As a rule, two key types of quantitative data are identified (Maimon & Rokach, 2010, p. 102). There is discrete data, which can only take a certain value. The number of people in a crowd can be an example of discrete data in an academic study. In addition to the discrete data, there is also the continuous data, which is characterized by its ability to take both certain and fractional values. According to the definition provided above, a person’s age or a shoe size can be viewed as an example of continuous data.
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As far as the types of quantitative data required to show the results of an intervention are concerned, it can be suggested that the information including the grades that the students receive for their performance, as well as the percentage of their improvement rates over the course of a specific time period, can be viewed as quite legitimate for its further use in an educational research. Apart from measuring the students’ the grades, there are other ways to incorporate quantitative data in the study, however for example an analysis of a specific intervention process in education may be incorporated into the research design (Stringer, 2013). Therefore, continuous data is obviously an essential part of any intervention process. To be more exact, the data in question will be required at the beginning and the end of each intervention process so that an assessment of the students in question cold be carried out and that the information regarding the efficacy of the aforementioned intervention could be obtained. Needless to say, the elements of personal information of the study participants, such as their age, must also be incorporated into the study and the analysis of the research results (Hays & Singh, 2011, p. 346).
However, it would be wrong to claim that an educational intervention does not presuppose the use of any discrete data at all. Quite on the contrary, it is essential to be able to identify the number of the research participants, as well as the quantity of tests to be run in the course of the study, the amount of samples to be taken in order to locate the research results, etc. (Stringer, 2013). Therefore, claiming that one of the data type is more important for the research outcomes than the other one does not seem legitimate – instead, one must mention that each quantitative data type plays an important role in the course of the research and must be incorporated into the latter accordingly.
Carrying out the basic tests allowing to define the difference in the students’ performance rates over time, though, is not enough to claim that the intervention used in the course of the research has turned out to be successful;. It is also necessary to incorporate the post hoc, ergo propter hoc fallacy concept in order to test the effects of the intervention. According to the logical fallacy in question, the very fact of the change does not necessarily mean that it was caused by the intervention designed for addressing the issue (Cox, 2006, p. 120). To identify the links between the change, should any occur, and the intervention steps, it will be necessary to identify all links between every single variable in the research including both dependent and independent ones, and test the effects of each variable separately, thus, locating the research outcomes.
Cox, L. A. (2006). Quantitative health risk analysis methods: Modeling the human health impacts of antibiotics used in food animals. New York, NY: Springer Science & Business Media.
Hays, D. G. & Singh, A. A. (2011). Qualitative inquiry in clinical and educational settings. New York, NY: Guilford Press.
Maimon, O. & Rokach, L. (2010). Data mining and knowledge discovery handbook. New York, NY: Springer Science & Business Media.
Stringer, E. T. (2013). Action research (4th ed.). Thousand Oaks, CA: SAGE Publications, Inc.