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.
This latest edition has been fully updated to accommodate the needs of users of SPSS Releases 17, 18 and 19 while still being applicable to users of SPSS Releases 15 and 16. As with previous editions, Alan Bryman and Duncan Cramer continue to offer a comprehensive and user-friendly introduction to the widely used IBM SPSS Statistics. The simple, non-technical approach to quantitative data analysis enables the reader to quickly become familiar with SPSS and with the tests available to them. No previous experience of statistics or computing is required as this book provides a step-by-step guide to statistical techniques, including: Non-parametric tests Correlation Simple and multiple regression Analysis of variance and covariance Factor analysis. This book comes equipped with a comprehensive range of exercises for further practice, and it covers key issues such as sampling, statistical inference, conceptualization and measurement and selection of appropriate tests. The authors have also included a helpful glossary of key terms. The data sets used in Quantitative Data Analysis with IBM SPSS 17, 18 and 19 are available online at http://www.psypress.com/brymancramer; in addition, a set of multiple-choice questions and a chapter-by-chapter PowerPoint lecture course are available free of charge to lecturers who adopt the book.
Author: Hazel R. Scott,Kevin G. Blyth,Jeremy B. Jones
Publisher: Elsevier Health Sciences
This book is designed as a companion to the initial years of hospital training for junior doctors in training, including, but not limited to, the core elements of the curriculum for Foundation Training in the UK. Patients have co-morbidity and mixed patterns of clinical presentation and thus the book brings together the key guidance on the presentation and care of all those who attend within a wide range of disciplines. These appear in the book as they present in real life, according to symptoms. Given the balance of the type of work done by most trainee hospital doctors, the emphasis of the book is on acute, as compared with chronic, symptom presentation and effective management. • Provides a concise and high quality account of the relevant information for those working in Foundation training • Includes practical step-by-step guidance on a range of core clinical procedures • Provides valuable information on the non-clinical aspects of a clinical career • Written by an author team with extensive practical experience of teaching trainee hospital doctors.
This practical introduction helps readers apply multilevel techniques to their research. Noted as an accessible introduction, the book also includes advanced extensions, making it useful as both an introduction and as a reference to students, researchers, and methodologists. Basic models and examples are discussed in non-technical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines. For example, readers will find data sets on stress in hospitals, GPA scores, survey responses, street safety, epilepsy, divorce, and sociometric scores, to name a few. The data sets are available on the website in SPSS, HLM, MLwiN, LISREL and/or Mplus files. Readers are introduced to both the multilevel regression model and multilevel structural models. Highlights of the second edition include: Two new chapters—one on multilevel models for ordinal and count data (Ch. 7) and another on multilevel survival analysis (Ch. 8). Thoroughly updated chapters on multilevel structural equation modeling that reflect the enormous technical progress of the last few years. The addition of some simpler examples to help the novice, whilst the more complex examples that combine more than one problem have been retained. A new section on multivariate meta-analysis (Ch. 11). Expanded discussions of covariance structures across time and analyzing longitudinal data where no trend is expected. Expanded chapter on the logistic model for dichotomous data and proportions with new estimation methods. An updated website at http://www.joophox.net/ with data sets for all the text examples and up-to-date screen shots and PowerPoint slides for instructors. Ideal for introductory courses on multilevel modeling and/or ones that introduce this topic in some detail taught in a variety of disciplines including: psychology, education, sociology, the health sciences, and business. The advanced extensions also make this a favorite resource for researchers and methodologists in these disciplines. A basic understanding of ANOVA and multiple regression is assumed. The section on multilevel structural equation models assumes a basic understanding of SEM.
Statistical Analysis Of Nonnormal Data Has Successfully Made Available In One Place Nonparametric Methods And Methods Of Discrete Data-Analysis. It Has Attempted To Introduce The Reader To Methods Appropriate For Simple, Continuous, Nonnormal Distribution Of Interest In The Newly Emerging Area Of Survival Analysis And Reliability. The Book Also Provides Computer Programmes For Ready Use.It Can Be Used By Anyone Familiar With Standard Statistical Principles And The Tools In The Framework Of Normal Distribution. Computer Programmes Are In Theready To Use Format. Therefore, Familiarity With Operations Of A Personal Computer And A Dos Environment Is The Only Prerequisite.The Book Would Make An Excellent Text For A Second Course In Statistical Methods For Biologists, Social Scientists, Engineers, Etc. Researchers In Various Disciplines Should Be Able To Use The Methods Described In The Book Without The Benefit Of A Formal Course.
Unlocking Business Opportunities in Gulf Co-Operation Council (GCC) Markets
Author: Doren Chadee,Banjo Roxas,Tim Rogmans
Category: Business & Economics
This book assesses the effectiveness of free trade agreements (FTAs) in unlocking international business opportunities in member states of the Gulf Cooperation Council (GCC). It takes an institutional perspective in explaining the existence and effects of non-tariff barriers and how FTAs can address these barriers to attract foreign investors.
Ton J. Cleophas,A.H. Zwinderman,Toine F. Cleophas,Eugene P. Cleophas
Author: Ton J. Cleophas,A.H. Zwinderman,Toine F. Cleophas,Eugene P. Cleophas
Publisher: Springer Science & Business Media
In clinical medicine appropriate statistics has become indispensable to evaluate treatment effects. Randomized controlled trials are currently the only trials that truly provide evidence-based medicine. Evidence based medicine has become crucial to optimal treatment of patients. We can define randomized controlled trials by using Christopher J. Bulpitt’s definition “a carefully and ethically designed experiment which includes the provision of adequate and appropriate controls by a process of randomization, so that precisely framed questions can be answered”. The answers given by randomized controlled trials constitute at present the way how patients should be clinically managed. In the setup of such randomized trial one of the most important issues is the statistical basis. The randomized trial will never work when the statistical grounds and analyses have not been clearly defined beforehand. All endpoints should be clearly defined in order to perform appropriate power calculations. Based on these power calculations the exact number of available patients can be calculated in order to have a sufficient quantity of individuals to have the predefined questions answered. Therefore, every clinical physician should be capable to understand the statistical basis of well performed clinical trials. It is therefore a great pleasure that Drs. T. J. Cleophas, A. H. Zwinderman, and T. F. Cleophas have published a book on statistical analysis of clinical trials. The book entitled “Statistics Applied to Clinical Trials” is clearly written and makes complex issues in statistical analysis transparant.
Whilst the ‘health sciences’ are a broad and diverse area, and includes public health, primary care, health psychology, psychiatry and epidemiology, the research methods and data analysis skills required to analyse them are very similar. Moreover, the ability to appraise and conduct research is emphasised within the health sciences – and students are expected increasingly to do both. Introduction to Research Methods and Data Analysis in the Health Sciences presents a balanced blend of quantitative research methods, and the most widely used techniques for collecting and analysing data in the health sciences. Highly practical in nature, the book guides you, step-by-step, through the research process, and covers both the consumption and the production of research and data analysis. Divided into the three strands that run throughout quantitative health science research – critical numbers, critical appraisal of existing research, and conducting new research – this accessible textbook introduces: Descriptive statistics Measures of association for categorical and continuous outcomes Confounding, effect modification, mediation and causal inference Critical appraisal Searching the literature Randomised controlled trials Cohort studies Case-control studies Research ethics and data management Dissemination and publication Linear regression for continuous outcomes Logistic regression for categorical outcomes. A dedicated companion website offers additional teaching and learning resources for students and lecturers, including screenshots, R programming code, and extensive self-assessment material linked to the book’s exercises and activities. Clear and accessible with a comprehensive coverage to equip the reader with an understanding of the research process and the practical skills they need to collect and analyse data, it is essential reading for all undergraduate and postgraduate students in the health and medical sciences.
Author: Stanton A. Glantz,Bryan K. Slinker,Torsten B. Neilands
Publisher: McGraw Hill Professional
A textbook on the use of advanced statistical methods in healthcare sciences Primer of Applied Regression & Analysis of Variance is a textbook especially created for medical, public health, and social and environmental science students who need applied (not theoretical) training in the use of statistical methods. The book has been acclaimed for its user-friendly style that makes complicated material understandable to readers who do not have an extensive math background. The text is packed with learning aids that include chapter-ending summaries and end-of-chapter problems that quickly assess mastery of the material. Examples from biological and health sciences are included to clarify and illustrate key points. The techniques discussed apply to a wide range of disciplines, including social and behavioral science as well as health and life sciences. Typical courses that would use this text include those that cover multiple linear regression and ANOVA. Four completely new chapters Completely updated software information and examples
A Physical-Technical Introduction for Physicians and Biologists
Author: Robert S. Reneman,J. Strackee
Publisher: Springer Science & Business Media
Category: Technology & Engineering
Nowadays clinical medicine is to a great extent dependent on techniques and instrumentation. Not infrequently, instrumentation is so complicated that technical specialists are required to perform the measurements and to process the data. Interpretation of the results, however, generally has to be done by physicians. For proper interpretation of data and good com munication with technical specialists, knowledge of, among other things, principle, advantages, limitations and applicability of the used techniques is necessary. Besides, this knowledge is required for critical comparison of systems to measure a certain variable. Critical evaluation as well as com parison of techniques and instruments ought to be an essential component of medical practice. In general, basic techniques and instrumentation are not taught in medi cal schools nor during residencies. Therefore, physicians themselves have to collect practical information about principle, advantages and limitations of techniques and instruments when using them in clinical medicine. This practical information, focussed on the specific techniques used in the various disciplines, is usually difficult to obtain from handbooks and manufacturers' manuals. Hence a new series of books is started on instru mentation and techniques in clinical medicine.
Rebecca M. Warner's Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.
In a recent paper, Bai and Perron (2006) demonstrate that their approach for testing for multiple structural breaks in time series works well in large samples, but they found substantial deviations in both the size and power of their tests in smaller samples. We propose modifying their methodology to deal with small samples by using Monte Carlo simulations to determine sample-specific critical values under the each time the test is run. We draw on the results of our simulations to offer practical suggestions on handling serial correlation, model misspecification, and the use of alternative test statistics for sequential testing. We show that, for most types of data generating processes in samples with as low as 50 observations, our proposed modifications perform substantially better.
The goal of Norman H. Anderson's new book is to help students develop skills of scientific inference. To accomplish this he organized the book around the "Experimental Pyramid"--six levels that represent a hierarchy of considerations in empirical investigation--conceptual framework, phenomena, behavior, measurement, design, and statistical inference. To facilitate conceptual and empirical understanding, Anderson de-emphasizes computational formulas and null hypothesis testing. Other features include: *emphasis on visual inspection as a basic skill in experimental analysis to help students develop an intuitive appreciation of data patterns; *exercises that emphasize development of conceptual and empirical application of methods of design and analysis and de-emphasize formulas and calculations; and *heavier emphasis on confidence intervals than significance tests. The book is intended for use in graduate-level experimental design/research methods or statistics courses in psychology, education, and other applied social sciences, as well as a professional resource for active researchers. The first 12 chapters present the core concepts graduate students must understand. The next nine chapters serve as a reference handbook by focusing on specialized topics with a minimum of technicalities.
Professionals in environmental health and safety (EHS) management use statistics every day in making decisions. This book was created to provide the quantitative tools and techniques necessary to make important EHS assessments. Readers need not be statistically or mathematically inclined to make the most of this book-mathematical derivations are kept to a minimum and subjects are approached in a simple and factual manner, complemented with plenty of real-world examples. Chapters 1-3 cover knowledge of basic statistical concepts such as presentation of data, measurements of location and dispersion, and elementary probability and distributions. Data gathering and analysis topics including sampling methods, sampling theory, testing, and interference as well as skills for critically evaluating published numerical material is presented in Chapters 4-6. Chapters 7-11 discuss information generation topics-regression and correlation analysis, time series, linear programming, network and Gnatt charting, and decision analysis-tools that can be used to convert data into meaningful information. Chapter 12 features six examples of projects made successful through statistical approaches being applied. Readers can use these approaches to solve their own unique problems. Whether you are a EHS professional, manager, or student, Health, Safety, and Environmental Data Analysis: A Business Approach will help you communicate statistical data effectively.
Gordon A. Fox,Simoneta Negrete-Yankelevich,Vinicio J. Sosa
Author: Gordon A. Fox,Simoneta Negrete-Yankelevich,Vinicio J. Sosa
Publisher: OUP Oxford
The application and interpretation of statistics are central to ecological study and practice. Ecologists are now asking more sophisticated questions than in the past. These new questions, together with the continued growth of computing power and the availability of new software, have created a new generation of statistical techniques. These have resulted in major recent developments in both our understanding and practice of ecological statistics. This novel book synthesizes a number of these changes, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language.
A fully updated edition of this key text on mixed models,focusing on applications in medical research The application of mixed models is an increasingly popular wayof analysing medical data, particularly in the pharmaceuticalindustry. A mixed model allows the incorporation of both fixed andrandom variables within a statistical analysis, enabling efficientinferences and more information to be gained from the data. Therehave been many recent advances in mixed modelling, particularlyregarding the software and applications. This third edition ofBrown and Prescott’s groundbreaking text provides an updateon the latest developments, and includes guidance on the use ofcurrent SAS techniques across a wide range of applications. Presents an overview of the theory and applications of mixedmodels in medical research, including the latest developments andnew sections on incomplete block designs and the analysis ofbilateral data. Easily accessible to practitioners in any area where mixedmodels are used, including medical statisticians andeconomists. Includes numerous examples using real data from medical andhealth research, and epidemiology, illustrated with SAS code andoutput. Features the new version of SAS, including new graphics formodel diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, andfurther material. This third edition will appeal to applied statisticians workingin medical research and the pharmaceutical industry, as well asteachers and students of statistics courses in mixed models. Thebook will also be of great value to a broad range of scientists,particularly those working in the medical and pharmaceuticalareas.