Handbook of Research Methods in Tourism

Handbook of Research Methods in Tourism

Quantitative and Qualitative Approaches

Elgar original reference

Edited by Larry Dwyer, Alison Gill and Neelu Seetaram

This insightful book explores the most important established and emerging qualitative and quantitative research methods in tourism. The authors provide a detailed overview of the nature of the research method, its use in tourism, the advantages and limitations, and future directions for research.

Chapter 11: Cluster Analysis

Liz Fredline

Subjects: development studies, tourism, environment, environmental sociology, tourism, geography, tourism, research methods, qualitative research methods, quantitative research methods


Liz Fredline INTRODUCTION The purpose of this chapter is to describe and illustrate the analytical technique known as cluster analysis and to outline its application in tourism research. This technique can be extremely useful for certain research questions, but a review of literature shows that its application has been quite infrequent. The chapter begins with a brief summary of the nature of the technique followed by a summary of some of the tourism literature which has employed cluster analysis. Following on from this is a summary of a worked example (using IBM SPSS 19.0) of a two-stage cluster analysis procedure. Finally, the chapter concludes with a discussion of the advantages and limitations of the technique as well as the possible future advances in tourism research which may be possible with the application of cluster analysis. Cluster analysis is a family of multivariate techniques useful for analysing cases based on their scores on a range of measured variables. Essentially the technique identifies cases with a comparable pattern of responses that can be regarded, for the purposes of the analysis, as similar. In so doing a large number of individual cases are summarized into a smaller, more manageable number of clusters. The outcome of a successful cluster analysis would be a small number of highly homogeneous clusters that are substantially different to each other (Hair et al., 1998). Cluster membership can then be used as a variable for further analysis aimed at understanding the clusters and the bases on which they were...

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