Data Analysis Procedure

This particular section explores data analysis techniques that were employed in the study. It is argued that qualitative research methods ―produce a lot of data and the researcher has to impose some form of order onto the data if it is to make any sense and contribute to our understanding of the research problem‖ (Deem, 2002:846). This suggests that data collected using qualitative methods should be organised for it to make sense since it is ―mixed up‖. It is argued that making sense out of qualitative data is achieved through data analysis. A range of analytical strategies (Cohen et al., 2000: 294-295) was employed to ensure in depth insights through critical engagement with the emerging discourse. Miles and Huberman (1994) outline the common features of qualitative data analysis as coding of field notes, noting reflections of other remarks in margins, sorting and sifting through materials to identify similar phrases, relationships and common consequences, isolating patterns, and processes, commonalities and differences and taking them back to the field in the next round of data collection; gradually elaborating a small set of generalisations that cover consistencies discerned in the database and confronting those generalisations with a finalised body of knowledge in the form of constraints and theories. For purposes of analysing data in this study, these ideas were used to assist the researcher to code the raw data from interviews and focus group discussions in order to come up with data sets. For a start, data was scrutinised for ―patterns of choice‖ which identify the frequency with the themes from the literature.

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For the treatment of more elaborative narratives such as the data from interviews, focus groups and life histories, Johnson (1998) suggests a different approach since the data is more bulky and contains more essential details. In addition, the data lacks any order needed for analysis since participants may jump from one topic to another and vice- versa.

Analysis of data is inescapably a selective process (Miles and Huberman, 1994:55) with coding and classifying being the means by which such selection and data reduction can be effected. Consequently the data was ordered and reduced. Ordering was done in terms of the objectives and the research questions of the study (Ryan, 2003). In ordering and reducing data from elaborate narratives, the following steps suggested by Ryan (2003) were used:

1. Re-reading the research‘s objectives and research questions.

2. Carefully reading a number of interviews, focus group discussions or narratives that will be processed. Markers used to highlight particular remarks. Also margins to define topics will be used.

3. Key words that belong to a certain topic in the sub-categories that have been developed under and above will be listed, then all qualitative was data coded in this way (Ryan, 2003:6).

Data collected from focus groups and interviews were treated to this approach. A model suggested by Wainwright (1997) summarises a common approach to qualitative data analysis adopted in treating data in this study:

Figure 3.1 Wainwright’s (1997) model to qualitative data analysis

Field

(Adopted from Wainwright, 1997:12)

The above model practically demonstrates that discovering themes is at the heart of qualitative data analysis.

Make up or cut up data Construct outline re- sequence Field Notes Search for categories and patterns (themes)

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Ryan (2000) denotes themes as abstract, often fuzzy, constructs which investigators can identify before, during and after data collection (Ryan, 2000:2). On the other hand Johnson (1998) identified two sources of themes as deductive and inductive sources. Deductive codes are themes that are developed before examining the current data. These are referred to as ―priori codes‖ (Johnson, 1998:36). In other words, the researcher decides to use a set of already existing codes or themes for his data. Sources of such themes are review of the literature, the characteristics of the topic, common sense constructs, or the researcher‘s values, the theoretical orientation and personal experiences with the subject matter (Strauss and Quinn, 1997). In short, these are categories or themes that emerge from the theories or the literature one uses. The positivist research paradigms discussed earlier in this chapter (section 3.2) are compatible with the deductive codes.

On the other hand, inductive codes or themes, according to Johnson (1998:4), are ―developed by the researcher through the direct examination of the data‖. In the social sciences, researchers in the qualitative tradition infer themes from the data and describe it as ―open coding‖ (Ryan, 2003:2). Emerging themes will be examined and compared to the theoretical framework. This approach, according to Cohen et al. (2000:295), ―transcends the rather artificial boundaries which the items themselves imply‖. Since the research project falls in the qualitative tradition it is compatible to inductively identify themes from the data.

According to Ryan (2003), a number of techniques exist which may be used in identifying themes in qualitative data. First there is the word based technique (such as noting word repetitions) which involves an analysis of key words in the text. It is less labour intensive and can be used with complex texts such as the words of Shakespeare or the Bible as well as with simple short answers to open ended questionnaires. Another technique is referred to by Ryan (2003) as the pawing through the data approach where investigators identify all text passages that are related to a major theme cut them out and sort them into sub thematic categories. This approach is highly recommended for identifying major themes.

Yet another technique identified by Ryan (2003) is the intentional analysis of linguistic features of the data. These may include metaphors, transitions or connectors. Last but

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not least, there is the careful reading of larger blocks of text where the researcher may compare and contrast, look for social science queries or search for missing information.

Hills (2003:1) suggest a sequence to be followed in analysis of data. The first step he suggests is to prepare data for analysis. This implies identification of data which has been collected for each research question. The second step involves referring to the research questions. In other words, addressing the aims of the study as well as the issues involved. The third and final step refers to a review of literature. The question is who said what about the research questions and whose work is relevant, contradicting or matching will be explored. The table adopted from Hills (2003) furnished below reflects the sequence discussed above.

Table 3.2 Hills’ (2003) sequence of handling qualitative data analysis.

Table Questions to guide the analysis process

Prepare data for analysis What data has been collected for each research question or objective?

Go back to research questions What did the study aim to do? What are the issues involved?

Go back to literature review Who said what about your research focus? Whose work seems most important?

Does your data seem to match/ contradict the work of others?

(Adapted from Hills 2003:1)

The above table again attempts to show the sequence that was followed in the analysis of data in discovering answers to different successive questions.

The above discussion has described treatment of data from elaborative narratives such as interviews and focus group discussions. Treatment of data from open-ended questions is attempted below.

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According to Johnson (1998), there are several slightly different steps to be followed in the analysis of answers to open-ended questions as compared to the analysis of more elaborative narratives.

The first step in treating answers to open-ended questions is to list the answers as they are provided and then reading the answers carefully line by line remembering the purpose of the question. In the process of reading, rough categories of answers that seem to belong together is made and these are coded. As a second step, all the answers were listed again but that was per code so that the researcher got a short list. Themes were inferred from all the answers and these were finally cut and pasted according to identified themes. This study used constant comparative analysis (Carey 1995:491) Morgan 1993:116). Notes from the interview transcripts and additional notes from the field journal were coded. In this study coding process was done by going through the interview transcripts and attributing a code to sentences and paragraphs. These codes represented an idea or theme with which each part of the data was associated. These codes were then written next to the relevant section of the transcript. After coding the transcript, the document was then highlighted, cut and pasted. The name of the participant who was interviewed, the code pertained to and the line numbers from the transcript were included in each coded section. This approach assisted in locating information to the original to provide additional contextual details. The quality of data analysis depends on repeated, systematic searching of the data (Hammersley 1981). In attempt to achieve this, repeated coding was performed to receive interpretations, in the light of new data gathered, until no new insights were being gleaned (Riley 1990). In addition, member checking was done in each case study institution. Once coding is completed, the codes that need common elements were merged to form categories (Strauss and Corbin 1990). The categories were then clustered around each research question which the categories contributed to addressing or answering. A list was then compiled of categories that related to each research questions. Once all the research questions had been allotted input from the categories, the information pertaining to each question was examined and reviewed to compile a report.

However, there are challenges associated with the analysis of qualitative data. Hills (2003) outlines the following challenges: the volume of data, data collected may vary

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in relevance; no simple facts and figures and last but not least the need to identify themes/ patterns in order to develop analysis.

While qualitative study approaches lead to findings which are limited in terms of generalizability, the case study does provide an opportunity to engage in a discourse about academic professional development in higher education. Academics were given the opportunity for their voices to be heard and in the process add to the increasing body of knowledge about educational development through further illumination and interpretation of collected data. Although qualitative data can be limited in terms of generalizability, its trustworthiness should be guarded through credible measures. The question of trustworthiness of findings is discussed below.

3.13 TRUSTWORTHINESS

Issues of validity and reliability concern qualitative researchers as they do with

quantitative researchers. However, under qualitative research the question of validity takes on a different meaning. This arises since, through qualitative research knowledge and knowledge construction, involves the views of the researcher and that of the researched (Unisa Learning connection, 2003). In the reconceptualization of validity in qualitative research, researchers are challenged to demonstrate that they have been rigorous and ethical in conducting their research. In this study participants were requested to sign the ethical clearance certificate (Appendix13) to signal their consent. Lincoln and Guba (1985) point out that while positivists talk about validity and reliability, naturalist inquiry is concerned about trustworthiness which includes credibility, transferability, dependability and confirmability. In this study, some of the measures taken to enhance legitimacy and rigour have been discussed.

3.14 CREDIBILITY

Hoepfl (1997:13) points out that one way to heighten credibility in case studies involves ―making segments of the raw data available for others to analyse and also use of ―member checks‖ in which respondents are asked to corroborate findings. In order to facilitate corroboration, audio taped interviews and focus group interviews of the study as well as typed transcripts of raw data including sample of answered questionnaires