My Books
Here are books and links to the books I have written and published through traditional publishers.
This is a second edition to the original published by Springer in 2006. The comprehensive volume takes a textbook approach systematically developing the field by starting from linear models and then moving up to generalized linear and non-linear mixed effects models. Since the first edition was published the field has grown considerably in terms of maturity and technicality. The second edition of the book therefore considerably expands with the addition of three new chapters relating to Bayesian models, Generalized linear and nonlinear mixed effects models, and Principles of simulation. In addition, many of the other chapters have been expanded and updated.
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Modeling and simulation are increasingly being used in science, business, and engineering to understand and explain complex systems, make predictions, and improve decision-making. Modeling today is not just about developing models. Modelers must be able to efficiently and effectively communicate their results to both laypeople and other scientists for their results to be useful and have impact. Unfortunately, scientists are taught to be boring and academic, all in the name of scientific rigor and professionalism. But it doesn’t have to be that way. In this engaging and dynamic book, Dr. Bonate shows step-by-step from planning to delivery how to improve your oral presentations and become a better model communicator. Numerous examples and humorous anecdotes are used to explain concepts and material. Anyone who engages in modeling and simulation will benefit from reading this book.
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In medical, psychological, sociological, and educational studies, researchers often design experiments in which they collect baseline (pretest) measurements prior to randomization. After randomization, a treatment is applied, and then post-treatment measurements using the same instrument are collected to assess the effect of the treatment (post-test scores). However, they often find themselves at a loss to know which method of statistical analysis is most appropriate for their data. How should you analyze pretest-posttest data? Difference scores? Percent change scores? ANOVA? Until now, consulting the available literature would lead to conflicting results, with available journals and textbook references advocating different methodologies.
Analysis of Pretest-Posttest Designs brings welcome relief from this conundrum. This one-stop reference - written specifically for researchers - answers the questions about and helps clear confusion about analyzing pretest-posttest data. Keeping derivations to a minimum and offering real life examples from a range of disciplines, the author gathers and elucidates the concepts and techniques most useful for studies incorporating baseline data.
Understand the pros and cons of different methods - ANOVA, ANCOVA, percent change, difference scores, and more.
Learn to choose the most appropriate statistical test - Numerous Monte Carlo simulations compare various tests and help you select the best one for your data.
Tackle more difficult analyses - Extensive SAS code saves you programming time and effort.
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