Discrete Choice Experiments Using R

A How-To Guide for Social and Managerial Sciences
2023
ISBN: 978-981-99-4561-0
Verlag: Springer Nature Singapore

Buch, Englisch, 203 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 500 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Chandra, Yanto

Shang, Liang

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologie: Allgemeines
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein
• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologische Forschungsmethoden

Inhaltsverzeichnis

Chapter 1. An Overview of Stated Preference Methods: What and Why.- Chapter 2. A Comparison of Stated Preference Methods.- Chapter 3. Understanding the Fundamentals of DCE Experiment.- Chapter 4. A Review of R and its Applicability DCE.- Chapter 5. Framing the Research Question and Theory.- Chapter 6. Identifying DCE Attributes and Levels.- Chapter 7. Designing DCE Choice Set using R.- Chapter 8. Designing DCE Survey and Collecting Data.- Chapter 9. Analysing DCE Data using R.- Chapter 10. Visualizing and Reporting DCE Data Using R.

Über Autor(innen)

Shang Liang, Ph.D., is Research Assistant Professor in the Department of Social Policy and Sociology at the Lingnan University. She obtained her Ph.D. in Public Policy from the City University of Hong Kong in 2020 and was awarded Sir Edward Youde Memorial Fellowship for Postgraduate Research Students in 2019. Prior to joining Lingnan University, she worked as a Post-doctoral Fellow in the Department of Social Work at the Chinese University of Hong Kong and Research Associate at the Centre for Social Policy and Social Entrepreneurship at the Hong Kong Polytechnic University. With a primary research interest centered around the intersection of ethics and entrepreneurship, she focuses on examining the roles, practices, and mechanisms of business ventures with a strong overarching social purpose in contributing to social and environmental sustainability. Beyond this focus, she also has a keen interest in exploring emerging methods in social sciences, such as corpus linguistics and artificial intelligence. Her work has been published in international journals, including Journal of Social Policy, Journal of Business Venturing Insights, VOLUNTAS, among others.

Yanto Chandra, Ph.D., is Associate Professor of Management and Policy at the City University of Hong Kong. His research focuses on explaining the drivers, processes, and outcomes of entrepreneurial action and strategy in various contexts––social, digital, and international––and their implications to economy, society, and policy. He is best-known for his work on social ventures, born globals, and more recently on Web3 and AI from management and policy perspectives. His methods of interest include computer-aided text analysis, corpus linguistics, text mining, visual methods, image analysis, and data science using R. He is Associate Editor of Journal of Business Venturing Insights and several other key journals in management. His work has been published in Journal of International Business Studies, Journal ofBusiness Venturing, Journal of Business Ethics, Journal of Business Venturing Insights, Journal of World Business, as well as Public Management Review, Public Administration Review, and World Development, among others. His research has won international accolades including Best Paper Award 2022 in the Academy of Management Annual Meeting and Kooiman Prize for Best Paper in 2021.