Inductive and deductive approach

It was stated that a mixed-methods approach of qualitative and quantitative methods was used. The difference between the two that should be noted is that qualitative research is considered inductive and quantitative research deductive. However

induction and deduction on their own are inadequate to base compelling arguments on in this complex study of e-Learning practice.

Induction, a characteristic of qualitative research driven by observation, does not elicit actual proof. On the other hand, deduction, by the nature of its logic driver, provides proof but does not base this on the real world through observation. This perceived dichotomy creates a tension in deciding on an approach for a study. The researcher elected to use mixed methods to harness the richness of the two approaches and to address internal validity.

The focus of this study is on what the participants do in e-Learning and why they elect to do this. To achieve this, the researcher found it necessary to employ both inductive and deductive reasoning which is aligned to Babbie’s (2010:23) wheel of science. Van der Merwe (1996:279) refers to this wheel of science as the “cycle of scientific enquiry” (See Figure 3.3 below).

Figure 3.3: Cycle of scientific enquiry (Babbie, 2010:23, as adapted by Van der Merwe, 1996:279)

In the implementation of this study the researcher used an iterative approach, progressing from the known to the unknown and looping back. This dual approach, according to Mouly (1978:5) is “a back-and-forth movement” where a researcher “operates inductively from observations to hypotheses, and then deductively from these hypotheses to their implications” to “check validity from the standpoint of

compatibility with accepted knowledge” (Cohen et al., 2005:4-5). This is referred to as the constant comparative method typified in the grounded theory tradition (Glaser and Strauss, 1967). This was particularly useful for the researcher to listen to the teachers and check what this represented in terms of the teachers’ beliefs. It further allowed the researcher to validate the teachers’ reality against known theories and for a theoretical concept to be tested against the teachers’ actions.

The deductive approach is based on a premise where a theory and hypotheses are developed and a strategy is designed to test the hypotheses. To understand why something is happening, according to Saunders et al. (2003:90) is deductive. This enables “researchers to make deductions from existing theories about the phenomena under investigation” (Van der Merwe, 1996:279). This does not presuppose that theory comes before data. Instead there is a relationship between data and theory, as empirical data may yield unexpected events and phenomena which may not be explainable through a theory. The richness of the product of relationship between theory and data is that it enables a theory-building process.

In this study the researcher needed, as a baseline, to determine what technologies teachers used, what they used these technologies for, what factors influenced their use of the technology, and their levels of uptake of technology. A survey questionnaire was used that drew on the constructs in technology adoption and diffusion theories. The researcher believed that such a questionnaire would provide data that could be measured statistically and further inform the design of leads and probes for the interviews.

The data from the survey questionnaires would yield proof in numbers and this could be interpreted using frequencies to show trends and patterns. Hence in this study the quantitative method using a deductive approach was deemed suitable. Employing the deductive approach in this research assisted in providing a frame of reference for the analysis and interpretation of the data.

The inductive approach conversely begins with data that has been generated, such as through interviews and observation. These are categorised and coded and, trends and patterns are sought as they emerge. These codes from the data are referred to

as “substantive codes which are developed during the open coding stage” (Bendassolli, 2013: online). An additional process of logically determining what theory or theories could explain the data is undertaken. In such cases the coding is known as theoretical codes, referring to the formal categorisation inherent in the specific theory (Bendassolli, 2013). The inductive approach tends to use small samples and is well suited to qualitative data. The sample size of this study is discussed later in this chapter.

To generate this data, interviews were used to elicit teachers’ perceptions on e- Learning and what drives them to use technologies for education. Interviews are one of the common methods of data collection in qualitative research, in inductive data gathering and in the grounded theory tradition (Glaser & Strauss, 1967; Bendassolli, 2013). The resulting data provided the basis for identifying trends and patterns. This was then used for linking with known theories that expound on the theoretical reasoning or underpinning aspects related to the actions of the teachers.

Electing to use the inductive approach as the qualitative method allowed the researcher to look for links, patterns and relationships in the data which, according to Van der Merwe (1996:279), “is useful in guiding subsequent data collection”. Details of the data analysis process are set out later in this chapter. Babbie’s (2010) wheel of science referred to as the “cycle of scientific enquiry” (Van der Merwe, 1996:279), is congruent with the "generic analytical cycle" (Bendassolli, 2013:online), which assumes qualitative research to be a cycle of induction and deduction. Accordingly Bendassolli (2013: online) notes that “researchers deductively draw upon concepts from an extant theory in order to explain, accommodate or embed their emergent substantive theory (the theory they were able to ground in their data)”.

In this study the creation of new knowledge through abduction comprised combining identifiable elements that emerged from theory with elements from the data. Abduction in the context of this study is taken to be a form of inference through which previously unrelated elements are subsequently suggested to be related.