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The process of data analysis can be recognised as one of the essential activities throughout human history. Its purpose is to collect and process different types of data to obtain new and valuable information. Data analysis is the core of academic research that drives the technological and scientific advancement of the humankind. Data analysis is the basis of discoveries and new steps in every sphere of knowledge. This course helped me become more familiar with this complicated subject.
Discussing the areas of program evaluation at which I feel to be stronger, I would have to name qualitative data analysis and sampling. The latter area is connected with many ethical issues of research and data collection (Babbie, 2016). As a result, it needs to be approached carefully and thoughtfully. Moreover, sampling is a complex procedure. There exists a wide variety of different types of sampling strategies falling under the major categories of probability and non-probability. To be able to apply the correct strategy and to choose the appropriate size and type of the sample, a researcher is required to rely on a set of the essential basic calculations that comprise the foundation of the logic of sampling (Babbie, 2016). Also, a set of cautions exists regulating sampling strategies and providing guidance for the researchers to make the right decision and select a substantial sample.
When it comes to the analysis of data, I feel that I am stronger at processing and evaluating qualitative data. In particular, such strategies as semiotics and coding are the areas where I feel more confident than in the other areas. I believe that when it comes to the analysis of the data, one of the key goals of this activity is not only to generate new knowledge but also to present the findings in an accessible way ready for the use in the field. Qualitative data has a lot to do with the communication of the new information and its processing as a consumable piece of knowledge at every step of the research design. Otherwise, as it was mentioned by Chagnon, Pauliot, Malo, Gervais, and Pigeon (2010), the application of the newly generated knowledge can be complicated due to the utilisation challenges.
At the same time, alongside the areas of program analysis and evaluation, there are the ones where I do not feel as confident; they include the analysis of qualitative data (statistical analysis) and the application of scales and typologies. When it comes to the statistical analysis, I believe that it is one of the major areas that enable the applicability of the EBPs and the organisational and individual attitudes towards them (Aarons, Sommerfeld, & Walrath-Greene, 2009). In other words, in many areas, the numerical data is more convincing and effective than qualitative data. In turn, typologies, scales, and indexes are employed for the purpose to obtain original measures of the given data (Babbie, 2016). The aspect that produces an adverse impact on my confidence in this area is the likeliness that a scale of choice is going to distort the data instead of offering an appropriate interpretation.
To conclude, I have some aspects to work on and strengthen. I plan to improve my statistical analysis skills by researching different statistical tools and instruments and practising their application to various kinds of data. Also, I plan to read research studies based on descriptive analysis and statistics to improve my understanding of the use of scales and typologies and learn how to organise data more effectively without jeopardising its validity.
References
Aarons, G. A., Sommerfeld, D. H., & Walrath-Greene, C. M. (2009). Evidence-based practice implementation: The impact of public versus private sector organization type on organizational support, provider attitudes, and adoption of evidence-based practice. Implementation Science, 4(83), 113.
Babbie, E. (2016). The basics of social research (7th ed.). Belmont, CA: Cengage.
Chagnon, F., Pouliot, L., Malo, C., Gervais, M., & Pigeon, M. (2010). Comparison of determinants of research knowledge utilization by practitioners and administrators in the field of child and family social services. Implementation Science, 5(41), 112.
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