How do you see the future of interior design education in the UK?

6.6 Data Analysis

Data analysis is a significant phase of any research (Creswell, 2014), as it is the sculpting of raw materials to turn them into a valuable piece of knowledge. Moreover, qualitative data analysis transforms the detailed, rich, and complex data into meaningful ideas, theories and knowledge (Savin- Baden and Major, 2013).

The discussion of the data analysis is divided into two sections. The first one explains the theoretical framework and the ontological perspective of how to deal with, analyse, and use the data to reach the aim of the research. The second section explains the actual steps of the analysis process undertaken, from collecting the data to framing the results and conclusion.

The theoretical framework for data analysis gives a clear sense of the aim of the results and the findings. This does not mean knowing exactly what the results and findings will be, but rather it gives a general sense of what knowledge would be pursued in relation to the research aims and objectives. There are four main approaches in Grounded Theory, they are; Classic Grounded Theory CGT, Constructive Aata Analysis, Qualitative Data Analysis, and Feminist Grounded Theory (Hernandez, 2009). The first two theories have been chosen. CGT was used for understanding the studio culture substantively, as this research investigated the participants’ domain – the studio culture, along with its meanings and perceptions across three different interior design programs. I chose to adopt a substantive approach that allowed me to be open and flexible with data (Glaser and Strauss 1965), alongside the constructive theoretical approach that led to the

construction of theories from the data (Charmaz, 2014). The substantive approach keeps the researcher’s thinking flexible, open and imaginative in relation to the research subject, so as to enable the grounding of new theories (Glaser and Strauss, 2009). The theoretical

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analysis approach was used for the investigation of a definition for creativity, as thematic structural analysis is required to do (Charmaz, 2014).

A thematic cross-sectional data analysis strategy was applied to identify themes, codes and categories across all data in all cases, in order to build a theory upon a saturation of categories of repetitive themes. The output of the research mainly consists of meaningful qualitative verbal data and excludes numerical data. The practical framework of analysing the data falls under two steps: data management and the abstraction and interpretation process.

6.6.1 Data Management

At the analysis stage, it is essential to manage the high volume of raw data by organising it into meaningful categories. There are five common stages for data management: familiarisation with data; coding or constructing the initial themes; categorising the

saturated themes together; writing and grounding the theory (Corbin and Strauss 2014). In practise, to apply all these processes over all the data, I dealt with the data according to the collection method, which was by observation and interviews. Dividing the data into smaller sections simplifies the data and makes it easier to manage, organise, and analyse.

In this research, the data has been divided twice, with the first section naturally following from the two collection methods. The collected data then could be horizontally divided into four sections:

1- Ethnographical observation of the natural settings of the studio (notes transferred to written description).

2- Exploratory ethnographical observation for individual students while they were presenting their projects.

3- Semi-structured interviews with students (audio files transcribed into text format), then coded in tables.

4- Semi-structured interviews with educators (audio files transcribed into text format), then coded in tables.

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Figure 7: shows the horizontal and vertical division approaches.

The second dividing approach is a vertical division for each of the four sections, as shown in the figure below. Each of these has been divided depending on the themes from the data surrounding the focus of the observation and interviews.

Returning to data management, the following discussion of the five stages of data management were applied across the divisions illustrated above. The first stage was to become familiar with the data by reading it several times and getting a general idea of possible emerging themes. Re-reading the data in different orders revealed new connections and links, and presented the data from different perspectives.

In this research context, the data included observations and interviews across three institutions. Under each type of data there were two subsections. The ‘observation’ data constitutes two categories: general observations of the studio and observations of

individual students. The ‘interview’ section includes both student and educator interviews. Reading and becoming familiar with the data was the first step to analysing the data carefully and thoughtfully.

The second step was coding the data under specific themes related to the research subject. Coding is a controversial stage in qualitative research analysis. It has been described as potentially damaging to the qualitative data (Bazeley, 2013). However, coding is about defining the meaning of the data, labelling segments and scrutinising Ethnographical observation •Studio environment •projects and activities •Students and educators interaction Observations of indiviuals •Students' actions and behaviours •Personal attitudes •Interactions and language Semi-structured interveiw (students) •Suplementary questions •MBTI test

•Six Thinking Hats activity

Semi-structured interview (educators) •Perceptions of

creativity and design process

•Pedagogical practices and studio

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meanings of sentences (Corbin and Strauss, 2014). Charmaz who supports Constructive Grounded Theory states:

With Grounded theory coding, you move beyond concrete statements in the data to making analytic sense of stories, statements and observations. We aim to make an interpretative rendering that beings with coding and

illuminates studied life. (2014 p. 111).

Thus, coding was the initial step towards transforming the raw data into a coherent and meaningful theory.

In this research context, the coding phase led to three sets of coded data: studio

observation, student data, and educator data. The coding process sought terms, words or statements that revealed values, perceptions and meanings. For written texts, I chose one main verb or subject in line-by-line coding (Charmaz, 2014).

The third stage was categorising the data according to common themes and specific categories using the Word document format. I Gathered similar phrases and words under a thematic structure, reviewing, extracting and parsing the data constantly. Through this process, these themes became saturated within conceptual categories, in order to become the terms used in the analytical writing.

The fourth step was to review these conceptual categories, and the best way to do this was to follow the guidance of these questions:

 Does the thematic framework fit the data?

 Has the researcher subjectively cherry picked the data to manipulate it?  Is there lots of neglected data that is not being used properly?

 Is there coherence between themes and sub-themes or does the data become messy?

 Has the data become fragmented?

When the data had successfully passed these questions, the themes and the sub-themes were checked. Similar themes were joined together and if there were broad themes, these were split into more specific ones. After this, a reconceptualisation of these themes served

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to link them to the theoretical framework, the research aims or the body of knowledge (Charmaz, 2014; Corbin and Strauss, 2014).

The fifth step in the data management process was to summarise, synthesise and display the data.

This step is about joining all the previous steps in one thematic synthesised framework to summarise the findings. Thematic coding tables are highly recommended for organising data and displaying the links between themes, as well as to facilitate creative thinking in synthesising the data (Appendix 11). Summarising the themes across all data sets simplified the analysis procedures and discussions in the abstraction and interpretation stages. Although some texts recommended NVivo software, it could also be manually done in a Word document format, which I chose for this research. Each section of data has independent coding tables which later created multiple matrices to extract any correspondence between different themes (Bazeley, 2013; Charmaz, 2014; Flick, 2014). Finally, a summary was produced, completing the matrices framework, by reading each theme, preserving the language and terms, synthesising the data to provide the essence of the findings which constructed the grounded theory.

By creating these summarising tables, I gained a clearer vision of what the findings were, of the related literature to be discussed in the discussion section, and which theories could be generated in relation to the research aim and objectives. As a Grounded theorist, the data management phase can be seen as a jam factory, responsible for the whole process of transforming the actual fruit into jars of jam. By selecting the related data, excluding non-related data, and analysing each category in relation to the subject, I worked through a process to extract the essence of the data. In this case, the main theory generated was that each individual is in an entity with inner and outer motivations, each representing and reflecting on the other.

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In this phase, meanings and theories were extracted, and new contributions to the already existing body of knowledge were made. At this stage, the newly grounded theory is

presented and the final part of the story is unveiled, depending on the conceptual categories that emerged from the tables constructed in the previous stage. There are three steps for writing and interpreting the data: theoretical sampling, saturating theoretical categories, and theorising the grounded theory.

6.6.3 Theoretical Sampling

The original definition of theoretical sampling in the Classic Grounded Theory is: “the process of data collection for generating theory whereby the analyst jointly collects, codes and analyses his data and decides what data to collect next and where to find them, in order to develop his theory as it emerges (Glaser and Strauss, 1967 p.45)”. As Jenna Breckenridge (2009) explained the theoretical sampling of Charmaz’s description as “a means of focusing data collection and increasing the analytic abstraction of theory by illuminating variation and identifying gaps that require elaboration.” It is about “seeking pertinent data to develop your emerging theory. The purpose of theoretical sampling is to elaborate and refine the categories constituting your theory” (Charmaz, 2014 p. 193). In fact, there are different methodological strands for each approach.

Theoretical sampling purposely selects the sample for the research based on deductive primary research, and that does not conflict with principles of Grounded Theory

(Breckenridge, 2009). The purpose of choosing the studio for observation, and the students and educators for interviews was to collect rich data about interior design pedagogy and to understand the concept of creativity within this context.

6.6.4 Saturating Theoretical Categories

In Grounded Theory, ‘saturated data’ means the accumulation of repetitive data or

categories in order to generate a theory or phenomenon, and that even if a higher volume of data has been accumulated, the results will hold (Corbin and Strauss, 2014). In this

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section, it means that the focus is on the theoretical patterns that developed in the theoretical sampling stage. Glaser (2014) defined it as:

… the conceptualization of comparisons of these incidents which yield different properties of the pattern, until no new properties of the pattern emerge. This yields the conceptual density that when integrated into hypotheses make up the body of the generated Grounded theory with theoretical completeness” (Glaser, 2002 p. 191)

At this stage, the categories and content of the Grounded theory have been fully

constructed and no more categories or themes are expected to develop: “categories are ‘saturated’ when gathering fresh data no longer sparks new theoretical insights, nor reveals new properties of these core theoretical categories” (Charmaz, 2014 p. 213). According to Alan Bryman (2012), in qualitative Grounded Theory theoretical saturation is particularly challenging when it comes to sampling size, as it is hard to anticipate a

sample size that would deliver saturation. In this research, the collected data shows saturation under certain categories, with no new emergent categories. However, time constraints have also dictated that the sample size is limited to three institutions and this point will be discussed in the section on limitations of the study.

6.6.5 Establishing the Grounded Theory

Theorising is a core element in a Grounded Theory approach and having a good understanding of the term ‘theory’ is essential. In the social sciences, “[a] theory states relationships between abstract concepts and may aim for either explanation or

understanding” (Thornberg and Charmaz, 2014 p. 41). This definition shows that theories explain phenomena, describe situations, help people to be more knowledgeable and gain deeper understanding.

There is uncertainty about defining theory in Grounded Theory, and the reality of

constructing Grounded theories. This research takes the constructivist Grounded Theory approach, which has been described as one that “theorizes the interpretive work that research participants do, but also acknowledges that the resulting theory is an

interpretation. The theory depends on the researcher’s view; it does not and cannot stand outside of it” (Charmaz, 2014 p. 239). In fact, this epistemological theoretical framework

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that underpins the whole research means that individuals construct the meanings of the world they live in. The Grounded theory approach aims to collect all these individually constructed meanings and develop an original theory. Grounded theorists need to reflect on their own interpretations. Relativism is a theory of reality, insofar as meanings,

perceptions and values are strongly and relatively linked to actual settings constructed by the researcher. Subjectivity of data analysis is unavoidable, however, as long as it falls within the theoretical and logical framework and explanations, it is valid. There is reflexivity of all the processes in the data analysis, in order that the research theory be constructed. Validating the data by considering participants’ views is a critical step in controlling the subjective element.

6.7 Research Design

The research design draws the full picture of how the research has been carried out, from the first step of thinking and planning to the last phase of analysing and documenting. Theoretically, the following figure presented the approaches and theories used in each phase. The practical aspect (field work) will demonstrate the steps followed in conducting the research, and the discussion falls under two focal phases: data collection and data analysis. Figure 8 visualises the methodological process of conducting the research. The structure shows the holistic process and phases from the beginning to the end.

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Figure 8: Diagram of main theories and approaches chosen for the methodology. The Practical Research Design has two main phases, which are data collection and data analysis. As shown in figure 8, there are several steps in each phase.

Figure 9 A diagram showing the main two phases of the research fieldwork. Research data