Efnisyfirlit

• 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

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