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Bayesian inference : with ecological applications

Link, W. A., & Barker, R. J. (2010). Bayesian inference : with ecological applications. Amsterdam ; Boston ; London: Elsevier/Academic. Ebook.

 (Publisher note) This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists.

From my perspective (AH), for the behavioral health researcher, the use of Bayesian methods to combine inferences from quantitative and non-quantitative data include the possibility of generating inferences when little or no quantitative data are available, its applicability to evaluation at all stages of program development, and its requirement that evaluators be explicit regarding the influence of qualitative information and alternative points of view in the evaluation of social programs.

Data analysis using SAS Enterprise guide

Meyers, L. S., Gamst, G., & Guarino, A. J. (2009). Data analysis using SAS Enterprise guide. Cambridge ; New York, NY: Cambridge University Press. E-book.
(Publisher note) This book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.

Linear model methodology.

Khuri, A. I. (2010). Linear model methodology. Boca Raton, FL: CRC Press. E-book.
(Publisher note)  Given the importance of linear models in statistical theory and experimental research, a good understanding of their fundamental principles and theory is essential. Supported by a large number of examples, Linear Model Methodology provides a strong foundation in the theory of linear models and explores the latest developments in data analysis. After presenting the historical evolution of certain methods and techniques used in linear models, the book reviews vector spaces and linear transformations and discusses the basic concepts and results of matrix algebra that are relevant to the study of linear models. Although mainly focused on classical linear models, the next several chapters also explore recent techniques for solving well-known problems that pertain to the distribution and independence of quadratic forms, the analysis of estimable linear functions and contrasts, and the general treatment of balanced random and mixed-effects models. The author then covers more contemporary topics in linear models, including the adequacy of Satterthwaite’s approximation, unbalanced fixed- and mixed-effects models, heteroscedastic linear models, response surface models with random effects, and linear multiresponse models. The final chapter introduces generalized linear models, which represent an extension of classical linear models. Linear models provide the groundwork for analysis of variance, regression analysis, response surface methodology, variance components analysis, and more, making it necessary to understand the theory behind linear modeling. Reflecting advances made in the last thirty years, this book offers a rigorous development of the theory underlying linear models.

Linear statistical models

Stapleton, J. H. (2009). Linear statistical models. New York, NY: Wiley. Call Number: QA279 .S695 2009
(Publisher note) Developed and refined over a period of twenty years, the material in this book offers an especially lucid presentation of linear statistical models. These models lead to what is usually called "multiple regression" or "analysis of variance" methodology, which, in turn, opens up a wide range of applications to the physical, biological, and social sciences, as well as to business, agriculture, and engineering. Unlike similar books on this topic, Linear Statistical Models emphasizes the geometry of vector spaces because of the intuitive insights this approach brings to an understanding of the theory. While the focus is on theory, examples of applications, using the SAS and S-Plus packages, are included. Prerequisites include some familiarity with linear algebra, and probability and statistics at the postcalculus level. Major topics covered include: methods of study of random vectors, including the multivariate normal, chi-square, t and F distributions, central and noncentral; the linear model and the basic theory of regression analysis and the analysis of variance; multiple regression methods, including transformations, analysis of residuals, and asymptotic theory for regression analysis. Separate sections are devoted to robust methods and to the bootstrap. Also discusseda re simultaneous confidence intervals: Bonferroni, Scheffé, Tukey, and Bechhofer; analysis of variance, with two- and three-way analysis of variance, random component models, nested designs, and balanced incomplete block designs, and analysis of frequency data through log-linear models, with emphasis on vector space viewpoint. This chapter alone is sufficient for a course on the analysis of frequency data.

Statistics for health care professionals working with Excel, 2nd ed

Veney, J. E., Kros, J. F., Rosenthal, D. A., & Veney, J. E. (2009). Statistics for health care professionals working with Excel, 2nd ed. San Francisco, CA: Jossey-Bass. Call Number: RA409.5 .V466 2009
Publisher note) Statistics for Health Care Professionals: Working with Excel (second edition) is written in a clear, easily followed style keyed to the powerful statistical tool, Microsoft Excel 2007. It introduces the use of statistics applicable to health administration, health policy, public health, health information management, and other professions, emphasizing the logic of probability and statistical analysis in all areas. Coverage includes data acquisition, data display, basics of probability, data distributions, confidence limits and hypothesis testing, statistical tests for categorical data, tests for related and unrelated data, analysis of variance, simple linear regression, multiple regression, and analysis with a dichotomous categorical dependent variable. A glossary and section-by-section review questions round out this uniquely comprehensive and accessible text.

Statistics at square one, 11th ed

Swinscow, T. D. V., & Campbell, M. J. (2002). Statistics at square one, 11th ed. London: BMJ Books. E-book.
(Publisher note) The new edition of this international bestseller continues to throw light on the world of statistics for health care professionals and medical students. Revised throughout, the 11th edition features new material in the areas of relative risk, absolute risk and numbers needed to treat, diagnostic tests, sensitivity, specificity, ROC curves, and free statistical software. There are new sections on reading and reporting statistics and formula appreciation.

Remote sensing and geographical information systems in epidemiology

Hay, S. I., Randolph, S. E., & Rogers, D. J. (2000). Remote sensing and geographical information systems in epidemiology. San Diego, CA; London: Academic. Call Number: QH547 .A3 v.47
Abstract: (Publisher note) This book presents a comprehensive guide to using the very latest methods of surveillance from satellites, including analysing spatial data within geographical information systems, interpreting complex biological patterns, and predicting risk both today and as it may change in the future. Although the book uses malaria and African trypanosomiasis (sleeping sickness) as examples, the techniques provided offer new ways of looking at disease surveillance.

Spatial epidemiology: methods and applications.

Elliott, P., Wakefield, J., Best, N., & Briggs, D. (2000). Spatial epidemiology: methods and applications. Oxford ; New York, NY: Oxford University Press. Call Number: RA792 .S685 2000
(Publisher note) This book describes, quantifies, and explains geographical variations in disease, especially with respect to environmental exposures at the small area scale. It is useful for researchers at any level of experience with spatial analysis and serves well as both a teaching text and reference book.

Intelligent research design: a guide for beginning researchers in the social sciences

Hancké, B. (2009). Intelligent research design: a guide for beginning researchers in the social sciences. Oxford ; New York, NY: Oxford University Press.  Call Number: H62 .H235 2009
(Publisher note) This book offers advice to doctoral researchers and graduate and advanced undergraduate students on how to embark on their research. Based on a decade of teaching early-stage researchers in the social sciences at the LSE and other universities, and written with the central problems of beginning researchers in mind, Bob Hancke guides them through the process of thinking about the links between theory, cases and data, and to do so in a way that helps to turn their initial plausible ideas into convincing arguments. This lively book, deliberately jargon-free and with a hands-on, pragmatic approach to research design, addresses the problems that research students face--or ignore, often at their peril--in the course of their first few years. Its central message is that research is a complex and iterative process in which researchers construct every relevant part of their project with one goal in mind: make a persuasive point. They define the question they ask and the debate they engage, construct their cases and data to answer that question, and write it up as an argument that brings out the strengths of their research design. It addresses such key issues as statistical versus configurational approaches, time in social science research, different types of case studies and comparative research, and a critical approach to data. The Appendix gives tips on presenting and discussing papers, and on crafting research proposals.

When research goes off the rails: why it happens and what you can do about it

Streiner, D. L., & Sidani, S. (2010). When research goes off the rails: why it happens and what you can do about it. New York, NY: Guilford Press.  E-book.
(Publisher note) Few behavioral or health science studies proceed seamlessly. This refreshingly candid guide presents firsthand vignettes of obstacles on the bumpy road of research and offers feasible, easy-to-implement solutions. Contributors from a range of disciplines describe real-world problems at each stage of a quantitative or qualitative research project—from gaining review board approval to collecting and analyzing data—and discuss how these problems were resolved. A detailed summary chart helps readers quickly find material on specific issues, methods, and settings. Written with clarity and wit, the vignettes provide exemplars of critical thinking that researchers can apply when developing the operational plan of a study or when facing practical difficulties in a particular research phase.