Data analysis

The research was cross-sectional with equivalent weighting placed on the qualitative and quantitative data (Creswell, 2009). Quantitative and qualitative data were complementary

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and sought to investigate different aspects of the study. However, quantitative data (from the survey) and qualitative data (from the questionnaires and interviews) were analysed

separately and then compared to determine how the data supported, refuted or built on the results.

The survey data were analysed using descriptive statistics. Raw data from the five-point Likert scale were entered into SPSS16 (a statistical analysis software package). That is, strongly disagree=1, disagree=2, uncertain=3, agree=4, and strongly agree=5. Using the analysis function of the SPSS package, percentages, mean scores and standard deviations were generated. These statistics allowed for analysis of completed responses from the 142 final-year preservice teachers. Agree and strongly agree percentages (i.e., from raw data responses 4 and 5) were added together. “Means and variances for items scored on a continuum (such as a five-point Likert-type scale) are calculated simply the way

other means and variances are calculated” (Kline, 2005, p. 95, parenthesis included). In this way, it could be determined the percentage of preservice teachers who generally agreed with a survey item in relation to their confidence to teach in the middle school. Mean scores showed where these responses were averaged, and the standard deviation (SD) showed the variation of responses to the mean score (see Hittleman & Simon, 2006). Quantitative data were organised into one table to provide an overall view of the preservice teachers’

confidence to teach in the middle years of schooling followed by the four aforementioned categories that emerged from the literature (see Figure 2.1). Therefore, five tables were produced catering for all survey items, which provided a framework for analysis and reporting purposes. The analysis of the qualitative data (questionnaire and interviews)

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followed the six steps suggested by Creswell (2009, p. 185), a version of which is noted in Figure 3.3.

As depicted in Figure 3.3, the qualitative data analysis was conducted in a linear approach building from the bottom up. The questionnaires were analysed prior to the interviews as the questionnaire provided a generalised response while the interview data provided more in-depth viewpoints. Creswell (2009) notes that, in data analysis, it can be useful to go from the general to the specific. For example, the questionnaire asked for the participants to outline what university experiences had assisted in building the confidence of the middle years preservice teachers to teach. The questionnaire was in written form and delivered in a limited time frame, hence, a more general response was elicited. A similar interview question was devised allowing the researcher to elicit a more in-depth or specific response.

Questionnaire data were prepared for analysis by first numbering each participant (1 to 142) and transcribing the responses under each question. The responses were analysed several times to gain a general idea of the content and overall meaning. Notes were kept to record any points of interest or important details that may be forgotten. The responses were coded for commonalities, a process where raw data are organised into particular categories on the basis of “themes, concepts or similar features” (Neuman, 2003, p. 420). That is, similar responses were collated as an emerging theme. As themes emerged (see step 5 in Figure 3.3), descriptions indicative of each theme were highlighted as examples to provide a narrative for addressing the research aims (Creswell, 2009). This process was undertaken with each question noted on the questionnaire.

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Figure 3.3. Process of analysis for the qualitative data.

Figure 3.3. This figure summarises how the qualitative data were analysed beginning with Step 1

and working in a linear approach through to Step 5. The steps were repeated several times to substantiate the accuracy of the emerging themes.

Step 6

Interpreting the meaning of the themes/descriptions

Step 5

Interrelating themes/descriptions

Themes Descriptions

Step 4 Coding the data

Step 3

Reading through the data

Step 2

Organising and preparing the data

Step 1

Raw data transcriptions Validating

the accuracy of information

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To check the reliability of the research, the processes in Steps 1 – 5 in Figure 3.3 were documented and repeated several times to substantiate the accuracy of emerging themes (Flick, 2007). The emerging themes were cross-checked by another researcher to ensure their authenticity and trustworthiness (Creswell, 2009). Step 6 in the data analysis involved “making an interpretation or meaning of the data” (Creswell, 2009, p. 189). This process involved the researcher analysing overall themes that emerged, and making comparisons with the other data sets such as empirical evidence gathered from the literature. Creswell (2009) notes that through this process, findings can either confirm or refute past information or new questions can emerge to provoke further research. Following this final step, the expertise of a qualitative researcher was sought to provide an alternate opinion as to whether the interpretations were accurate and not influenced by the views of the researcher

(Neuman, 2003).

The interview data was related to all three research aims and served the purpose of

complementing the survey and questionnaire data. It also provided in-depth responses and an opportunity to clarify and expand the existing information. The steps noted by Creswell (2009) in Figure 3.3 were followed for the analysis of this qualitative data. The first step was to transcribe the 10 audio-taped interviews and align with the researcher’s notes. The transcriptions were then organised into responses under each question, similar to the process undertaken for the questionnaires. For example, interview question 6 asked: What university experiences in your course have increased your confidence to teach in the middle-years classroom? All the responses to this question were noted below and the participants’ transcribed responses were numbered 1 to 10, which allowed for a comparison of each participant’s response. Step three included reading through the

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ordered responses to each question. It is important before coding that the researcher becomes familiar with the data so as to make well-informed decisions about the emerging patterns and trends (Hittleman & Simon, 2006). To ensure validity, a fellow researcher was asked to view the coded data and check for any anomalies or themes possibly

overlooked (Hittleman & Simon, 2006). The researcher provided further suggestions and, once clarified, the themes were confirmed. Suitable examples of the transcribed audio- taped interviews were highlighted so they could be used to substantiate and emphasise a theme’s emergence. As the interview data addressed all three aims of the research,

interview themes were compared to the four themes emerging from the literature (Section 2.6) and combined with the survey data to address the first aim of the research. The themes emerging from the interview questions relating to the second and third aims of the research (e.g. question 6 & 8 respectively) were then compared and combined with the questionnaire data.