DATA ANALYSIS

After data is collected, the researcher has a responsibility to make sense of the collected data. According to Mertens, Pugliese and Recker (2017:1) “data analysis is an iterative process of manipulating and interpreting numbers to extract meaning from them answer research questions, test hypotheses, or explore meanings that can be derived inductively from the data”. However, this definition by Mertens et al. (2017) seems to be biased toward quantitative data analysis as emphasis is on numbers being manipulated. In a mixed-methods study, the researcher has the option to choose the type of analysis to use. The analysis is dependent on the design used in the study. According to Osborne (2008:131), “Mixed methods data analysis includes parallel mixed analysis, concurrent mixed analysis, and sequential mixed analysis”. Creswell (2014a) says the type of mixed design chosen determines the type of analysis used. He names the types of designs used in mixed methods studies as convergent parallel mixed methods; explanatory sequential mixed methods; exploratory sequential mixed methods; embedded mixed methods; transformative mixed methods; and multiphase mixed methods. Data analysis follows the design chosen. This study adopted the convergent mixed methods design. The convergent parallel design of mixed methods approach meant that both qualitative and quantitative data was collected side by side, thus while questionnaires were being completed by some teachers, teacher observations were conducted with others at the same school. Interviews were also conducted during the same period. According to Creswell (2014: n.p.), the researcher using convergent parallel design “collects both quantitative and qualitative data, analyses them separately, and then compares the results to see if the findings confirm or disconfirm each other”.

4.9.1 Quantitative Data Analysis

Quantitative data was analysed separately using the SPSS. The last set of data collected was the questionnaires. This is because they formed the largest sample and covered a larger geographical area of three provinces. According to Hendricks (2011), data analysis begins with the recognition of variables. In a general sense, the term variable describes anything that changes. However, Mertens et al. (2017:1) state that:

exploration is the first step of any data analysis: we run a few basic manipulations and tests to summarise the data in meaningful statistics, such as means and standard deviations; we visualize the data; we try to improve our understanding of the information in the data

However, Gosh (2015: 261) says “the first step in the analysis of data is a critical examination of the assembled data”. The variations in steps the scholars suggest seem to emanate from the different understanding of what analysis is, when it begins and when it ends. Some scholars actually contend that analysis starts as soon as data collection begins and goes on until overall sense of the data made at completion of analysis. Ghosh (2015) describes the order in the analysis of results as categorisation, coding, tabulation and statistical analysis and inference. As was observed in the versions of steps in a qualitative study, so is the situation in a quantitative study. Analysis in this study started with the identification of variables for entry into SPSS. This study dealt with nominal and ordinal data, which are nonmetric measurements (Jupp 2006). According to Murphy, Myors and Wolach (2014:43), “nonparametric test statistics do not require a priori assumptions about distributional forms, and tend to use little information about the observed distribution of data in constructing statistical tests”.

After collecting all the questionnaires, the researcher went through each questionnaire and checked the completion rates by respondents and ascertained that most questions were fully answered. The researcher then organised the questionnaires according to provinces and numbered them serially in readiness for entry into SPSS. The researcher decided to enter the questionnaires province by province for easy checking and possible corrections. Those whose variables were entered were labelled ‘entered’ and signed to avoid re-entry. Not all questions in the questionnaires were coded in SPSS because some of the questions were open-ended, used to validate the ‘yes’ and ‘no’ questions. For instance, there were questions that asked, “if your answer to the previous question is ‘Yes’, explain ways in which you were trained”. Some of such questions were transformed into quantitative codes and analysed quantitatively while others were analysed qualitatively by use of NVIVO software.

After coding the quantitative data, the researcher ran case summary reports to verify the total entries and see the excluded cases so as to determine their impact on overall analysis. Running summary reports in SPSS also helped the researcher to review the questionnaires and verify wrong entries and unassigned entries. Quantitative data was subjected to the SPSS in order to derive meaningful descriptive representations in terms of tables, percentages,

means, standard deviations, graphs, significant differences, and correlations among data. Sidhu (2014), Kombo and Tromp (2013) and Mukherji and Albon (2015) acknowledge the use of SPSS as a quantitative data analysis tool. The researcher used non-parametric tests in SPSS to try and obtain the meaning of the data from different angles in order to get the consistence of data. The Chi-square test of goodness of fit and bivariate correlations was run to establish significant differences and correlations among data. There was an advantage in using non-parametric tests for this study. Murphy et al. (2014:43), believe that “nonparametric tests can have more power than their parametric equivalents under a variety of circumstances especially when conducting tests using distributions with heavy tails (i.e. more extreme scores than would be expected in a normal distribution)”. It is because of such presumed power that this study used nonparametric tests to help easily generalise data from 120 respondents, thereby reducing high error occurrences during analysis. In analysing the data, cross tabulations helped to cross-check data relationships with their sources. Further, Pearson’s correlation coefficient and bivariate relationships were run to obtain differences and relations between the variables.

4.9.2 Qualitative Data Analysis

The data collected in this study was subjected to rigorous analysis in order to obtain the desired meaning from which conclusions were drawn. Flick (2013: 5) defines qualitative data analysis as “the classification and interpretation of linguistic (or visual) material to make statements about implicit and explicit dimensions and structures of meaning-making in the material and what is represented in it.” The researcher considered the design adopted to guide this study. In this study, the researcher analysed part of the qualitative data first because most of this data was ready before the quantitative data. Several scholars propose stages of data analysis with slight differences in the order. For instance, Table 4.3 below provides an illustration of three approaches to qualitative data analysis.

Table 4.3

Stages in qualitative data analysis

Step Murkheji (2015) Lodico, et al. (2006) Creswell (2014a) 1 Becoming familiar with

data

Preparation and organising of data

Organising and preparing for data analysis (transcribing, typing field notes, cataloguing, sorting arranging data into different types)

2 Coding the data Reviewing and exploring the data

Read and look at all the data (getting general ideas)

3 Categorising the codes Coding data into categories Start coding of the data 4 Identifying themes and

relationships among the codes

Constructing descriptions of people, places and activities

Use the coding process to generate a description of the setting or people as well as categories or themes for analysis

5 Developing concepts and arriving at generalised statements

Building themes and testing hypotheses

Advance how the description and themes will be represented in the qualitative narrative

6 Reporting and interpreting data Making an interpretation in qualitative research of the findings or results One advantage of qualitative data is that the researcher starts analysis right at the point when data is being collected, at the time of the interviews or discussions with the respondents. The researcher in this study started familiarising himself with the data from the point of collection. All interview data and post lesson discussions with teachers were recorded on an MP3 audio device. After every interview and post lesson discussion, the researcher listened to the audio recordings and made brief notes in his note book. The brief notes highlighted general ideas that came from respondents. The researcher also transcribed the audio recordings, thus becoming even more familiar with the data collected and helping to reflect on the interviews and post lesson discussions had revealed. This practice is recognised as applicable by Creswell (2014:n. p.) saying:

data analysis in qualitative research will proceed hand-in-hand with other parts of developing a study namely, the data collection and write-up of findings. While interviews are going on, for example, researchers may be analysing an interview collected earlier, writing memos that may ultimately be included as a narrative in the final report and organising the structure of the final report.

This enabled the researcher, especially that he used unstructured interviews to be able to improve his interviewing techniques and collect even richer data in subsequent interviews and post lesson discussion. At the completion of transcription, the researcher made a print-out

of 243 pages of transcribed interviews and post-lesson discussions. The data was subjected to rigorous reading and re-reading to ensure little or nothing relevant was missed out during analysis. Lodico et al. (2006:305) observe that “qualitative researchers should continually read, reread, and reexamine all of their data to make sure that they have not missed something or coded them in a way that is inappropriate to the experiences of the participants”. The researcher went through each script to edit spellings, removing names of respondents and places for ethical reasons and highlighting key concepts that emerged from the interviews. (The verbatim records needed to remain without alteration). This was to prepare the data for coding.

After making corrections on the printed transcripts, the researcher made corrections on soft copies, named and ordered the transcripts. The transcripts were then separated according to their categories: interviews for ESOs (N = 12); interviews for CS (N = 2); and post-lesson discussions (N = 12). Codes were allocated to the interviewees as follows: ESO1 – ESO12; CS1, CS2; and TR1 – TR12 for post-lesson discussions conducted after observation. Codes for qualitative data collected through questionnaires were SET 1 – SET 120. The folders with each type of data were then uploaded into NVIVO software for further organisation and systematic sorting, coding, categorisation and analysis. NVIVO is a computer software package that helps to analyse qualitative data (Mukherji & Albon 2015). In NVIVO, each interview was coded and memos created to help remind the researcher of very important points. While coding was being done, NVIVO also provided a platform to edit the transcripts and further provided the researcher with an opportunity to interact with the data. Memos created were able to help the researcher identify similarities and differences from coded data even before actual analysis itself. Annotations were used to make comments on key points that were observed during coding. After data coding was done, a check through the codes was done and certain points that were wrongly coded were un-coded and recoded appropriately. Case codes were also created to easily provide a platform for comparison with data coded as internal codes. After all necessary coding was done, the researcher made sense of the coded data by running word frequency charts from the biographic respondent data, cluster analysis and comparison diagrams. These provided the basis for qualitative data analysis in this study. Thus, by use of cluster analysis, the researcher was able to identify coded themes and export the data under each theme to the main document. The comparison diagram helped the researcher to identify similar and different ideas between codes. Thus, similar concepts were grouped at the centre while differences were set on each side.

While the researcher ran the actual analysis tools in NVIVO, analysis continued from one stage to another. This is perhaps the reason some scholars contend that analysis is actually an on-going process because the researcher interacts and starts making sense of the data right from the time data starts being collected. Creswell (2014a) acknowledges that data collection and data analysis must be a simultaneous process for qualitative research.