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Stated Preference Methods Using R explains how to use stated preference (SP) methods, which are a family of survey methods, to measure people’s preferences based on decision making in hypothetical choice situations. Along with giving introductory explanations of the methods, the book collates information on existing R functions and packages as well as those prepared by the authors. It focuses on core SP methods, including contingent valuation (CV), discrete choice experiments (DCEs), and best–worst scaling (BWS). Several example data sets illustrate empirical applications of each method with R. Examples of CV draw on data from well-known environmental valuation studies, such as the Exxon Valdez oil spill in Alaska. To explain DCEs, the authors use synthetic data sets related to food marketing and environmental valuation. The examples illustrating BWS address valuing agro-environmental and food issues. All the example data sets and code are available on the authors’ website, CRAN, and R-Forge, allowing readers to easily reproduce working examples. Although the examples focus on agricultural and environmental economics, they provide beginners with a good foundation to apply SP methods in other fields. Statisticians, empirical researchers, and advanced students can use the book to conduct applied research of SP methods in economics and market research. The book is also suitable as a primary text or supplemental reading in an introductory-level, hands-on course.
This is a practical book with clear descriptions of the most commonly used nonmarket methods. The first chapters of the book provide the context and theoretical foundation of nonmarket valuation along with a discussion of data collection procedures. The middle chapters describe the major stated- and revealed-preference valuation methods. For each method, the steps involved in implementation are laid out and carefully explained with supporting references from the published literature. The final chapters of the book examine the relevance of experimentation to economic valuation, the transfer of existing nonmarket values to new settings, and assessments of the reliability and validity of nonmarket values. The book is relevant to individuals in many professions at all career levels. Professionals in government agencies, attorneys involved with natural resource damage assessments, graduate students, and others will appreciate the thorough descriptions of how to design, implement, and analyze a nonmarket valuation study.
This book delivers a user guide reference for researchers seeking to build their capabilities in conducting discrete choice experiment (DCE). The book is born out of the observation of the growing popularity – but lack of understanding – of the techniques to investigate preferences. It acknowledges that these broader decision-making processes are often difficult, or sometimes, impossible to study using conventional methods. While DCE is more mature in certain fields, it is relatively new in disciplines within social and managerial sciences. This text addresses these gaps as the first ‘how-to’ handbook that discusses the design and application of DCE methodology using R for social and managerial science research. Whereas existing books on DCE are either research monographs or largely focused on technical aspects, this book offers a step-by-step application of DCE in R, underpinned by a theoretical discussion on the strengths and weaknesses of the DCE approach, with supporting examples of best practices. Relevant to a broad spectrum of emerging and established researchers who are interested in experimental research techniques, particularly those that pertain to the measurements of preferences and decision-making, it is also useful to policymakers, government officials, and NGOs working in social scientific spaces.
A multidisciplinary graduate and practitioner guide, first published in 2000, which offers the theory and application of stated choice methods.
A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.
The monetary valuation of environmental goods and services has evolved from a fringe field of study in the late 1970s and early 1980s to a primary focus of environmental economists over the past decade. Despite its rapid growth, practitioners of valuation techniques often find themselves defending their practices to both users of the results of applied studies and, perhaps more troubling, to other practitioners. One of the more heated threads of this internal debate over valuation techniques revolves around the types of data to use in performing a valuation study. In the infant years of the development of valuation techniques, two schools of thought emerged: the revealed preference school and the stated preference school, the latter of which is perhaps most associated with the contingent valuation method. In the midst of this debate an exciting new approach to non-market valuation was developed in the 1990s: a combination and joint estimation of revealed preference and stated preference data. There are two primary objectives for this book. One objective is to fill a gap in the nonmarket valuation "primer" literature. A number of books have appeared over the past decade that develop the theory and methods of nonmarket valuation but each takes an individual nonmarket valuation method approach. This book considers each of these valuation methods in combination with another method. These relationships can be exploited econometrically to obtain more valid and reliable estimates of willingness-to-pay relative to the individual methods. The second objective is to showcase recent and novel applications of data combination and joint estimation via a set of original, state-of-the-art studies that are contributed by leading researchers in the field. This book will be accessible to economists and consultants working in business or government, as well as an invaluable resource for researchers and students alike.
The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover, recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas, Statistics in Toxicology Using R explains the statistical analysi
The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
This book provides a general introduction to the R Commander graphical user interface (GUI) to R for readers who are unfamiliar with R. It is suitable for use as a supplementary text in a basic or intermediate-level statistics course. It is not intended to replace a basic or other statistics text but rather to complement it, although it does promote sound statistical practice in the examples. The book should also be useful to individual casual or occasional users of R for whom the standard command-line interface is an obstacle.
Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and ComputationData Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts