Methods of Data Analysis Adopted

Tutor Interview Analysis

Template analysis was chosen to analyse the tutor interview data given its value when comparing respondents’ views within a specific context and its suitability for anti- positivistic, idiographic research (King, 2004b). King (2004b: 256) argues that template analysis is not a single method or research itself, or a methodological position, but a series of techniques for the inductive analysis of textual, or in this case, interview data. The literature review chapters identified a range of issues for the interview to explore and these formed the basis of the a-priori codes identified for the first template. However, some flexibility was required in template development and subsequent analysis, particularly, in relation to emotional competence. Template analysis affords the exploration of broad themes but allowed amendments if new factors were emerging from the data and allowed opportunity to consider both Mayer and Salovey’s and Goleman’s models of emotional intelligence.

The first template had a mix of descriptive codes, such as tutor experience, and analytical codes, for example, tutor ability to work within available resources. The codes were

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developed into a hierarchy to allow deeper levels of analysis but not too structured to prevent confusion in the organisation and interpretation of data (King, 2004b: 258).

From the analysis of tutor interviews, themes and factors underpinning module delivery were determined and compared to the other research instruments. A selective approach (King, 2004) was adopted which identified themes that were most relevant to develop an understanding of the tutors’ skills and qualities and areas of good practice in blended tutoring. These were then compared and analysed in relation to data from the learner questionnaire, VLE content and tutor MSCEIT scores. I was careful to explore relationships between themes beyond the linear structure of the template, such as tutor perceptions of online learning, which allowed analysis across various strands of the research.

The first stage of the multiple-case study analysis was to listen to each interview carefully. Notes were made about what appeared to be relevant themes worthy of further investigation; however, the real benefit of this process was to listen to the manner in which points were made. It was illuminating to note expressions of confidence, concern, anxiety and enthusiasm, and was particularly helpful when trying to analyse beneficial emotional competences for effective tutoring.

The interview transcripts were read with a few points noted about areas that were striking and this was repeated with margin notes made for potentially significant comments and observations (Bryman, 2008: 550). This led to the alteration of codes identified in the pilot study both in relation to emerging concepts, such as tutor self-efficacy, and issues identified in the literature review chapters, such as the motivations of adult learners. Other

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more general theoretical themes emerged (Bryman, 2008: 550) particularly around desirable skills, qualities and traits of tutors. The interview transcripts were then reviewed in more detail and coded using the developing template (King, 2004b). Again, themes were noted as the coding process was undertaken and were analysed using a framework approach to thematic analysis (Bryman, 2008: 550) which involved tabulating emerging ideas against tutors (who were ranked in descending order of learner perceptions, measured by CEQ scores). Through this process, themes emerged that were important in all modules; important in those of tutors receiving the highest scores on the learner questionnaire; and those that were only observable in the tutors receiving lower scores. The final template is presented in Appendix 7 which highlights the code hierarchy utilised. The names of codes used and their descriptions can be found in Appendix 8.

Analysis of the Module VLE Content

The analysis of the VLE content used the template developed for the interview analysis when considering any tutor comment. Tutors made comments within the VLE, such as announcements, or within discussion and chat media, and these were analysed using the interview template, which allowed consideration of emotional competences in such media. The text also provided an insight to the quality of online pedagogy and support, such as weaving the discussion (Feenberg, 1989; cited in Salmon, 2003: 42) and timely responses to learners. Categorisations were also made regarding the online pedagogical approach adopted and the alignment of teaching, learning and assessment throughout modules (Mayes and de Freitas, 2004: 7). Analysis of assessment briefs, held within the VLE, was particularly useful in this regard and provided an outline of the module structure.

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Analysis of the Learner Questionnaire

The learner questionnaire was analysed using descriptive statistics only during the pilot study. Results were compared to the other research instruments’ findings.

During the full empirical research, the learner questionnaire was analysed assuming the data was on an interval scale6 to allow parametric analysis. Two parts of the learner questionnaire were modified versions of other questionnaires, the CEQ (Ramsden, 1991) and R-SPQ (Biggs et al., 2001), both of which developed their scales with acceptable reliability with confirmatory factor analysis confirming the fit to their intended structures. Whilst there has been some debate about the nature of data generated by Likert scales (for example: see, Knapp, 1990), given the previous scrutiny of both the CEQ and R-SPQ, and their subsequent treatment in other research as interval scale data, (for example: see, Kreber, 2003; Richardson, 2005; Ginns and Ellis, 2007), they have been treated as such in this research. The OTQ has similarly been assumed to be a scale.

The main judgements of effective blended tutoring, and therefore the dependent (or outcome) variables, are:

 CEQ and subsections;  OTQ and subsections;  learner achievement.

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Statistical analysis was undertaken to explore associations between the dependent variables above, and a range of independent (or predictor) variables identified from literature review chapters, the pilot study, and preliminary interview analysis. Examples of independent variables included:

 the MSCEIT measure of the tutor’s emotional competence;  the tutor’s teaching experience;

 the tutor’s technical skills;

 the tutor’s training relevant to blended learning contexts;  the tutor’s workload.

To consider other factors that could potentially influence learners’ perceptions of effective tutors, the independent (or predictor) variables included:

 learners’ approaches to study;

 learners’ factual data including age and gender;  learners’ HE experience;

 learners’ blended learning experience.

This research identified associations between the dependent and independent variables, however, caution is needed when considering association and causality. Causality refers to how particular sets of conditions lead to predictable outcomes (Oppenheim, 1992: 16). This point is pertinent for this research with the analysis exploring probabilistic relationships in which factors increase or decrease tendencies for particular outcomes,

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namely factors influencing learners’ perceptions of effective blended tutoring. Although the use of the terms ‘dependent’ and ‘independent’ variables implies a causal relationship as independent variables are hypothesized to cause changes in dependent variables, judging causality between the identified variables is difficult and therefore these terms are used tentatively.

From interviews with tutors, analysis of VLE content, and factors that emerged from the pilot study, I ascertained assessment measures regarding the qualities, skills and experience of the tutors (see Chapter 8.6 for an outline of this process) which allowed analysis and comparisons with the measures of effective tutoring. Criteria were developed to provide ordinal scores7 about aspects of the tutor.