Research approach

There are two core research approaches available: deductive and inductive. This sub-section will discuss these approaches, with emphasis given to the chosen approach, the deductive. Then, the reasoning behind my decision and the stages for using the deductive approach will be addressed.

Deductive research is associated with developing a conceptual framework or theory and then using it for testing purposes (Farquhar, 2012). Grix (2001) states that deductive research involves clear assumptions and solid previous knowledge in order to understand and find new answers for a particular problem. Above all, it is solidly theory-driven research. Correspondingly, Blaikie (2010) argues that the deductive approach requires an in-depth analysis of theoretical work prior to data collection. Collins (2010) notes that the deductive approach is more suitable and common when the research subject has a strong literature background that the researcher can rely on to define propositions and relationships. Also, the deductive approach is associated more with positivism, since the need to explain causal relationships between variables is an inherent element of the deductive approach. Another crucial element of the deductive research pathway is the need to select large size samples as well as apply controls to ensure the validity of the data (Saunders et al., 2012). The inductive approach, on the other hand, is more relevant when the subject is ‘new,’ with little published research on the topic. In such circumstances, it is more suitable to use the inductive approach to generate data in order to answer the research question. Also, the inductive approach relates more to flexible methodology rather than structured (Saunders et al., 2012). The deductive approach involves objectivity since the research needs to be independent of what is being observed. Feelings and personal viewpoints should not enter into the research. Only the facts that can be measured can produce knowledge. Related research terms and propositions must be accurately defined (Collins, 2010). Deductive reasoning moves from a

pattern that might be logically or theoretically expected to observations with the view to test whether the expected pattern actually occurs in the real world (Maxfield & Babbie, 2012). Therefore, the deduction can be a hypothesis, prediction, or an explanation of the regularity, depending on the aim of the research (Blaikie, 2010). In addition, deductive research needs to explain the causal relationships between two or more variables or concepts (Collins, 2010; Saunders et al., 2012). On a statistical level, the deductive approach makes ‘‘use of general information to draw conclusions about specific cases’’ (Lee et al., 2009). Deductive reasoning is based on logical propositions. One of the main branches of deductive reasoning is conditional reasoning. Under this reasoning, the researcher attempts to draw conclusions based upon an ‘‘if-then proposition.’’ If antecedent condition (a) is met, then consequent event (q) follows (Sternberg, 2009). Collins (2010) highlights five basic stages for the use of the deductive research approach:

1) Set a testable proposition, the relationship between two or more variables or concepts.

2) Indicate how the variables can be measured. 3) Test this proposition.

4) Review the outcome with the view to confirm the theory or the model or to establish how the proposition needs to be modified.

5) Finally, if necessary, modify the proposition and the repeat the process.

Similarly, Blaikie (2010, cited in Saunders et al., 2012) divides the deductive approach into six stages:

1) Put forward a testable proposition or hypothesis regarding two or more concepts or variables.

2) Use the existing literature and specify the conditions under which the theory is expected to be supported, thus deducing a testable proposition or number of propositions.

3) Examine the logical aspect of the argument by comparing it with existing theories to validate whether it offers an advance in understanding or not.

4) If it does, then test the hypotheses using appropriate data in order to measure the concepts or variables and provide an analysis.

5) If the findings are not consistent with the hypotheses, the test has failed. This implies that the theory is false and the researcher must either reject the hypotheses or modify them and restart the process.

6) If the results of the analysis are consistent with the premise/hypotheses, then the theory/model is validated.

Based on the above stages, the term ‘‘deductive’’ refers to research that relates to the method of proposing hypotheses in order to test their acceptability or falsity by examining whether they are compatible with empirical data (Grix, 2001). Inductive research, on the other hand, draws conclusion from specific empirical data (the particulars) and attempts to provide generalised theories leading to more abstract ideas (Grix, 2001). Similarly, as Lee et al. (2009) argue, an inductive approach in statistics involves drawing general conclusions from specific information rather than vice versa, as with deduction (Saunders et al., 2012). Within the framework of the inductive approach, emphasis is given to qualitative data (Collins, 2010). Commonly, the inductive approach uses interviews as the main data sample in order to understand and interpret the nature of a problem. Also, its methodology is less structured compared to the deductive approach (Saunders et al., 2012).

Figure 6. Deductive and Inductive Approaches

Housing bubbles as a field of research have a strong academic background. The nature of the research topic requires an in-depth analysis of the existing literature prior to data collection. Moreover, a quantitative method is the most preferred tool of analysis for bubble research, as objectivity is a core characteristic in such research. Alternatively put, the topic of housing bubbles is, in itself, more oriented toward positivism. In addition, the need to explain causal relationships between variables is crucial for understanding the complex nature of housing bubbles. A large sample size is also a necessity for undertaking a reliable examination of housing bubbles, while the application of controls to ensure the validity of the data is also important. With this study, my intention is to build a new ‘‘structure methodology’’ to address the research question, with the view of providing empirical tests. Taking into consideration the differences between the deductive and inductive approaches, as well the research characteristics of housing bubbles described above, I conclude that the deductive approach is the most suitable route for this study.

5.1.3 Research design and strategy