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Statistical science’s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. Analysis of Ordinal Categorical Data, Second Edition provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developments and recent methods. Special emphasis is placed on interpretation and application of methods including an integrated comparison of the available strategies for analyzing ordinal data. Practitioners of statistics in government, industry (particularly pharmaceutical), and academia will want this new edition.
A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
TItis volume is the first effort to compile representative work in the emerging research area on the relationship of disability and physical environment since Barrier-Free Environments, edited by Michael Bednar, was published in 1977. Since that time, disability rights legislation like the Americans, with Disabilities Act in the United States, the worldwide growth of the independent-living move ment, rapid deinstitutionalization, and the maturation of functional assessment methodology have all had their impact on this research area. The impact has been most noticeable in two ways-fostering the integration of environmental vari ables in rehabilitation research and practice, and changing paradigms for environ mental interventions. As the contributions in this volume demonstrate, the relationship of disabil ity and physical environment is no longer of interest primarily to designers and other professionals concerned with managing the resources of the built environ ment. The physical environment has always been recognized as an important variable affecting rehabilitation outcome. Until recently, however, concepts and tools were not available to measure its impact in clinical practic~ and outcomes research. In particular, lack of a theoretical foundation that integrated environ ment with the disablement process hampered development of both research and clinical methodology. Thus, the physical environment received little attention from the mainstream rehabilitation research community. However, this situation is changing rapidly.
Rise to today’s challenges with these innovative and helpful value-based solutions!Containing important, research-based insights into social work practice in these fields, Social Work Health and Mental Health Practice, Research and Programs provides unique perspectives on shared practice problems from around the world, offering new solutions to the dilemmas practitioners face every day, such as reduced reliance in inpatient/residential service provision, increased reliance on economics in the era of managed care, the move toward multidisciplinary service provision, the growing awareness of diversity of needs, and the cultural requirements of providing effective services.Social Work Health and Mental Health Practice, Research and Programs provides unique international perspectives on real-world social work practice issues, including: ways to use your social work skills to solicit organ/tissue donation for transplants how a social work directed community organization affected change in health behaviors in East Harlem, New York a look at how to promote psychosocial well-being following a diagnosis of cancer a survey of what mental health services Hong Kong elderly feel they need and what they now receive an examination of the role of demographics and social support in clinician- and patient-related compliance among HIV/AIDS patients a discussion of the appropriateness of hospice services for non-English speaking patients and much more!
"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
This Ph.D. thesis concerns the problem of assessing distributional properties in multivariate regression models with possibly related marginal models. Such models are of great importance in most branches of statistics. The four papers in this thesis deal with different distributional aspects of relevance for all models. Paper 1 concerns the problem of assessing normality in the multivariate regression model. Paper 2 concerns the problem of assessing normality in situations when the data is heteroscedastic or autocorrelated. Paper 3 concerns testing for heteroscedasticity in linear regression models. Paper 4 presents four different types of tests for autocorrelation to be used in SUR models or multivariate regressions.