Qualitative Data Analysis

Qualitative data analysis involves interpreting, synthesising, and communicating the data – “making sense of data in terms of the participants’ definitions of the situation, noting patterns, themes, categories and regularities” (Cohen et al., 2011, p. 537). A challenge in qualitative data analysis is its subjective nature; a researcher always has a bias, background, or agenda which leads to a certain interpretation of the data (e.g. over-emphasising some aspects of the data and ignoring others) (Berger, 2015; Cohen et al., 2011). Moreover, “Data obtained are all couched in ‘social events,’ so reporting involves a double hermeneutic process … by which the researcher interprets the data from participants who have already interpreted their world, and then relates them to the audience in his/her own words” (Cohen et al., 2011, p. 540).

To address these inherent challenges in analysing data and interpreting the interpretations of others, it is recommended (Whyte, 1993) to be as clear and honest as possible about the researcher’s aims, roles within the research and theoretical influences (see Chapters 1 and 2, and section 4.6). Clarity about choices and processes also allows others to “use the same assumptions and methods to verify or challenge conclusions” (Cohen et al., 2011, p. 541).

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Qualitative content analysis, the approach used in this research, is only one of “numerous research methods used to analyse text data. Other methods include ethnography, grounded theory, phenomenology, and historical research. Research using qualitative content analysis focuses on the characteristics of language as communication with attention to the content or contextual meaning of the text” (Hsieh and Shannon, 2005, p. 1278). Qualitative content analysis is a “research method for the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns,” and it aims to offer insight and understanding of the phenomenon under investigation (Hsieh and Shannon, 2005, p. 1278). Content analysis “defines a strict and systematic set of procedures for the rigorous analysis, examination and verification of the contents of written data;” it is a process for making replicable and valid inferences from the text (Cohen et al., 2011, p. 563). It is not simply counting words, but rather “examining language intensely for the purpose of classifying large amounts of text into an efficient number of categories that represent similar meanings” (Hsieh and Shannon, 2005, p. 1278).

Content analysis was chosen because it is a suitable approach for interpreting data collected from open-ended questionnaires; it can be used for synthesising information, outlining themes, and describing patterns (Cohen et al., 2011). This approach is consistent with the use of mixed research methods and the pragmatist paradigm underpinning the research undertaken in this Ph.D. research (Cohen et al., 2011).

Within qualitative content analysis, there are also a number of approaches which can be taken to analysing, coding and presenting data – and there is inconsistency in the terminology used in the literature to describe these various approaches (Cohen et al., 2011). In their review of content analysis, Hseih and Shannon (2005) argue that there are three major approaches, differentiated primarily by the processes for developing codes/categories:

(1) Conventional

When theory/research is limited in the phenomena under investigation, this approach is commonly used. Instead of using preconceived labels in coding the data, codes and categories are derived from the data itself. Through immersion in the data, new insights emerge. This process is akin to open coding (Cohen et

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al., 2011) or inductive category development (Hseih and Shannon, 2005, p. 1279; Mayring, 2004).

(2) Directed

This approach is more structured than conventional content analysis. It’s utilised when theory and research about the phenomena under investigation exists, but need validation, conceptual extension, or further exploration and description (Hseih and Shannon, 2005, p. 1279). In contrast to conventional content analysis, codes are determined a priori from the existing theory and research; this process is referred to deductive category development (Mayring, 2004).

(3) Summative

“Fundamentally different” from the other two, a summative approach focuses on key words and their usage, aiming to identifying patterns that would lead to “an interpretation of the contextual meaning of specific terms or content” (Hseih and Shannon, 2005, p. 1286). It is typically used in analysing, for example, word usage in manuscripts, journals or text books. Usually, it begins by searching for words identified by the researcher(s), subsequently looking for euphemisms of those words, and ultimately drawing conclusions based on the usage.

This list of approaches to content analysis, however, is not exhaustive (see Cohen, 2011, pgs. 563-569); moreover, within any of these approaches there are multiple ways to approach the coding, categorising, summarising and presenting data.

In this research, elements of both directed content analysis and conventional content analysis were used, given the research questions and aims. Ezzy’s (2002) approach to qualitative content analysis, which widely is utilised and cited by educational researchers, was employed. The following procedure (Ezzy, 2002, p. 83) was conducted:

• Start with a sample of texts (the units)

• Define the units of analysis (e.g. words, sentences)

• Define the categories (i.e. nodes) to be used for analysis

• Review the texts in order to code them and place them into categories

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After the final step of counting the occurrences of words, codes, and categories, statistical analysis can be applied to help interpret the results (Cohen et al., 2011, p. 564). Weber (as cited in Cohen et al., 2011) also advocates using statistical techniques and quantitative methods in content analysis, as the “highest content-analytic studies use both quantitative and qualitative analysis of texts” p. 564). To help complete this process, this researcher utilised the data analysis software, Nvivo, and its matrix coding query feature, which can help a researcher identify relationships between codes/categories. Sections 5.4, 6.4 and 7.4 explain in more detail how Nvivo was utilised.

As Cohen et al. (2011) summarise, analysing qualitative data is an iterative process that “put simply…involves coding, categorizing (creating meaningful categories into which the units of analysis – words, phrases, sentences, etc. – can be placed), comparing (categories and making links between them), and concluding – drawing theoretical conclusions from the text” (p. 564).

The subsequent chapters (5, 6 and 7) provide further description of the development of the codes/categories and the coding processes utilised in analysing data collected during each phase of the research. For example, the coding development process in phase 1 (Exploratory Case Study: Students) was utilised to refine and modify codes to create a coding schema, which then was used to analyse the data the of the Explanatory Case Study: Students (there were some additional minor modifications to reflect the different subject content studied in the learning interventions). Following on, the researcher utilised the same process to develop a coding schema for the third phase of the research – the Explanatory Case Study: Teachers. Using both qualitative and quantitative measures, the researcher provides interpretations of the data collected from the questionnaires to answer the research questions and present additional significant themes arising.