Data analysis

A well-defined data analysis strategy is important for exploratory studies since the goal of the research study is to develop a theory (Dubé & Paré, 2003). Having adequate description of the context of the case and extracts of data underlying the analysis helps to establish a chain of evidence to support the study’s findings (Benbasat et al., 1987). Data collection and data analysis happen concurrently in qualitative research as emerging results and insights from previous analysis informs the next stage of data collection (Eisenhardt, 1989; Miles & Huberman, 1994; Patton, 2002). This brings about internal validity since both data collection and data analysis processes occur in close proximity to each other, thus validating the final evidence (Dubé & Paré, 2003).

The first step in the analysis stage is organization of the data collected. All data, such as observation notes, online survey responses and interview transcripts are to be categorised and cross-categorized based on the data collection method and data collection stage. The observation notes and survey responses were already in text format, so could be easily transferred in NVivo. However, each interview had to be first transcribed individually by the researcher to the closest possible accuracy. The researcher applied bits of rewording and paraphrasing to the interview conversations so that the transcript could be contextualised to the research context and reduce content with irrelevant conversations during the interview period.

Data have been collected in different stages using various data collection methods, starting with the baseline data in 2012. The baseline data have been analysed using the NVivo to track the themes emerging from the survey responses from student, teachers and parents. The survey responses were analysed across multiple areas of investigation, including various constructs in access, capability and outcomes divide

97 identified earlier. This stage of data analysis helped to re-think and structure the constructs under the three categories of the digital divide framework adapted for the study. Much of the analysis is directly supported by quotations from participants to reveal “the undigested complexity of reality” (Patton, 2002, p. 463). Apart from the baseline data, two further rounds of data have been collected using one follow-up surveys and interviews/follow-up interviews.

Analytical descriptions have been used (Yin, 2003), to explain the lessons learned from the baseline data analysis so that it could then be applied in a broader context during subsequent investigations (Stake, 1995). Once all the data from surveys and interviews was organised and transcribed, NVivo was used to code the plain data into themes giving deeper understanding of the phenomenon under investigation. Insights from data from each phase of the study have been reported through research publications, presentations and summary reports that have been shared with the case study institution. The qualitative nature of the data, such as interview quotes have helped bring about richness in the reporting of the data analysis. Direct quotes in qualitative reports helps to capture the participant views regarding the phenomenon under study without the researcher in the middle (Creswell, 2007).

Creswell (2007) suggests finding a connection between the emerging themes for maintaining the rigour in the analysis of the data. That also includes combining or merging relevant themes at the different levels of the inquiry so that a theoretical and conceptual model can be generated. Janesick (2016) suggests using the quotes from the participants as they are expressed, to guide the reader to the analytical discourse. Therefore, quotes from the interview data have been widely used as evidence and back up the conclusions reached.

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3.10.1

Data analysis checklist

A researcher has to be flexible with the themes emerging from the analysis as the themes mapped based on the predicted data and the themes emerging from the empirical data may not match. This section utilises the checklist identified by Dubé and Paré (2003) for the steps taken as the themes emerge during the analysis of empirical data. As suggested by (Dubé & Paré, 2003), the following table lists the steps to establish the rigour in the exploratory case study research design.

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Table 3.6: Attributes to assess data analysis in case study research (Dubé & Paré, 2003)

Data Analysis Authors Exploratory This study

Elucidation of data

collection process (Benbasat et al., 1987) (Yin, 2003) (Eisenhardt, 1989)

 

Combining and merging themes from data

Field notes (Yin, 2003)

(Eisenhardt, 1989)  Field notes were made during the visits to the school for the meetings and events like staff training sessions and annual conference. This was never used directly into the analysis, but was used as input to prepare for subsequent round of data collections.

Coding and

reliability check (Yin, 2003)  Purposeful sampling of the critical case. Baseline and follow-up surveys and interviews.

Data displays (Yin, 2003)  

Folder structures to organise the data collected through various stages of the study. Visual mapping of themes and codes in NVivo.

Flexible and opportunistic data collection process (Benbasat et al., 1987) (Yin, 2003) (Eisenhardt, 1989)  

As the themes, emerging from the analysis of baseline data required revisiting of the ideas and the themes anticipated before the data collection and analysis. Therefore, the flexibility was maintained throughout the various stages of data collection and analysis.

Logical chain of

evidence (Benbasat et al., 1987) (Yin, 2003)  Emphasised in the context of the case and the richness of the data retrieved from the case to establish the clear chain of evidence up to the results.

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Explanation

building (Yin, 2003)  Followed the descriptive analysis of the qualitative data.

Searching for cross-

case patterns (Eisenhardt, 1989) (Lee, 1991)

 _{Not applicable}

Quotes (evidence) (Benbasat et al., 1987)

(Yin, 2003)  Exact quotes from the participants have been used in the write-up as

evidence.

Reviews of data

analysis and results (Yin, 2003)  Repeated the analysis exercise to spot any difference in the results. Reporting the outcomes through presentations, peer-reviewed conferences and journals.

Comparison with

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