Data collection and data analysis

Holistic case study examines the nature of one unit of analysis within the case.

Embedded case study involves more than one unit of analysis within the case.

In the case study research of the R5K project the two units of analysis are the project as a whole and the individual projects run by students within. The type of case study design is therefore the embedded case study as it is determined by multiple units of analysis.

The research onion is unpeeled further in the next section, next considering the time horizons and finally the data collection and analysis utilised in the study.

triangulation, following the recommendations of May and Pope (2000) and Yin (2003) as described above.

3.2.6.1 Population and sampling

3.2.6.1.1 Population

University students from the BTech: Industrial Design course at CPUT (2015) and alumni from the same course (2010-2014), as well as their employers, will be the focus of research activities in this study. The population will therefore be those who have participated in the R5K project as well as their employers. The unit of research is Industrial Design practice and the unit of analysis are the participants. This study is to identify what the R5K project has contributed to the learning experience of students and evaluate how effective it has been in preparing students for the working world.

3.2.6.1.2 Sampling technique

As mentioned the sample for this research is centred on the past and present R5K project participants, and stakeholders in industry who have employed them. This is a purposeful sample as these participants were considered most likely to provide contextually relevant and reliable data. The relevance of the participants is due to the fact that they participated in the R5K project at CPUT for the duration of their year of BTech study.

The sampling was made up of:

• An online survey of all 129 R5K participants since 2010

• 5 alumni of Industrial Design who participated in the R5K project

• 3 Industrial Designers who hired R5K alumni

• 3 R5K alumni who hire Industrial Designers

• 3 current groups comprising of 19 students

As this project has been running since 2010 with 129 participants to date, the data collection included a survey of all students and interviews with one alumnus per year of the R5K project as well as one member of each group of the 2015 R5K project. To corroborate and triangulate findings, interviews were also conducted with three employers of R5K alumni as well as three R5K alumni who hire other Industrial Designers.

3.2.6.2 Participant observation

From 1900 during the first generation of the case study method participant observation was the predominant method of data collection (Johansson, 2003, p.14), likely related to its origins in anthropological research. One of the hallmarks of case study research is its use of mixed methods including documents, videos, recordings, interviews, surveys or other qualitative data (Darke et al., 1998, p.276). As Baxter and Jack state “each data source is one piece of the puzzle with each contributing to the researcher’s understanding of the whole phenomena’ (Baxter & Jack, 2008, p.554). Participant observation is one method of data collection within the case study method.

In one respect the researcher acts as participant observer in that he is the coordinator of the course and project on which the research is based, thus providing him with direct inside perspective. This offers a privileged position in understanding the trajectories of the project and the students over the years, which assists in contextualising the data from other methods employed. For example, the descriptions of the R5K projects draw heavily on his personal experience. However, to avoid the potential pitfall of subjective bias, the data from other structured methods is allowed to speak for itself.

In other respects the researcher is a participant observer within research methods such as the thematic analysis workshop. Here the researcher’s role will be to facilitate the workshop in such a way that encouraged the student participants to surface their insights without the researcher influencing the outcomes. Although the researcher will be present in the workshop is has been clearly stated that he is on

study leave and has no sway on the participating students marks or project outcomes.

3.2.6.3 Surveys and Questionnaires

Baxter and Jack emphasise the importance of survey data as it “facilitates reaching a holistic understanding of the phenomena have being studied” (Baxter & Jack, 2008, p.554). Surveys must be designed to avoid the potential bias of survey participants not being sufficiently diverse (Leedy, 1997, p.219).

Survey sampling is the process of choosing, from a much larger population, a group about which we wish to make generalised statements so that its selected parts will represent the total group (Leedy, 1997, p.211).

Another danger to avoid with surveys is the possible lack of depth due to subjects’

limited understandings of the subject or ability to deliver their understanding of the subject (Mouton, 2009, p.153). In the case of this study all participants of the survey have been participants in the R5K project, they all therefore have a intimate understanding of the project.

In this study, an online survey questionnaire was designed and sent to all past and current R5K project participants to assess their experience of the project and of their experience since then in industry.

3.2.6.4 Interviews

Interviews seek to find common threads of viewpoints from multiple participants.

Interviews can be structured or semi-structured, a structured interview contains a set of closed and predetermined questions similar to that of a questionnaire while a semi- structured format allows for probing questions to gain more clarity after the set questions are asked (Leedy, 1997, p.199).

In this study, interviews were designed after the survey responses were received, so that the findings thereof could be further explored in more depth. A semi-structured

interview schedule was designed, and the researcher met with each respondent face to face to conduct the interview.

3.2.6.5 Thematic Analysis

According to Braun and Clarke (2006) thematic analysis is a qualitative analytic method for identifying, analysing and reporting themes within data. It describes data in detail often from creative angles. Patterns within data describe and show important meaning that emerges about the subject of enquiry (Braun & Clarke, 2006, p.82).

The aim of a thematic analysis approach is to capture something important about the data which can be shown as a theme within the data by participants who have direct knowledge about the data.

Braun and Clarke suggest the following guide when conducting thematic analysis (Braun & Clarke, 2006, p.16):

1 Becoming familiar with the data 2 Generating initial codes

3 Searching for themes 4 Reviewing themes

5 Defining and naming themes 6 Producing the report.

In this study, a participatory thematic analysis workshop was facilitated with representatives of the 2015 R5K groups, in which they were invited to review the data of the survey and interviews, and identify themes in the patterns of data. This step in the research thus comprises an overlap between data collection and analysis, as the workshop participants’ views reveal additional data about their experience, while beginning to make sense of the significance of the data.

The qualitative data gathering will be subject to factor and content analysis. All data will be collated and aggregated, themes will be interrogated through a process of appreciative inquiry with current R5K students.