statistical basics before guiding you through more demanding statistical challenges. Written in recognition of Health Sciences courses which require knowledge of statistical literacy, this book guides the reader to an understanding of why, as well as how and when to use statistics. It explores: How data relates to information, and how information relates to knowledge How to use statistics to distinguish information from disinformation The importance of probability, in statistics and in life That inferential statistics allow us to infer from samples to populations, and how useful such inferences can be How to appropriately apply and interpret statistical measures of difference and association How qualitative and quantitative methods differ, and when it’s appropriate to use each The special statistical needs of the health sciences, and some especially health science relevant statistics The vital importance of computers in the statistical analysis of data, and gives an overview of the most commonly used analyses Real-life local examples of health statistics are presented, e.g. A study conducted at the Department of Obstetrics and Gynecology, University of Utah School of Medicine, explored whether there might be a systematic bias affecting the results of genetic specimen tests, which could affect their generalizability. Reader-friendly writing style t-tests/ ANOVA family of inferential statistics all use variants of the same basic formula Learning Objectives at the start of each chapter and Quick Reference Summaries at the end of each chapter provide the reader with a scope of the content within each chapter.
Genetic Counseling Research: A Practical Guide is the first text devoted to research methodology in genetic counseling. This text offers step-by-step guidance for conducting research, from the development of a question to the publication of findings. Genetic counseling examples, user-friendly worksheets, and practical tips guide readers through the research and publication processes. With a highly accessible, pedagogical approach, this book will help promote quality research by genetic counselors and research supervisors--and in turn, increase the knowledge base for genetic counseling practice, other aspects of genetic counseling service delivery, and professional education. It will be an invaluable resource to the next generation of genetic counseling and its surrounding disciplines.
This work guides the reader through the process of data analysis and features hints and warnings. It should be of interest to those studying quantitative methods in all disciplines, in particular marketing, management, economics and psychology.
Biostatistics for Oral Healthcare offers students, practitioners and instructors alike a comprehensive guide to mastering biostatistics and their application to oral healthcare. Drawing on situations and methods from dentistry and oral healthcare, this book provides a thorough treatment of statistical concepts in order to promote in-depth and correct comprehension, supported throughout by technical discussion and a multitude of practical examples.
Statistical inference is the foundation on which much of statistical practice is built. This book covers the topic at a level suitable for students and professionals who need to understand these foundations.