This book provides ten case studies with concise real-world examples illustrating how MAXQDA is used in practice. In each research example, the authors present their way of using MAXQDA, addressing a variety of practical questions, such as how the coding system was developed, how coded documents were analyzed, what tools were used, and how those tools informed the results. Embedded in the context of each research example, readers can follow analytical processes step-by-step and gain insights into efficient ways to use MAXQDA. Readers can benefit from this book in several ways: - Get inspirations and solutions for their own projects - Avoid pitfalls by considering the lessons learned from each author presented at the end of each chapter - Use the examples in teaching Audience: - All researchers who want to get to learn the potential of qualitative data analysis with MAXQDA - All users of MAXQDA who want to expand their knowledge and skills, no matter if they are novices or experts
Designed to introduce students to quantitative methods in a way that can be applied to all kinds of data in all kinds of situations, Statistics and Data Visualization Using R: The Art and Practice of Data Analysis by David S. Brown teaches students statistics through charts, graphs, and displays of data that help students develop intuition around statistics as well as data visualization skills. By focusing on the visual nature of statistics instead of mathematical proofs and derivations, students can see the relationships between variables that are the foundation of quantitative analysis. Using the latest tools in R and R RStudio® for calculations and data visualization, students learn valuable skills they can take with them into a variety of future careers in the public sector, the private sector, or academia. Starting at the most basic introduction to data and going through most crucial statistical methods, this introductory textbook quickly gets students new to statistics up to speed running analyses and interpreting data from social science research.
"Multivariate Data Analysis - in practice adopts a practical, non-mathematical approach to multivariate data analysis. The book's principal objective is to provide a conceptual framework for multivariate data analysis techniques, enabling the reader to apply these in his or her own field. Features: Focuses on the practical application of multivariate techniques such as PCA, PCR and PLS and experimental design. Non-mathematical approach - ideal for analysts with little or no background in statistics. Step by step introduction of new concepts and techniques promotes ease of learning. Theory supported by hands-on exercises based on real-world data. A full training copy of The Unscrambler (for Windows 95, Windows NT 3.51 or later versions) including data sets for the exercises is available. Tutorial exercises based on data from real-world applications are used throughout the book to illustrate the use of the techniques introduced, providing the reader with a working knowledge of modern multivariate data analysis and experimental design. All exercises use The Unscrambler, a de facto industry standard for multivariate data analysis software packages. Multivariate Data Analysis in Practice is an excellent self-study text for scientists, chemists and engineers from all disciplines (non-statisticians) wishing to exploit the power of practical multivariate methods. It is very suitable for teaching purposes at the introductory level, and it can always be supplemented with higher level theoretical literature."Résumé de l'éditeur.
Spatial Data Analysis: Theory and Practice, first published in 2003, provides a broad ranging treatment of the field of spatial data analysis. It begins with an overview of spatial data analysis and the importance of location (place, context and space) in scientific and policy related research. Covering fundamental problems concerning how attributes in geographical space are represented to the latest methods of exploratory spatial data analysis and spatial modeling, it is designed to take the reader through the key areas that underpin the analysis of spatial data, providing a platform from which to view and critically appreciate many of the key areas of the field. Parts of the text are accessible to undergraduate and master's level students, but it also contains sufficient challenging material that it will be of interest to geographers, social and economic scientists, environmental scientists and statisticians, whose research takes them into the area of spatial analysis.
Compositional data are quantitative descriptions of the parts of some whole, conveying exclusively relative information. Examples are found in various fields, including geology, medicine, chemistry, agriculture, economics, social science, etc. This concise book presents a very applied introduction to compositional data analysis, focussing on the use of R for analysis. It includes lots of real examples, code snippets, and colour figures, to illustrate the methods.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.
This collection of essays brings together many of the world's most distinguished statisticians to discuss a wide array of the most important recent developments in data analysis. The book honors John W. Tukey, one of the most influential statisticians of the twentieth century, on the occasion of his eightieth birthday. Contributors, some of them Tukey's former students, use his general theoretical work and his specific contributions to Exploratory Data Analysis as the point of departure for their papers. They cover topics from "pure" data analysis, such as gaussianizing transformations and regression estimates, and from "applied" subjects, such as the best way to rank the abilities of chess players or to estimate the abundance of birds in a particular area. Tukey may be best known for coining the common computer term "bit," for binary digit, but his broader work has revolutionized the way statisticians think about and analyze sets of data. In a personal interview that opens the book, he reviews these extraordinary contributions and his life with characteristic modesty, humor, and intelligence. The book will be valuable both to researchers and students interested in current theoretical and practical data analysis and as a testament to Tukey's lasting influence. The essays are by Dhammika Amaratunga, David Andrews, David Brillinger, Christopher Field, Leo Goodman, Frank Hampel, John Hartigan, Peter Huber, Mia Hubert, Clifford Hurvich, Karen Kafadar, Colin Mallows, Stephan Morgenthaler, Frederick Mosteller, Ha Nguyen, Elvezio Ronchetti, Peter Rousseeuw, Allan Seheult, Paul Velleman, Maria-Pia Victoria-Feser, and Alessandro Villa. Originally published in 1998. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
