Data analysis – using the Morse and Field approach

Qualitative research creates rich amounts of data, which need to be systematically analysed in a logic fashion (De Vos et al., 2011:397). De Vos, Strydom, Fouche and Delport (2005:333) argue that data analysis creates order, structure and gives meaning to masses of collected data. On a practical level, Bogdan and Biklen (1998:106) state that data analysis involves the techniques a researcher can use to make sense out of and learn from many pages of recorded statements and behaviour from transcripts and field notes. Wilson (1998:3) adds that the researcher is concerned with the “process”, and how and why things happen the way they do and then distilling the meaning of the observation.

Various approaches for qualitative analysis have been documented and include:

Guba and Lincoln’s constant comparative approach, Huberman and Mile’s approach, Marshall and Rossman’s approach, Tesch’s approach, and Morse and Field’s approach (De Vos et al., 2002:338-343). These analysis approaches are briefly explained in Table 5.4.

Table 5.4: Qualitative data analysis approaches

Analysis approach Description

Guba and Lincoln’s constant comparative approach

Consists of a continuous developing process involving four steps so as to derive theory and not purely for data processing.

Huberman and Mile’s approach Consists of three linked data analysis steps, namely data reduction, data display and drawing conclusions in order to verify them.

Marshall and Rossman’s approach Consists of four steps and suggests that the researcher should be empowered to discover any new / surprising dimensions by generating categories and testing them.

Tesch’s approach Consists of eight steps where the researcher focuses on carefully reading through all transcripts to identify a list of topics which are then clustered into categories whereafter all data is assembled into categories.

Morse and Field’s approach Consists of a process of four steps where data is fitted together to make the “invisible” obvious and linking their consequences.

Source: Adapted from De Vos et al. (2002:338-343).

De Vos et al. (2002:344) observe that all these methods require the researcher to logically account for data analysis steps taken, and that the final conclusions are based on collected data. The Morse and Field (1996) approach was chosen for this research study because it was proven to be a successful method of analysis in academic work of management and marketing disciplines (Botha, 2009;

Niemann, 2005).

The essence of the Morse and Field approach is that theory cannot emerge from data without the researcher immersing and familiarising him / herself with the data or without active intellectual work (Morse & Field, 1996:103). Morse and Field (1996:103) further add that the researcher should engage in solid and creative data analysis, which requires intelligent questioning, a persistent search for answers, active observation and truthful recall. This approach has four steps where the researcher seeks to achieve comprehension (step 1) and when saturation is reached, data patterns are categorised according to thematic meanings (step 2) to form a theory (step 3) which is then placed in context of

established knowledge (step 4). These steps happen almost sequentially (Morse

& Field, 1996:103) and each of these steps is discussed in greater detail in the following sections. The sections also report on how the researcher applied each step of the Morse and Field approach during data analysis of the empirical study.

5.5.3.1 Step 1: Comprehend

As soon as data collection begins, preparation for data analysis begins through the transcribing, checking, correcting and coding of interviews and field notes (De Vos, 1998:341). The researcher achieves comprehension when sufficient data is available to write a complete and detailed account of all information collected (Morse & Field, 1996:104). A central process to achieve comprehension is coding, which enables data sorting and uncovering underlying meanings in the text (Morse & Field, 1996:104). De Vos (1998:335) reasons that coding requires the researcher to identify persistent words and phrases within the data. To achieve this, Morse and Field (1996:104) state that line-by-line analysis of an interview transcript best facilitates this process. When comprehension is reached, the researcher should be in a position to identify patterns of experience relevant to the topic and should consequently be able to make predictions about the probable outcome (Burden & Roodt, 2007:15; De Vos, 1998:341). Saturation is reached and comprehension completed when the researcher is familiar with the data and when no new patterns emerge in the research results (De Vos, 1998:341; Morse & Field, 1995:127).    

Comprehension of the theory and concepts were reached through the literature review in Chapters 2 to 4. In terms of the empirical study, comprehension was reached post-interviews and after the researcher had gone through all the transcripts numerous times. Line-by-line analysis of each transcript was done and responses were coded in the right margin of every page. When the researcher was familiar with all the data and established that no new patterns emerged, comprehension was reached.

5.5.3.2 Step 2: Synthesise

De Vos (1998:341) together with Morse and Field (1996:105) describe this step of the process as “sifting”, and this occurs when the researcher is familiar with the

research setting. In other words, the researcher has the confidence to describe and give examples of the topic without referring to notes (Morse & Field, 1996:105). Patterns are categorised and transformed into a story that makes coherent sense (Morse & Richards, 2002:131; Corbin & Strauss, 1990:63). Morse and Field (1996:105) suggest there are two main ways to achieve this:

• Interparticipant analysis: This takes place where the researcher compares transcripts across participants to look for similarities and differences.

• Category analysis: This occurs when the data is then sorted by commonalities from all participants.

Identified interpretation categories from the category analysis then act as baskets into which texts are placed which are consistent, but distinct from one another (Marshall & Rossman, 1998:154). In essence, synthesising should help the researcher to interpret, link, see relationships, estimate and to verify findings (Morse & Field, 1996:105).    

During the synthesising step of the empirical study, three interpretation categories and subcategories were identified based on the in-depth interviews. These are illustrated in Table 5.5.

Table 5.5: Interpretation categories and subcategories

Interpretation subcategory

Participants’ answers to the semi-structured questions of the interview guide were grouped into these interpretation categories and subcategories. Specifically, the researcher transferred participants’ responses to these interpretation categories into an Excel spreadsheet where colour codes were used for coding as identified in the comprehension step (refer to Appendix G). In other words, each response was placed into these interpretation categories to reflect similarities and differences between participants.

5.5.3.3 Step 3: Theorise

In step 3, the data should be sorted, implying that alternative explanations should be selected to compare the data until the best explanation that describes the data, is found (Morse & Field, 1996:105). The researcher should remain open to different explanations, codes and interpretation categories until maturity is reached through comprehending and synthesising (De Vos, 1998:341). During this process, a theory emerges from the data when achieved results are compared and linked to original propositions (Niemann, 2005:200; Morse & Field, 1995:125). The “solution” or best theory is the one that provides the best comprehensive, coherent, simple and useful explanation for linking diverse and unrelated facts together (Morse & Field, 1996:106).   During this process, the researcher used the literature review (Chapters 2 to 4) and the gathered information to theorise about the use of data visualisation and storytelling in quantitative research reports.

5.5.3.4 Step 4: Recontextualise

De Vos (1998:342) states that at this stage the theory is developed to be applicable to other settings and other populations. The published work of other researchers and literature plays an integral part so as to provide the context in which the researcher links the research results (of the interpretation categories) to the literature (Morse & Field, 1996:106). Ultimately, the categories and research results are combined in such a way that it becomes obvious how they are supported by theoretical models (De Vos, 1998:271). Therefore in this step, the results are placed in context of the established knowledge to identify where it supports the literature or where it could claim unique contributions (De Vos, 1998:342). In this step, the researcher built on the theories identified in step 3

(refer to section 5.5.3.3) so as to conclude whether data visualisation and / or storytelling is used strategically in quantitative research reports (presented in Chapter 7).