Qualitative Data Analysis (QDA) is the range of processes and procedures whereby we move from the qualitative data that has been collected into some form of explanation, understanding or interpretation of the people and situations we are investigating. QDA is usually based on an interpretative philosophy. The idea is to examine the meaningful and symbolic content of qualitative data (Ann Lewins, 2010; Coffey, Holbrook, & Atkinson, 1996; Seidel & Kelle, 1995; C. Taylor & Gibbs, 2010). Creswell (2011) mentioned that the qualitative researchers first collect data and then prepare it for data analysis. This analysis initially consists of developing a general sense of the data, and then coding description and themes about the central phenomenon (J. W. Creswell, 2011b) . Figure 3.3 shows the qualitative process of data analysis.
Figure 3.3: Qualitative Process of Data Analysis (J. W. Creswell, 2011b)
After every interview session, the researcher wrote down comments as a pre-analysis of the interview, a process called prompt analysis. Prompt analysis was undertaken because we thought of the existing data (the completed interviews) when iterating the same questions as we conducted the new interview sessions. Miles and Huberman (1994) emphasized that this procedure is part of prompt analysis (Miles & Huberman, 1994). This approach enables us to focus on the new points and skip the less significant issues in order to save time in the new interviews. This iteration also facilitates pre- defining codes, which are used to analyse the interviews in the future. The semi- structured interviews are flexible in design. Thus, we modify the interview questions while conducting new interviews iteratively.
The transcribed materials consisted of only seven interviews from six specialist physicians and one medical informatics expert. The researcher selected only seven interviews because of data saturation (Alan Bryman, 2008; Fontanella, Ricas, & Turato,
2008). The materials comprised 101 pages, 73,034 words, and approximately 12.30 hours of audio recording. Approval was obtained from the Research Ethics Committee of Health before any information was gathered from the participants (see section 3.2.7). Harteny (2012) mentioned that the data analysis cannot be automatically performed. Humans have both domain expertise and the uniquely human capabilities of organization, breakdown, creation, generalization, induction, intention, inference, deduction, thought, and rationalization. These abilities can be applied to data to acquire information and knowledge. Moreover, these tools can facilitate the analysis of the obtained data (Hartney, 2012). Miles and Huberman (1994) suggested a number of ways that utilize computer software to aid qualitative research as shown in Table 3.2.
Table 3.3: Uses of Computer Software in Qualitative Studies (Miles & Huberman, 1994)
Use of Computer software in qualitative studies a Making note in the field.
b Writing up or transcribing field notes.
c Editing: correcting, extending or revising field notes.
d Coding: attaching key words or tags to segments of text to permit later retrieval.
e Storage: keeping text in an organised database.
f Search and retrieval: locating relevant segments of text and making them available for inspection.
g Data “linking”: connecting relevant data segments with each other, forming categories, clusters or networks of information.
h Memoing: writing reflective commentaries on some aspect of the data, as a basic for deeper analysis.
i Content analysis: counting frequencies, sequence or location of words and phrases.
j Data display: placing selected or reduced data in a condensed, organised format, such as a matrix or network, for inspection.
K Conclusion drawing and verification: aiding the analyst to interpret displayed data and to test or confirm findings.
l Theory building: developing systematic, conceptually coherent explanations of Findings: testing hypotheses.
m Graphic mapping: creating diagrams that depict findings or theories. n Preparing interim and final reports.
According to Miles and Huberman (1994), qualitative data can be divided into three activity flows, namely, data reduction, data display, and conclusion drawing/verification. These three activities also show each of the themes in greater depth. Data reduction is a process of selecting, focusing, simplifying, abstracting, and transforming the data that appears in written field notes or transcriptions.
The transcribed materials were stored in digital format. Then, the researcher edited the data by checking the spelling, recovering the missing words, and correcting the errors with the assistance of a native English speaker. The transcription process was followed by the subsequent data reduction in an Excel format. In this context, the issues were classified based on the codes of the participants. Johnson and Christensen (2008) defined coding as marking the segments of data with symbols, descriptive words, or category names (Johnson & Christensen, 2008). The researcher followed the coding manual as mentioned in Saldaña (2012). Coding is just one way of analysing qualitative data (Johnson & Christensen, 2008). Figure 3.4 shows the procedures of the qualitative data analysis (Saldaña, 2012). The researcher began to analyse the textual data by grouping quotes under the predefined codes. Unsurprisingly, more data and information were discovered in the transcripts. However, as a rule of thumb for developing coding schemes, no coding will ever be perfect (Willms & Johnson, 1993), and not every piece of the note must be coded (Miles & Huberman, 1994). Hence, we limited the coding to build a balance between covering adequate details to contribute to our research and avoiding excessive details on a particular IS. Analysis of the interviews enabled the modification of the additional codes that appeared.
Figure 3.4: Procedures of the Qualitative Data Analysing (Saldaña, 2012)
Meanwhile, the researcher used the Statistical Package for Social Sciences (SPSS) to analyse the demographic data of the participant. The SPSS program provides a wide range of statistical analyses to obtain the most accurate responses for different data types. This study uses SPSS version 18.0 to analyse data, specifically for the descriptive analysis, testing the differences, and measuring associations results (Carver & Nash, 2011; Pallant, 2010).