Quantitative Methods in Tourism: A Handbook (Aspects of Tourism) - Hardcover
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Inhaltsangabe
Tourism studies often deal with complex mixes of external and local factors and the attitudes, perceptions and actions of tourists themselves. In seeking to understand individual elements of this mix, or the results of interactions between them, tourism authorities, managers and researchers often collect quantitative data, but until now the few existing guides to understanding quantitative data have been either very simple or very complicated. This book provides a guide to dealing with real-world data and goes beyond the methods usually covered in introductory textbooks. The first part considers key issues associated with using well known methods to produce valid and reliable models of real-world phenomena, emphasizing issues in data selection, approaches to factor and cluster analysis, and mathematical modelling using regression methods (including logistic regression) and structural equation modelling. The second part covers new approaches to modelling: maximum likelihood estimation, simulation and agent-based modelling. Each chapter includes extensive references to additional reading, and an appendix summarises the software introduced in the book. The book provides many practical examples of applications to tourism research, considers practical issues associated with application of quantitative techniques, and discusses common pitfalls and how to identify and remedy them. The result is a guide to quantitative methods in tourism that de-mystifies both simple and apparently complex techniques and makes them more accessible to tourism researchers.
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Über die Autorinnen und Autoren
Rodolfo Baggio holds a degree in Physics (MPhys) and a PhD in Tourism Management. After having worked for leading information technology firms for over 20 years he is presently at the Bocconi University, Milan, Italy, where he teaches courses in Computer Science and coordinates the Information and Communication Technologies area at the Master in Economics and Tourism. He is also Research Fellow at the Carlo F. Dondena Centre for Research on Social Dynamics. He has managed several international research projects and actively researches and publishes in the field of information technology and tourism. His current interests focus on the application of complexity theory and network analysis methods to the study of tourism destinations.
Jane Klobas is Alberto Dondena Research Fellow at the Carlo Dondena Centre for Research on Social Dynamics at Bocconi University in Milan, Italy, and Professor at the University of Western Australia Business School. She teaches quantitative research methods to undergraduate, master and doctoral degree students in Italy and Australasia, and conducts applied research using both qualitative and quantitative research methods. She is author or co-author of several books and book chapters, and has published research on the psychology and management of technology-mediated learning and knowledge sharing in many journals.
Rodolfo Baggio holds a degree in Physics (MPhys) and a PhD in Tourism Management. After having worked for leading information technology firms for over 20 years he is presently at the Bocconi University, Milan, Italy, where he teaches courses in Computer Science and coordinates the Information and Communication Technologies area at the Master in Economics and Tourism. He is also Research Fellow at the Carlo F. Dondena Centre for Research on Social Dynamics. He has managed several international research projects and actively researches and publishes in the field of information technology and tourism. His current interests focus on the application of complexity theory and network analysis methods to the study of tourism destinations.Jane Klobas is Alberto Dondena Research Fellow at the Carlo Dondena Centre for Research on Social Dynamics at Bocconi University in Milan, Italy, and Professor at the University of Western Australia Business School. She teaches quantitative research methods to undergraduate, master and doctoral degree students in Italy and Australasia, and conducts applied research using both qualitative and quantitative research methods. She is author or co-author of several books and book chapters, and has published research on the psychology and management of technology-mediated learning and knowledge sharing in many journals.
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Quantitative Methods in Tourism
A Handbook
By Rodolfo Baggio, Jane KlobasMultilingual Matters
Copyright © 2011 Rodolfo Baggio and Jane Klobas
All rights reserved.
ISBN: 978-1-84541-174-9All rights reserved.
Contents
List of Examples, vii,
List of Figures, ix,
List of Tables, xv,
Contributors, xvii,
Foreword, xix,
Introduction, xxi,
Part 1: The Analysis of Data,
1 The Nature of Data in Tourism, 5,
2 Testing Hypotheses, 21,
3 Data Analysis, 42,
4 Model Building, 88,
5 Time-dependent Phenomena and Forecasting, 136,
Part 2: Numerical Methods,
6 Maximum Likelihood Estimation, 175,
7 Monte Carlo Methods, 189,
8 Agent-based Modeling and Simulations, 199,
Appendix: Software Programs, 220,
CHAPTER 1
The Nature of Data in Tourism
This chapter contains a brief review of the nature of data as used in tourism and hospitality, and discusses the main quality characteristics needed to obtain useful and reliable outcomes from data analysis. A list of the main sources of tourism data is provided.
Introduction
The protagonist in the adventures described in this book is the datum, better known in its plural form, data. The original Latin meaning, something given (and accepted as true), defines it well. It is (usually) a number, the result of some observation or measurement process, objectively representing concepts or other entities, put in a form suitable for communication, interpretation or processing by humans or automated systems. By themselves, and out of a specified context, data have no meaning at all; they are merely strings of symbols. Once organized or processed in some way, and associated with some other concepts or entities, they become useful information, assuming relevance, purpose, providing insights into phenomena, allowing judgments to be made and decisions to be taken (if interested in a discussion of these concepts, the review by Zins (2007) is a good starting point). All statistical techniques have exactly this objective.
Many disciplines, and tourism is no exception, require large quantities of data. The main challenge a researcher has today is that of managing a huge quantity, variety and complexity of data types, and of being sure to obtain useful and valid outcomes.
Data: A Taxonomy
It is possible to categorize data in several ways. One distinction is between primary and secondary data. Another classifies data by its level of measurement or measurement scale. Yet another is the medium or form from which the data are derived. We provide a brief overview of the key issues associated with data of each type here.
The distinction between primary and secondary data is made on the basis of the source of data and its specificity to the study for which it is gathered. Each type of source has strengths and weaknesses, the focus of our discussion here.
Primary data
Primary data are those collected directly from the original or 'primary' source by researchers through methods such as direct observation (both human observation and automatic collection of data such as clicks on links in Web sites or through use of other information and communications technology), questionnaire surveys (online, printed or administered by telephone or computer), structured or unstructured interviews and case studies. To be classified as primary data, the data elements collected using any one of these techniques will be unique and tailored to the specific purposes of the study conducted. The most used techniques, their strengths and limitations are well described in many books (Babbie, 2010; Creswell, 2003; Hair et al., 2005; Neuman, 2006; Phillimore & Goodson, 2004; Veal, 2006; Yin, 1994). Here, we concentrate on recent developments and issues of particular relevance to tourism research.
The main disadvantages are well known: cost and time. Collecting tailored information tends to be expensive in terms of resources needed (money and people) and it may take a long time to properly design the research and process the results. Recently, the use of the internet and the world wide web has reduced the cost and time requirements for conducting surveys. However, unless used carefully, the use of online surveys can hide problems related to the representativeness of the sample and the technical characteristics of the medium used, and individual differences among respondents can bias results. Of course, these concerns are not unique to electronic media, but can be exacerbated by the seductive ease and speed of online data collection. Indeed, many survey experts consider internet surveying (provided the sample is representative) to provide valid, reliable and relatively error-free results, among other reasons because data are captured directly from the respondent without the need for an interviewer or assistant to enter the data separately into a database for analysis (Dillman, 2007).
Regardless of the method used to capture primary data, the researcher should consider and understand well all issues associated with sampling (representativeness and sample size) and obtaining data of suitable quality. From a practical point of view, it is advisable to start any study by surveying a pilot sample and studying the responses obtained. Participants in the pilot study can be asked to identify any questions that they found difficult to understand or to answer and, using a technique known as cognitive interviewing, they can also be asked how they interpreted specific questions. The data collected from a pilot study can be used to estimate population parameters for the statistical models that will be used to draw conclusions from the final survey, information that can be used to determine the data distribution and sample size necessary or desirable for the larger-scale investigation to be conducted effectively (Dillman, 2007; Pan, 2010).
Secondary data
In many cases, collecting primary data is not within the reach of the investigator. Furthermore, it is not always necessary to have primary data to conduct a study. For example, very few researchers would start collecting primary data on the number of tourists visiting a country or on the GDP of some nations. When theoretical or practical reasons do not indicate direct collection of data, secondary data are used. Secondary data are data gathered, typically by someone else, for a purpose other than the study for which they will be used. The main sources of secondary data are government agencies (statistical bureaus, public tourism departments), international associations and institutions, private research companies and industry associations. Data from these sources are available directly from the provider organization (particularly in the case of those public institutions that have an obligation – often by law – to make public the outcomes of their activities) or from libraries and electronic databases. Often, they can be obtained from these sources over the internet. Useful data for some studies can also be found in previously published research or reports and from the databases (typically customer or visitor databases) maintained by individual organizations.
Secondary data, therefore, tend to be readily available and are often free or inexpensive to obtain. In addition, it is often possible to assemble large quantities of data and to draw the data from different sources. On the other hand, secondary data may be more difficult to use and to interpret because, typically, they were gathered by other researchers for...
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