Data Analysis and Interpretation

Data analysis in this study was descriptive, that is, conducted according to an analytic system that emerged from the research questions and data, rather than any existing set of categories or codes. The main conceptual task in understanding the data occurs at the data processing and analysis stage. This is particularly problematic for research utilizing a naturalistic framework. Miles (1979 in Lincoln and Guba, 1985) aptly characterizes the challenges thus:

Qualitative data tend to overload the researcher badly at almost every point: the sheer range of phenomena to be observed, the recorded volume of notes, the time required for write-up, coding and analysis can be overwhelming. But the most serious and central difficulty in the use of qualitative data is that methods of analysis are not well formulated. For quantitative analysis there are a number of clear conventions the researcher can use. But the analyst faced with a bank of qualitative data has very few guidelines for protections against self-delusion, let alone the presentation of ‘unreliable’ or ‘invalid’ conclusions to scientific or policy-making audiences. How can we be sure that an ‘earthy’, ‘undeniable’ or ‘serendipitous’ finding is not, in fact, wrong? (p. 354)

In an attempt to overcome these obstacles, the present study utilized multiple complementary approaches aimed at deriving meaning from the collected data. Documentary evidence in the form of curriculum materials was analysed using the Sussex Scheme for Analysis of Curriculum Materials (see Appendix 3). The quantitative data from the pupil assessment was analysed with Statistical Package for the Social Sciences (SPSS) software, while the qualitative data from the observation and interviews were analysed descriptively.

The NEA assessment data was entered into SPSS 16.0 version with answers labelled as ‘A’ - 1, ‘B’-2, ‘C’-3, ‘D’-4. Syntax was developed to execute the total scores and run frequencies and answers re-coded to 1 and 0. All correct answers were coded as 1 and the others as zero. Missing values were coded as 99. In computing the score, the total score was divided by total number of questions and multiplied by 100 as a percentage. Competency level was determined by using the NEA minimum competency score of 35 and proficiency score of 55 (for competency levels 1 and 2).

Comp 1 < 35 Comp 2 < 55

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4.9.1. Qualitative Data Analysis

The qualitative data analysis was continuous and cyclical. Analysis happened as the research was taking place as well as afterwards. This is because in qualitative data collection, there is the need to keep going back and forth between the research instrument, data collected and data participants in order to document emerging understandings, and revise the instrument in light of them. First, field notes taken during classroom observations were expanded by listening to audio-recordings of lessons. Details omitted from the notes were filled in and any incidents pertaining to teacher effectiveness were also noted at this stage. This process resulted in more detailed written records than the original notes.

Next, from these expanded field notes, original analytic categories were amended so that they more closely reflected what was captured by lesson observations. Repeated occurrences of significant events across classes, or those that differed greatly from place to place but which were not included in the original concepts, were noted, and new categories were constructed to accommodate these incidents. For instance the classroom observation instrument utilised categories for teacher effectiveness including lesson preparation, interaction with learners in lesson delivery and teachers’ mastery over curriculum content. Given the social constructivist framework within which this research was being undertaken, it was critical to explore how lessons link up with the culture of the people and whether teachers made any effort to build on the experiences of the learners. Thus the original categories were expanded to accommodate the social constructivist framework of the research.

Last, the audio-recordings were listened to again to note the duration or count the occurrences of relevant incidents for analysis. In writing up the results, descriptions of activities could be retrieved from the field notes and the relevant segments of audio-recordings could be traced when illustrations were required.

Lincoln and Guba (1985) present a useful discussion of data analysis approaches based on a continuum elaborated by Goetz and LeCompte (1981) to represent the principal methods of processing qualitative data. This continuum illustrates the degree of convergence between general naturalistic inquiry and ethnography that is particularly evident in the domain of data analysis. The somewhat overlapping ranges on the continuum are inductive/deductive, generative/verificatory, constructive/enumerative, and subjective/objective (Goetz and LeCompte 1981).

86 As should be evident from the discussion so far in this chapter, this study is positioned towards the inductive/generative/constructivist/subjective pole. In other words, categories were not only taken from existing theory but developed from analysis of lesson observation notes, and then triangulated with interview and other data. I saw my research task as the identification and linking of constructs that might explain phenomena associated with classroom activity on the parts of learners and teachers. Thus the analysis emerged not only from the focus of the study and the data collection instruments utilized, but also from the perceptions and perspectives of the participants (including me, the researcher).

In the case of interviews, data obtained from the various participants was analysed using a combination of narrative, descriptive and interpretive approaches. The narrative approach treated the interview data as a story and situated account, and as an outcome of the application of the conceptual framework of the study, which is based on a constructivist view of learning, to participants’ stories and experience of events (Jephcote et al., 2004). The interpretative approach allowed the researcher to “see and treat social actions and human activities as texts or as a collection of symbols expressing layers of meaning” (Berg, 2004: 266).

4.10. Methodological Issues and Challenges