Data Analysis Techniques

According to Yin (2009) data analysis can be defined as investigating, categorising, organising, tabulating, testing or otherwise recombining evidence, to draw empirically based conclusions. Without an understanding of how data can be analysed, fieldwork can generate an enormous data from different sources of data collection if caution is not taken (Easterby-Smith et al., 2004). For example content analysis which also called thematic analysis is used to analyse data collected during this research.

4.7.1 Content analysis

Content analysis is one of the common methods in analysing qualitative data. Krippendorff (2004) defines content analysis as a research technique which allows replication and permits valid inferences from text to the context of their use, which is more qualitative in nature. Collis and Hussy (2009) explained that content analysis has two types: conceptual analysis and relational analysis. According to Stemler and Steve (2001), conceptual analysis generally has a number of steps to be undertaken. The first step as explained by Stemler and Steve (2001) is to decide the level of analysis, whether to code for single word or for sets of words. Once this is decided, the second step is to determine how many concepts should be coded, while the third step is to decide whether to code existence or frequency of concept. These steps then help to

To validate the developed framework with

concerned stakeholders x x

To develop guidelines based on validation of framework for emergency management stakeholders in order to ensure effective Early Warning Systems in the UAE

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determine the level of generalisation which will be carried out in relation to the research aim, objectives and/or questions (Collis and Hussy, 2009).

Therefore data analysis for this research has two parts using an adapted version of the steps required for content analysis. The first part involves the analysis of quantitative data collected through interview case study and text words in questionnaire, which were all triangulated with ontology of emergency and disaster management as examined in literature review. The second part involves the analysis of qualitative data which has been grouped based on themes and patterns of responses created using the research questions and objectives. This approach is in line with what Saunders et al., (2009) emphasised that, one of the most common data analysis method for qualitative data is through thematic analysis which include categorising or coding of data, commencing from interview transcripts from the fieldwork.

Using the transcripts or notes of qualitative interviews through thorough reading, themes or word which has been coded can be selected and analysed (Saunders, et al., 2009). Furthermore, Silverman (2010) also explained that qualitative research can be analysed using content analysis similar in concept to thematic analysis using classification of themes and collating pattern of answers to research questions or objectives. Regardless of the preferred word adopted for analysing qualitative research, the following steps were introduced by Leedy and Ormrod (2001) as guidance to analyse the collected data through qualitative case study research:

1. Categorization of data: data should be categorized and classified into meaningful groups. 2. Interpretation of each issue: documents, responses and any data elements should be

carefully examined for exact meaning in relation to the case study.

3. Identification of designs and patterns: the interpretations of data should be analysed for underlying themes.

4. Grouping and generalization: an overall description of the cases. Conclusions are drawn that may have implications outside the specific case study under study.

This research has adopted these steps to analyse the data collected through the interview, documentation, literature review, but more importantly, the questionnaire words and interview data. The actual software used to further verify the data collected during this research is explained in section 4.7.2.

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4.7.2 Using computer software for data analysis

According to Kumar (2005) there are indications that the computer programs can accelerate, handle complicated statistical techniques, display the analysed data and present them in graphically way. While many of such computer programs are more commonly used for quantitative data analysis, there also are also some computer programs that can be used in analysis of quantitative and qualitative data. Eriksson and Kovalainen (2008) emphasised that using computer programs help the researcher to deal with analysing qualitative data such as; writing up or transliterating notes, coding interviews, editing, attaching key words and data linking, and report writing. Software program such as Nvivo is one of the most common packages used for qualitative data analysis. It has many benefits which enables the software to manage and analyse a variety of data source from different type such as video, audio, images; revise the text without affecting the coding; review and recode coded data; allows quick modification to coding.

According to Bazeley (2008), Nvivo can assist in analysing qualitative data in terms of managing and organising data. Nvivo provides rapid access to conceptual and theoretical knowledge; graphically model that built from the data concepts and shows the relationships between the data concept before reporting the data (Bazeley, 2008). This encouraged the use of Nvivo in this research in order to ensure that the results are as objective as possible. In addition to this, there are a number of statistical tools and tests which were used in order to achieve the objectives of this research. The statistical package for social scientists (SPSS) software is known to be particularly useful for quantitative analysis research in psychology, sociology, psychiatry, and other behavioural sciences (Landau and Everett, 2004). SPSS allows performing descriptive and inferential statistics (Sawalha, 2011), which is crucial for this research based on the inductive approach adopted for analysing interpretivsim philosophy research like this one. Based on the above description and discussion of different techniques for data analysis, the researcher used content analysis (conceptual analysis) for qualitative data (semi-structured interview) where the oral notes were transferred into written records then classified and coded using NVivo 10. The rational for this is to explore the respondents‟ views about the concept of EWS among professionals in the emergency and disaster management sector in the UAE. Whereas, the

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quantitative data collected from questionnaire was analysed using SPSS software version 16. The analysis of questionnaire helped the researcher to design a framework which can potentially enhance EWS and capabilities for increasing disaster resilience in the UAE.