Data Analysis Methods

Using multiple research methods (see Section 3.3.4), the researcher obtained both quantitative and qualitative data. Because their inherent features were distinct, the quantitative and qualitative data were analysed separately and differently. However, in the discussions of research findings (see Chapters 4, 5, and 6), quantitative and qualitative information was combined and compiled to provide a holistic picture of the participants’ interpretation ability of visual representations in plant anatomy. In the following paragraphs, each method that was utilised to analyse each type of data is described.

3.3.5.1 Quantitative Data

In this investigation, quantitative data were generated from participants’ scores on plant anatomy diagnostic tests. Once an instrument administration was completed, participants’ answers to the test were assessed manually based on a scoring rule as described in Section 3.3.4.2. For the first parts of the PADI-I and the PADI-III, students’ scores on each item in the instruments were directly tabulated in Microsoft Excel spreadsheets. The tabulated raw data were then inputted into IBM SPSS Statistics 20 software for statistical analyses (Tsui & Treagust, 2010). The analyses

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included frequencies, percentages, and Cronbach’s alpha coefficient for internal consistency of the dichotomously scored tests (Downing, 2004; Streiner, 2003). Subsequent evaluations were applied to students’ responses to the first part of the PADI-II and drawing parts of the three instruments. Initially, all participants’ answers and drawings were assessed independently by two reviewers who had extensive knowledge of plant anatomy. For the independent assessments, these two assessors relied on particular rubrics as shown in Appendices D3, C4, D4, and E4 for the first part of the PADI-II and the drawing part of the PADI-I, the PADI-II and the PADI-III, respectively. Then, the individual evaluation results were compared; and percentages of agreement were computed. If the minimum 80% of agreement was not reached (McHugh, 2012), the two reviewers either revised the rubrics or reassessed students’ responses. When this minimum percentage of agreement had been met, any differences in an individual assessment were discussed by the two reviewers, and final decisions about the student’s score were made. The final scores were then tabulated in Microsoft Excel spreadsheets to enable computations of frequencies and percentages.

In addition to students’ performance on diagnostic tests, quantitative data also derived from instructors’ reports of students’ achievement on plant anatomy. The tabulated students’ scores received from the instructors were then presented in pie charts for quick analysis. The aim of the inclusion of this document is to triangulate the findings of the diagnostic tests about the student participants’ conceptual understanding of plant anatomy.

3.3.5.2 Qualitative Data

Qualitative data in this research were derived from classroom observations, semi- structured interviews and document reviews. The qualitative database from the three methods, including field notes, interview transcripts and document analysis records were then analysed using qualitative content analysis. Adopting a definition proposed by Graneheim, Lindgren, and Lundman (2017), content analysis refers to a strategy for analysing text-based data in order to describe the visible component in the texts or to interpret the underlying abstract ideas of the qualitative evidence. This method of analysis was selected because the main goal of content analysis is to provide insights toward the phenomena being investigated (Fraenkel et al., 2012; Hsieh &

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Shannon, 2005; Merriam, 2009), and thus is in line with the primary purpose of this investigation.

Referring to the three approaches to content analysis described by Graneheim et al. (2017), this investigation used the deductive approach to analyse the content of qualitative information being collected. Using this theory-driven strategy, the initial categories in each qualitative database were predetermined based on the research questions and theoretical framework of this study (Graneheim et al., 2017; Hsieh & Shannon, 2005). In so doing, this research has an opportunity to validate or expand the existing theory being relied on in this investigation (Cohen et al., 2011; Graneheim et al., 2017; Hsieh & Shannon, 2005).

The step-by-step process of qualitative data analysis in this research was carried out using a strategy described by Graneheim and Lundman (2004) as follows. Initially, the qualitative database was sorted into the predetermined categories which included the seven factors of the CRM model (see Section 2.4 for descriptions) and teaching strategies. Any content that was difficult to categorise under the predetermined categories was put under a new created category. Afterwards, the researcher read thoroughly and carefully through an item, for example one interview transcript, under a particular category to gain insights about the content of the information being collected. This text was then reread for the process of coding. At this stage, while reading the text, meaningful sentences or paragraphs were highlighted and condensed, then the meaning of these meaning units was coded by the researcher and recorded in the margins. This process is known as “open coding”, that is, “a process of segmenting and labelling text to form descriptions and broad themes in the data” (Creswell, 2012 p. 243). Once the entire passage had been coded, the emerging codes which had similarities were sorted into new categories. In the next step, the researcher moved to another item under a different initial category. This new information was treated using the same process as outlined above. Subsequently, all emerged categories in the whole qualitative database were interpreted and separated into themes.