Quantitative Methods in Tourism

Höfundar: Rodolfo Baggio; Jane Klobas (Útgáfa: 2)
Quantitative Methods in Tourism

Kaup valmöguleikar

In this revised second edition, Baggio and Klobas build upon the work of their previous volume, offering a presentation of quantitative research methods for tourism researchers. This accessible and rigorous guide goes beyond the approaches usually covered in introductory textbooks on quantitative methods to consider useful techniques for statistical inquiry into tourism matters of all but the most econometrically complex kind.

The first part of the book concerns common issues in statistical analysis of data and the most widely-used techniques, while the second part describes and discusses several newer and less common approaches to data analysis that are valuable for tourism researchers and analysts. Updates to the second edition include: • a new chapter on “Big Data”  • consideration of data screening and cleaning • the use of similarity and diversity indexes for comparing samples • observations about the partial least squares (PLS) approach to path modelling • a new section on multi-group structural equation modelling • a new section on common method variance and its treatment • revised and updated section on software • fully updated references and examples.

Nánar um bókina

Útgefandi
Ingram Publisher Services UK- Academic
ISBN
9781845416218
Print ISBN
9781845416188
Format
ePub
Útgáfa
2
Höfundar
Rodolfo Baggio; Jane Klobas
Tungumál
English
Útgefið
2017-07-06
Prent takmörkun á líftíma
100

Kaflar

  • Cover-Page
  • Half-Title
  • Series
  • Title
  • Copyright
  • Contents
  • Contributors
  • Foreword
  • Introduction to the Second Edition
  • Introduction
  • Part 1: The Analysis of Data
  • 1 The Nature of Data in Tourism
  • Data: A Taxonomy
  • Primary data
  • Secondary data
  • Combining primary and secondary data
  • Data Harmonisation, Standards and Collaboration
  • Quantitative and categorical data
  • The many forms of data
  • Data Quality
  • Data screening and cleaning
  • Why screen data?
  • Concluding remarks
  • Sources of Secondary Tourism Data
  • International organisations
  • Associations
  • Private companies
  • References
  • 2 Testing Hypotheses and Comparing Samples
  • Parametric and Non-Parametric Tests
  • Effect Size and Statistical Power
  • Sample Size and Significance
  • Bootstrap
  • Meta-analysis
  • A Summary of Statistical Tests
  • Similarity and Dissimilarity Measures
  • Similarity measures for a single sample
  • Similarity measures for two or more samples
  • Mahalanobis Distance and Multivariate Outlier Detection
  • References
  • 3 Data Reduction
  • Factor Analysis
  • Techniques for exploratory factor analysis
  • Choosing the number of factors to extract
  • Selecting variables
  • Rotation and interpretation of factors
  • Using the Results of a Factor Analysis
  • Data Considerations and Other Issues in Factor Analysis
  • Cluster Analysis
  • How cluster analysis works
  • Using distance measures to represent similarity and difference in cluster analysis
  • Partitioning
  • Hierarchical cluster analysis
  • Evaluating and improving cluster analysis solutions
  • Multidimensional Scaling and Correspondence Analysis
  • References
  • 4 Model Building
  • Simple Regression
  • The regression equation
  • Initial inspection of the data: Is there evidence of a linear relationship?
  • A Solution for Non-Linearity: Transformation
  • Measuring the quality of the linear regression model
  • The statistical significance of the regression model
  • Assumptions that must be met for a valid linear regression model
  • Assessing the Validity of Assumptions
  • More pitfalls: Influential values and outliers
  • The extrapolation limitation
  • Multiple Regression
  • Modelling categorical variables
  • Assessing the quality of a multiple regression model
  • The multicollinearity problem
  • Choosing a multiple regression model
  • Logistic Regression
  • The logistic regression model
  • Assumptions of logistic regression
  • Interpreting and reporting the results of logistic regression analyses
  • Evaluating the quality of a logistic regression model
  • Path Modelling
  • Comparing SEM and PLS
  • The language of covariance-based structural equation modelling
  • Specifying a structural equation model
  • Basic operations of SEM
  • Measuring the fit of a structural equation model
  • Assumptions of SEM and associated issues in estimation
  • Measurement models and structural models
  • Dealing with small samples
  • Mediation and Moderation in Model Building
  • Mediation
  • Moderation
  • Multilevel Modelling
  • Hierarchically structured data
  • Testing for multilevel effects
  • Modelling multilevel effects
  • Multilevel Regression Models
  • Multi-Group Analysis in Structural Equation Modelling
  • Common Method Variance: A Special Case of Multilevel Variance
  • The effects of CMV
  • Techniques for identification and remediation of CMV
  • References
  • 5 Time-Dependent Phenomena and Forecasting
  • Basic Concepts of Time Series
  • Smoothing methods
  • Autoregressive integrated moving average models
  • Filtering Techniques
  • Hodrick–Prescott filter
  • Comparing Time Series Models
  • Combining Forecasts
  • Correlation between Series
  • Stationarity, Stability and System Representations
  • Predictability
  • Non-linearity (BDS test)
  • Long-range dependency (Hurst exponents)
  • References
  • Part 2: Numerical Methods
  • 6 Maximum Likelihood Estimation
  • Estimating Statistical Parameters
  • Likelihood Ratio Test
  • References
  • 7 Monte Carlo Methods
  • Numerical Experiments
  • Random and Pseudorandom Numbers
  • References
  • 8 Big Data
  • Technology
  • Data collection tools
  • Some Statistical Remarks
  • Artificial Intelligence and Machine Learning
  • Supervised learning
  • Unsupervised learning
  • Concluding Remarks
  • References
  • 9 Simulations and Agent-Based Modelling
  • Complex Adaptive Systems and Simulations
  • Agent-Based Models
  • Issues with Agent-Based Models
  • Evaluation of an Agent-Based Model
  • ABM and Tourism
  • Concluding Remarks
  • References
  • Appendix: Software Programs
  • Software List
  • Statistical Packages
  • Generic packages
  • Specialised programs cited in this book
  • Development environments and programming languages
  • References
  • Subject Index