The effect of this is that the focus of the research moves from theory to data.
The deductive approach is also a descriptive approach in that it describes facts and illustrates theories. Deductive research is often applied to questionnaires and the collection of quantitative data which addresses the hypothesis.
There are several advantages to deductive reasoning. It is a relatively simply research method to standardise, as it is a highly structured approach based on scientific principles. Furthermore the structured approach means that the researcher does not necessarily need to be highly knowledgeable about the research topic. Most managers are familiar with the deductive approach and therefore are much more likely to put faith in conclusions that are made using it. In addition it is a quick method of gaining data, as the time-consuming aspects of this type of research are mainly in the set-up and data collection phases. This also means that the amount of time needed to invest in the research can be scheduled and predicted relatively easily. All of these things make deductive research low risk in comparison to inductive research.
There are also a number of concerns with this approach. Firstly it is important that causal relationships between all variables in the research are explained and defined. Selecting samples of sufficient size is a crucial factor in determining the success of this type of research. The sample must be large enough to generate accurate conclusions that are reliable in all applications of the data. A significant issue with deductive research is in clearly defining abstract concepts. Taking the example of employee morale, it must be defined in terms of level of satisfaction of employees, which is a personal concept and may mean different things to different people. Conducting this type of research means that concepts must be clearly defined to enable accurate responses from research subjects. Lastly, controls must be applied to ensure the validity of the data gained from the research. If the research was in defining levels of employee satisfaction in a particular department, reasons for high or low employee moral must be established. For example, the data taken from employees may indicate a relationship between workload and employee salary. This data can then be used to refine the hypothesis of a relationship between workload, employee salary, and morale, and then collect new data for analysis. This may include input from different departments, both to increase sample size and to note the effects of varying workloads or salaries in different departments. This ensures the accuracy of the hypothesis because it takes into account variation of these aspects in different departments. A larger sample size allows further refinement of the hypothesis - for example it may narrow down the hypothesis to focus on young employees, and allow the researcher to pinpoint different effects of low or high morale, such as absenteeism. In this way, an initial hypothesis focusing simply on employee morale can be progressively refined to pinpoint causal relationships that become evident as the focus of the research narrows.
One of the main disadvantages of this type of research is that it relies on subjects to fill and return questionnaires, which may become more difficult to do as sample size