Analysis of primary data

As this research adopted a concurrent triangulation design, data were collected and analysed separately, with data sets bought together during the discussion stage. A number of methods were employed in the analysis of quantitative data. Chi-square tests quantified the significance of relationships hypothesised in the conceptual framework and exploratory factor analysis and cluster analysis were employed in order to create an image based typology. Post-hoc tests were also employed in order to identify cluster characteristics. The data analysis methods, employed on quantitative data will be discussed in this chapter (Section 4.4.1) and analysis provided in Chapter 7. Qualitative data were coded using thematic content analysis in order to identify key themes surrounding memory, nostalgia and place attachment. In order to test the hypotheses and validate the quantitative findings, framework analysis was performed,

identifying key relationships within the transcribed material. Qualitative data analysis methods are introduced in this chapter (Section 4.4.2) while analysis is documented in Chapter 8. Both quantitative and qualitative data analysis was conducted using data analysis software, as such a brief introduction to the software will also be given (Section 4.4.3).

4.4.1 Quantitative analysis

In order to analyse quantitative data Chi-square tests, exploratory factor analysis, cluster analysis, ANOVA and Tukey’s Post-hoc test were utilised. The benefits, limitations and use of these methods within this research will be discussed.

161 Chi-square tests

Chi-square tests were implemented in this research in order to test hypotheses 1-5 which made assumptions about the relationships between variables. Chi-square is a non-parametric test used, to compare observed and expected frequencies, in order to identify a significant relationship between the two variables tested (Cunningham and Aldrich, 2011; Bryman, 2015).

Chi-square is particularly useful in this research for its ability to compare categorical data (Sarantakos, 2012; Pallant, 2013). Although appropriate, given the nature of the data and purpose of chi-square, non-parametric tests are seen to be limited as they are less sensitive than parametric tests (Pallant, 2013).

Exploratory Factor Analysis

Factor analysis is one of the most commonly used statistical tests in applied research (Brown, 2006). Popularity of this technique, in image research, is apparent when reviewing Table 4.1 and Table 4.3, yet the use of factor analysis is also prevalent in segmentation studies

(Galloway, 2002; Petrick, 2005; Lee, Lee, Bernhard and Yoon, 2006; Konu et al., 2011; Phillips and Brunt, 2013). Exploratory factor analysis identifies the inter-correlations of a set of variables (Pallant, 2013) in order to identify their underlying structure (Hinton, Brownlow, McMurray and Cozens, 2004; Hair et al., 2014) and is used, within this study, as a data summarisation technique (Hair et al., 2014). It has been noted that “factor analysis provides the basis for creating a new set of variables that incorporate the character and nature of the original variables in a much smaller number of new variables” (Hair et al., 2014, p. 98). In this case the fifteen original variables were reduced into four factors, which were directly

incorporated into cluster analysis in order to create the typology. Due to the nature of this study, which was seeking to identify variance in image, principal component analysis was chosen over common factor analysis as the extraction method. Principal component analysis

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seeks to identify the linear combination of variables that accounts for the greatest amount of variance and, as such, derives factors that contain a small proportion of unique variance (Hair et al., 2014).

Cluster Analysis

While R-type factor analysis, as described above, grouped variables based upon their inter-correlations, cluster analysis is used in order to group cases based upon their similarity. In this study k-means cluster analysis was utilised in order to allocate respondents into a

predetermined number of clusters. Although hierarchal cluster analysis is deemed more rigorous, K-means cluster analysis has gained acceptability and usage (Hair et al., 2014) as such it has been used in past image segmentation studies (Leisen, 2001).

In order to determine the number of clusters, cluster solutions were tested for significance. As there were 500 cases in this research a cluster solution could range between 2 and 500. Due to the pragmatic nature of this study, and its practical implications for marketing, the number of clusters was restricted to ten as observed in other tourism studies (Goodrich, 1977; Leisen, 2001). K-means cluster analysis was used in this study in order to create a six-fold image based typology. In order to identify the characteristics of these groups ANOVA and Tukey’s post-hoc test were applied.

4.4.2 Qualitative analysis

In order to analyse qualitative data both thematic content analysis and framework analysis were utilised. The capabilities, limitations and use of these methods within this research will be discussed.

163 Thematic Content Analysis

Content analysis was used to examine unstructured data gathered through in-depth

interviews. It has been noted that content analysis is an effective method to examine many forms of unstructured information (Bryman and Bell, 2007) and was utilised in this research to systematically evaluate interview transcripts (Malhotra, 2004; Hall and Valentin, 2005). As “a research technique for making replicable and valid interferences from data to their context”

(Krippendorff, 1980, p. 21) content analysis was a suitable approach to achieve the research aims. In this application content analysis enabled different ‘units of analysis’ to be considered (Bryman and Bell, 2007). Multiple units of analysis were adopted in this study in order to identify a number of themes and the relationships between them. According to Bryman and Bell (2007) “when the process of coding is thematic, a more interpretive approach needs to be taken” (p. 310). However, it has been discussed that “qualitative content analysis is the least interpretive of the qualitative analysis approaches in that there is no mandate to re-present the data in any other terms but their own” (Sandelowski, 2000, p. 338). Therefore, one of the most significant criticisms of content analysis is the subjectivity in the coding process where it must be noted that content analysis is reliant, to some extent, on subjective judgement (Choi et al., 2007a). In order to limit this, as far as possible, a thorough coding scheme was designed and software was used to ensure rigor in the coding. Another criticism of content analysis is its tendency to be labour intensive, particularly when human coding is required (Krippendorff, 1980). Although such an approach still requires a human coder, computer-assisted qualitative data analysis software (CAQDAS) does increase efficiency and accuracy due to the need to organise and analyse data in a methodical manner.

164 Framework Analysis

Many approaches can be employed in the analysis of qualitative data, however there is tendency for researchers to utilise a number of approaches (Green and Thorogood, 2004). In this research framework analysis was also utilised to ensure that data was managed effectively during the process of thematic coding. Framework analysis involves the use of a matrix in order to facilitate cross case analysis (Bazeley and Jackson, 2013). Framework analysis, as described by Ritchie et al. (2014) follows five key steps: familiarisation, constructing a thematic framework, indexing and sorting, reviewing data extracts and data summary and display. In addition to providing an organised approach for analysing unstructured data, this well-defined process allows researchers to reconsider and rework ideas throughout the analysis stage (Ritchie and Spencer, 2000). Despite using the framework approach in order to manage data a number of limitations are identifiable in the analysis of qualitative data where the output relies on the “creative and conceptual abilities of the analyst to determine meaning, salience and connections” (Ritchie and Spencer, 2000, p. 177). Strauss & Corbin (1998) also acknowledge the subjectivity of both selection and interpretation of data collated in qualitative research.

4.4.3 Software packages

Software packages were used in the analysis of both data sets; while SPSS was employed to collate and analyse the quantitative data, NVivo was used to the same end for the qualitative material. The benefits and limitations of using software packages for the purpose of data analysis will be discussed and the capabilities of both SPSS and NVivo explored.

165 SPSS

In order to analyse the quantitative data collected, SPSS, a statistical software package designed for the social sciences was utilised. SPSS is one of the longest standing and most widely known and used software packages available (Uprichard, Burrows and Byrne, 2008).

Initially developed in the late 1960s, SPSS allowed researchers to run basic descriptive statistics, cross tabulations and regression analysis (Uprichard et al., 2008). Today however SPSS facilitates a diverse range of statistical analyses. Consequently, the development of software packages such as SPSS has enabled researchers to apply complex statistical tests to a data set within seconds (Hinton et al., 2004), increasing the efficiency of quantitative data analysis. SPSS has many capabilities, from running descriptive statistics to summarise the data sample to testing statistical association through correlation and regression analysis, t-tests, Chi-squared and ANOVA testing (Antonius, 2013). Furthermore, SPSS can be used to aggregate variables using factor analysis (Bryman and Cramer, 2009). Factor analysis is facilitated by the use of SPSS in this study in order to examine the correlation between variables to identify variations within the data. Despite the advantages that such packages bring in terms of efficiencies limitations do exist. Statistics packages are susceptible to human error, particularly in terms of imputing and coding data. Although software makes it convenient to run a

multitude of tests, a deep level of understanding is necessary in order to interpret their output. Furthermore, a thorough knowledge of the capability of such software packages is needed to ensure that it can be utilised to full capacity.

NVivo

NVivo is just one example of CAQDAS software, which is able to assist researchers in analysing qualitative data through increased effectiveness and efficiency. Such software has numerous capabilities, allowing researchers to explore alternative meaning in the data (Richards, 2002)

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identify gaps in the data (Wickham and Woods, 2005) and revisit data to explore new concepts or viewpoints (Sin, 2007). NVivo assists in the management of data (Bazeley and Jackson, 2013) and was used effectively in this study to collate interview transcripts and perform framework analysis. NVivo was also used to visualise the data, as the software is able to identify the content and structure of a range of items and visually represent relationships between them (Bazeley and Jackson, 2013). This capacity was utilised throughout the analysis process in order to illustrate relationships between the memories and themes identified within the different cases. For the purpose of this research NVivo facilitated both thematic content analysis and framework analysis.

Bazeley and Jackson (2013, p. 3) argue that using software such as NVivo can help to “ensure rigour in the analysis process” as software encourages the researcher to work more

methodically, thoroughly and attentively. The main benefit of using CAQDAS software is, however, increased efficiency where the software is able to sort data far more expediently than if it were to be sorted manually (Ritchie et al., 2014). Despite the benefits gained, through the use of CAQDAS software, human error limits the analysis as software cannot compensate for poor work or “limited interpretive capacity” (Bazeley and Jackson, 2013, p. 3).

Despite the benefits of CAQDAS software a number of concerns have been raised regarding the impact of this software on the process of qualitative data analysis, most notably about forfeiting a close relationship with the data (Fielding & Lee, 1998; Weitzman & Miles, 1995).

Exploring this notion, a number of researchers have argued however that software can allow users to get too close causing a code retrieve cycle (Marshall, 2002; Jackson, 2003; Johnston, 2006; Ritchie et al., 2014). In this way NVivo and other CAQDAS packages facilitate a more positivist approach where emphasis is often placed on the coding and quantifying of data (Bazeley and Jackson, 2013).

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