Data Analysis Procedures

Miles and Huberman (1994, p. 10) defined qualitative analysis as ―consisting of three concurrent flows of activity: data reduction, data display, and conclusion drawing/verification‖. The process of data analysis involves ―preparing the data for analysis, moving deeper and deeper into understanding the data, representing the data, and making an interpretation of the larger meaning of the data‖ (Creswell, 2003; p. 190). The

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process of data analysis occurs both within the quantitative (descriptive analysis) phase and the qualitative (description and thematic analysis) phase.

Data Analysis of Questionnaire

A descriptive statistical method was used to analyze, describe and present the quantitative data from the ―Survey of Student Communication & Study Habits‖ (Creswell, 2003; Cohen et al., 2007; Creswell & Plano Clark, 2011). Descriptive techniques intend to summarise numeric data in tables, graphs or representations of scores/percentage (Cohen et al., 2007). Descriptive statistics seeks to support researchers in understanding the data, detecting patterns and better communicating the results (Tashakkori & Teddlie, 2003). A statistical software program, SPSS (Statistical Package for Social Sciences) - the most widely used statistical package for social sciences (Cohen et al., 2007) - was used for in- depth data analyses. Results were recorded in a spreadsheet and transferred to SPSS for statistical analysis.

Data Analysis of Interviews

The qualitative software Atlas.ti 7.1.7 was used to import the transcription of the interview and to code each response. During data analysis, the interviewer/researcher immerses herself in all the material, working with all the interview transcriptions. It was conducted following the three-phase procedure described by Miles and Huberman (1994).

Data reduction is the first step of qualitative data analysis and involved the process of

selecting, simplifying, abstracting and extracting themes and patterns from transcripts. The aim of this reduction is to organize data in such way that final conclusions can be drawn and verified (also known as data condensation) (Miles & Huberman, 1994).

To accomplish this task, thematic analysis was employed to analyse the semi-structured interviews as outlined by Braun and Clarke (2006) (Table 16). Thematic analysis is a method for identifying, analysing and reporting patterns (themes) within data that minimally organizes and describes the data set in (rich) detail (Braun & Clarke, 2006). By using thematic analysis, there is the possibility to link the various concepts and opinions of the learners and compare these with the data that has been gathered during the literature review. The process consists of reading through textual data, identifying themes, coding, and interpreting the structure and content of the themes (Guest, Namey & Mitchell, 2013).

91 Table 16. Phases of Thematic Analysis

Phase Description of the process

Familiarizing yourself with your data

Transcribing data, reading and re-reading the data, noting down initial ideas.

Generating initial codes Coding interesting features of the data in a systematic fashion across the entire data set, collating data relevant to each code.

Searching for themes Collating codes into potential themes, gathering all data relevant to each potential theme.

Reviewing themes

Checking if the themes work in relation to the coded extracts (Level 1) and the entire data set (Level 2), generating a thematic ‗map‘ of the analysis.

Defining and naming themes

Ongoing analysis to refine the specifics of each theme, and the overall story the analysis tells, generating clear definitions and names for each theme.

Producing the report

The final opportunity for analysis. Selection of vivid, compelling extract examples, final analysis of selected extracts, relating back of the analysis to the research question and literature, producing a scholarly report of the analysis.

Note. From ―Using thematic analysis in psychology‖, by V. Braun & V. Clarke, 2006, Qualitative Research in Psychology, 3(2), p 87.

Data reduction began with reading and re-reading the transcribed data. The themes began to emerge with the initial reading of each transcript. According to Miles and Huberman (1994, p. 56), codes are defined as ―tags or labels for assigning units of meaning to the descriptive or inferential information compiled during a study‘‘. Coding is the first step of data analysis by indexing or categorizing the text in order to establish a framework of thematic ideas about it (Strauss & Corbin, 1990; Gibbs, 2007). Interview transcripts were imported into Atlas.ti for analysis and in vivo codes - assigning a label to a section of data using a word or short phrase taken from the transcripts (Given, 2008) - were generated. The transcriptions were analysed using open, axial and selective coding strategy (Strauss & Corbin, 1990). In open coding, interviewer/researcher immerses herself in the data through line-by-line analysis, coding the data in as many ways as possible into themes and categories (Strauss & Corbin, 1990; Miles & Huberman, 1994; Cohen et al., 2007). According to Gibbs (2007, p. 52), line-by-line coding forces the researcher ―to pay close attention to what the respondent is actually saying and to construct codes that reflect their experience of the world‖. In axial coding, the categories are ―refined, developed and related or interconnected‖ (Gibbs, 2007, p. 50). During axial coding, the researcher works to understand categories in relationship to other categories and their subcategories. During selective coding, the core category is identified, selected and related, in a systematic way, to the other categories uncovered in the research (Strauss & Corbin, 1990; Gibbs, 2007).

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Analysis was a highly iterative process involving successively reading, coding, reviewing, and re-coding the data into categories or ―families‖ (family: term used within Atlas.ti to refer to thematic categories) because they share some characteristic (Creswell, 2003; Fereday, 2006; Saldaña, 2009). This process uses inductive reasoning, by which categories and codes, supported by quotations, emerge from the data through the researcher‘s careful examination and constant comparison. Code names were assigned to those themes that were detected and then organized into categories (sub-categories) of related topics, patterns, concepts, and ideas that emerged from learners‘ perspectives.

Figure 4. Digital technology theme.

Figure 5. Sample of category, sub-categories and codes.

Overall, the process of coding produced two themes (digital technology and generation of students), twelve categories, several of which included smaller subcategories and 145 codes. In regard to ―digital technology‖, these categories consisted of (1) meaning (software and device), (2) benefits (social and academic purposes; disadvantages), (3) use (frequency: low, moderate and high; social and academic purposes), (4) for university (URV resources, used at URV, used by professors), (5) for home, (6) for work, (7) for

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entertainment, (8) digital communication technologies (software and device), and (9) Internet (daily use, frequency and connection). In ―generation of students‖, these categories consisted of (10) terms, (11) identification and (12) factor. A detailed list of categories and codes can be found in Appendix D. Figures 4 shows the digital technology theme and its categories; and Figure 5 shows a sample of category, sub-categories and codes.

Data display is the second step and is used to incorporate information into an accessible

summary to facilitate later conclusion-drawing that include matrices (see Appendix D) and networks (see Figure 4) (Miles & Huberman, 1994). Matrices are rows and columns of data that have been extracted from coded transcripts and are organized according to themes. Networks are charts that summarize information by providing a picture of reduced data.

Conclusion-drawing and verification is the final step of data analysis and consists of

drawing initial conclusions (Miles & Huberman, 1994). The results are verified and deemed appropriate by evaluating their trustworthiness.

Instrumentation: Choosing Qualitative Data Analysis Software

All the interviews were transcribed, analysed and coded using Atlas.ti (Figure 6), a computer-assisted qualitative data analysis (QDA) software, and user friendly application. According to Dicicco-Bloom and Crabtree (2006, p. 315) ―using a computer to facilitate analysis can save time, make procedures more systematic, reinforce completeness and permit flexibility with revision of analysis processes‖. The choice of this qualitative data analysis software was grounded in a number of reasons: (a) for its capacity to handle and organise the large amounts of data that was collected throughout this study, ―by allowing the researcher to concentrate on conceptual issues, without having to worry about storage and retrieval of information‖ (Attride-Stirling, 2001, p. 403); (b) enable the researcher to associate codes or labels with chunks of text, sounds, pictures, video and other digital media formats that cannot be meaningfully analysed by formal, statistical approaches; (c) to search these codes for patterns; and (d) to construct classifications of codes that reflect testable models of the conceptual structure of the underlying data (Lewis, 2004; Hwang, 2008).

94 Figure 6. Sample of analysis process in Atlas.ti.

The use of the Atlas.ti software has significantly facilitated the process of organising, re- arranging and managing the considerable amount of data. For example, after coding the interviews, all passages assigned to a specific code could be viewed on screen and printed. Figure 6 illustrate one interview with all relevant codes that was being displayed on the right hand side enabling ease of navigation. Different sets of interviews were assigned with different colours for easy distinction.

Integration of Quantitative and Qualitative Data

Creswell (2003, p. 212) suggests that integration of two types of data might ―occur at several stages in the research process‖. It could occur during data collection, analysis, interpretation, or in some combination of these stages. In this study, integration of qualitative and quantitative data occurred largely at the interpretation stage and in the final discussion.