Many scientists and technologists would like to carry out their own statistical analyses without reference to a professional statistician. Often, however, they have no knowledge of statistics or otherwise do not know how to apply it to research and development problems. The first edition of Statistics in Research and Development was written for the
Many scientists and technologists would like to carry out their own statistical analyses without reference to a professional statistician. Often, however, they have no knowledge of statistics or otherwise do not know how to apply it to research and development problems. The first edition of Statistics in Research and Development was written for these people. The second edition brings the book up-to-date. The text is divided into two parts; the first introduces basic but very important statistical techniques whilst the second part presents the modern powerful methods of data analysis that are particularly useful in modern research and development. Problems are provided at the end of each chapter with worked solutions provided at the end of the book. A problem-centered approach is used throughout and care has been taken to choose problems with which the scientist or technologist can identify. The results of the statistical analyses are reinterpreted into the language of the scientist. Mathematics is kept to a minimum and the assumptions underlying each technique are clearly explained. All the techniques introduced are powerful and proven, and commercial computer programs are available for many of them.
In Research as Development, Salla Sariola and Bob Simpson show how international collaboration operates in a setting that is typically portrayed as "resource-poor" and "scientifically lagging." Based on their long-term fieldwork in Sri Lanka, Sariola and Simpson bring into clear ethnographic focus the ways international scientific collaborations feature prominently in the pursuit of global health in which research operates "as" development and not merely "for" it. The authors follow the design, inception, and practice of two clinical trials: one a global health charity funded trial and the other a pharmaceutical industry-sponsored trial. Research as Development situates these two trials within their historical, political and cultural contexts and thus counters the idea that local actors are merely passive recipients of new technical and scientific rationalities. While social studies of clinical trials are beginning to be an established niche in academic writing, Research as Development helps fill important gaps in the literature through its examination of clinical research situated in cultures in low-income settings. Research as Development is noteworthy for the way it highlights the critical and creative role that local researchers play in establishing international collaborations and making them work into locally viable forms. The volume shows how these clinical and research interactions bring about changes in culture, technologies and expertise in Sri Lanka, contexts that have not previously been written about in any detail.
Statistical Analysis of Human Growth and Development is an accessible and practical guide to a wide range of basic and advanced statistical methods that are useful for studying human growth and development. Designed for nonstatisticians and statisticians new to the analysis of growth and development data, the book collects methods scattered throughout the literature and explains how to use them to solve common research problems. It also discusses how well a method addresses a specific scientific question and how to interpret and present the analytic results. Stata is used to implement the analyses, with Stata codes and macros for generating example data sets, a detrended Q-Q plot, and weighted maximum likelihood estimation of binary items available on the book’s CRC Press web page. After reviewing research designs and basic statistical tools, the author discusses the use of existing tools to transform raw data into analyzable variables and back-transform them to raw data. He covers regression analysis of quantitative, binary, and censored data as well as the analysis of repeated measurements and clustered data. He also describes the development of new growth references and developmental indices, the generation of key variables based on longitudinal data, and the processes to verify the validity and reliability of measurement tools. Looking at the larger picture of research practice, the book concludes with coverage of missing values, multiplicity problems, and multivariable regression. Along with two simulated data sets, numerous examples from real experimental and observational studies illustrate the concepts and methods. Although the book focuses on examples of anthropometric measurements and changes in cognitive, social-emotional, locomotor, and other abilities, the ideas are applicable to many other physical and psychosocial phenomena, such as lung function and depressive symptoms.
`[Research for Development] is well-written and, at every stage, is well-documented with practical examples. The simplicity with which it is written adds to its value in that non-professional persons get well-aquainted with the research process. Every chapter in the book ends with highlighting of the main points made in that chapter... A further strength of the book is the inclusion of an appendix with a list of websites that deal with issues in the area of development research... the simplicity of its organization and message should appeal to people/researchers across disciplines' - Pakistan Development Review `Research for Development achieves the near impossible: it provides vast quantities of useful guidance for almost anyone involved in research for development regardless of the size of your research project or your role within that project' - Arvac Bulletin `Written by professional researchers, this immensely practical book provides development workers with a more research-oriented point of view, so that they can avoid mistakes in the design of programmes. It will also help them to understand people's needs and respond accordingly' - The Asian Age `It is a beautiful and comprehensive compilation giving scores of instances that prove the essentiality if carrying out a survey of a particular locality for bringing about a change there' - Rafique Jalal, DAWN This book provides a comprehensive introduction and handbook for undertaking and managing research in development. It is designed to provide both a quick reference manual and an indispensable learning tool for all students, researchers and practitioners engaged in development work. The text is divided into two parts: Managing research for development, and Doing research for development. Together the two parts review the complete research process from outlining the essential role and purpose of research, highlighting specific issues to development research, to demonstrating how to evaluate and secure the best results from subsequent research projects. The book includes: an overview of different types of research in development work; practical steps to writing a brief and managing research; practical steps to evaluating and promoting research findings; step by step guides to getting started and choosing a research method; detailed guidelines to seven key research techniques; examples, exercises, summaries and checklists; and glossary and guides to additional resources and packages Drawing on considerable hands-on experience, Research for Development will be an essential companion and invaluable tool for anyone engaged in contemporary development research, development work and development studies.
Drug development is the process of finding and producingtherapeutically useful pharmaceuticals, turning them into safe andeffective medicine, and producing reliable information regardingthe appropriate dosage and dosing intervals. With regulatoryauthorities demanding increasingly higher standards in suchdevelopments, statistics has become an intrinsic and criticalelement in the design and conduct of drug development programmes. Statistical Issues in Drug Development presents anessential and thought provoking guide to the statistical issues andcontroversies involved in drug development. This highly readable second edition has been updated toinclude: Comprehensive coverage of the design and interpretation ofclinical trials. Expanded sections on missing data, equivalence, meta-analysisand dose finding. An examination of both Bayesian and frequentist methods. A new chapter on pharmacogenomics and expanded coverage ofpharmaco-epidemiology and pharmaco-economics. Coverage of the ICH guidelines, in particular ICH E9,Statistical Principles for Clinical Trials. It is hoped that the book will stimulate dialogue betweenstatisticians and life scientists working within the pharmaceuticalindustry. The accessible and wide-ranging coverage make itessential reading for both statisticians and non-statisticiansworking in the pharmaceutical industry, regulatory bodies andmedical research institutes. There is also much to benefitundergraduate and postgraduate students whose courses include amedical statistics component.
Designed to provide a comprehensive, step-by-step approach to organic process research and development in the pharmaceutical, fine chemical, and agricultural chemical industries, this book describes the steps taken, following synthesis and evaluation, to bring key compounds to market in a cost-effective manner. It describes hands-on, step-by-step, approaches to solving process development problems, including route, reagent, and solvent selection; optimising catalytic reactions; chiral syntheses; and "green chemistry." Second Edition highlights:• Reflects the current thinking in chemical process R&D for small molecules• Retains similar structure and orientation to the first edition. • Contains approx. 85% new material• Primarily new examples (work-up and prospective considerations for pilot plant and manufacturing scale-up)• Some new/expanded topics (e.g. green chemistry, genotoxins, enzymatic processes)• Replaces the first edition, although the first edition contains useful older examples that readers may refer to - Provides insights into generating rugged, practical, cost-effective processes for the chemical preparation of "small molecules" - Breaks down process optimization into route, reagent and solvent selection, development of reaction conditions, workup, crystallizations and more - Presents guidelines for implementing and troubleshooting processes
This innovative text offers a completely integrated approach to teaching research methods and statistics by presenting a research question accompanied by the appropriate methods and statistical procedures needed to address it. Research questions and designs become more complex as chapters progress, building on simpler questions to reinforce student learning. Using a conversational style and research examples from published works, this comprehensive book walks readers through the entire research process and includes ample pedagogical support for SPSS, Excel, and APA style.
How statistical thinking and methodology can help you make crucial business decisions Straightforward and insightful, Statistical Thinking: Improving Business Performance, Second Edition, prepares you for business leadership by developing your capacity to apply statistical thinking to improve business processes. Unique and compelling, this book shows you how to derive actionable conclusions from data analysis, solve real problems, and improve real processes. Here, you'll discover how to implement statistical thinking and methodology in your work to improve business performance. Explores why statistical thinking is necessary and helpful Provides case studies that illustrate how to integrate several statistical tools into the decision-making process Facilitates and encourages an experiential learning environment to enable you to apply material to actual problems With an in-depth discussion of JMP® software, the new edition of this important book focuses on skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, and understanding the limitations of statistical analyses.
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
