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• 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