Mathematics: Starting out

Practical and up-to-date, Structural Equation Modeling includes chapters on major aspects of the structural equation modeling approach to research design and data analysis. The chapters, written by internationally recognized leaders in the development and application of structural equation modeling, target graduate students and seasoned researchers in the social and behavioral sciences who wish to understand the basic concepts and issues associated with the structural equation modeling approach as well as potential applications to research problems. Although technically sound, the chapters are primarily nontechnical in content and style, a characteristic that makes the volume an excellent introduction to the structural equation modeling approach for readers who have a firm grasp on traditional inferential statistics.

This book explores the PhD experience as never before and provides a “survival guide” for current and prospective PhD students. The book investigates why mental health issues are so common among the postgraduate population, going beyond the statistics, looking at lived experience of both the author and as well as current PhD students, who have found balancing mental wellness with the PhD endeavour challenging.The author discusses tips and tricks she wished she had known at the start of her PhD process for managing mental health, such as managing imposter feelings, prioritising workload, and self-care strategies to help others throughout their own journey.

Science has never been more important, yet science education faces serious challenges. At present, science education research only sees half the picture, focusing on how students learn and their changing conceptions. Both teaching practice and what is taught, science knowledge itself, are missing. This book offers new, interdisciplinary ways of thinking about science teaching that foreground the forms taken by science knowledge and the language, imagery and gesture through which they are expressed. This book brings together leading international scholars from Systemic Functional Linguistics, a long-established approach to language, and Legitimation Code Theory, a rapidly growing sociological approach to knowledge practices.

The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata by Xing Liu helps readers learn how to conduct analyses, interpret the results from Stata output, and present those results in scholarly writing. Using step-by-step instructions, this non-technical, applied book leads students, applied researchers, and practitioners to a deeper understanding of statistical concepts by closely connecting the underlying theories of models with the application of real-world data using statistical software.

The chief goal in this textbook is to show students how calculus relates to biology, with a style that maintains rigor without being overly formal. The text motivates and illustrates the topics of calculus with examples drawn from many areas of biology, including genetics, biomechanics, medicine, pharmacology, physiology, ecology, epidemiology, and evolution, to name a few. Particular attention has been paid to ensuring that all applications of the mathematics are genuine, and references to the primary biological literature for many of these has been provided so that students and instructors can explore the applications in greater depth. Although the focus is on the interface between mathematics and the life sciences, the logical structure of the book is motivated by the mathematical material. Students will come away from a course based on this book with a sound knowledge of mathematics and an understanding of the importance of mathematical arguments. Equally important, they will also come away with a clear understanding of how these mathematical concepts and techniques are central in the life sciences.

The fourth edition of Generalized Linear Models and Extensions gives a comprehensive overview of the nature and scope of generalized linear models (GLMs) and of the major changes to the basic GLM algorithm that allow modeling of data that violate GLM distributional assumptions. The text stands out in its coverage of the derivation of the GLM families and their foremost links, but it also guides the reader in applying the various models to real data. This edition has new sections on bivariate and multivariate models including bivariate count data models estimated via copula functions and models based on bivariate distributions put forward by Famoye and by Marshall and Olkin. In addition, there are new sections on Bayesian GLMs illustrating background, the estimation of models using the bayesmh command of Stata 14, and the updated bayes prefix syntax available in Stata 15.