Research Methods & Professional Practice

It can be agreed upon that human beings are curious in nature, willing to explain encountered phenomena, and love to explore. When it comes to science, however, this curiosity is controlled with a well-known methodology called the Scientific Method (Hepburn & Andersen, 2015). One of the exciting things I learned in this unit is that one of the components of the scientific method is reasoning which can be deductive or inductive. Both are essential types of reasoning, but inductive reasoning is closer to real-world problems where curiosity or “Observation” is the first loop in the chain of reasoning (Miessler, 2020). One benefit I can take from that and relate to the field of computer science is that solutions to problems may not always be through using established theories, tools, and techniques. Instead, it may need to start from observation. In other words, thinking out of the box. Nevertheless, such solutions may need to be backed again by hypothesis testing to have scientific validity.

The first step in the scientific method is formulating a research question. This question should be chosen carefully as it matters more than knowing the correct answers (Phillips et al., 2007). I believe that during the process of formulating a research question, researchers get a deeper look at the way they think about a problem or observation, which helps them stay focused on what matters most.

After formulating a research question, the next step is to perform a literature review (survey), an iterative process that will get the researcher closer to the required focus with each iteration (Dawson, 2015).

Next comes the research design, which is a blueprint for the research project that includes all the necessary details related to data gathering and analysis (BUSINESS RESEARCH METHODOLOGY, n.d). I learned about different research methods that act as a tool for the researcher to collect data. Research can be qualitative or quantitative. Quantitative methods use measurable variables that can be computed and statistically analyzed. Examples are experiments, questionnaires, and systematic reviews. The latter summarizes existing evidence on a topic, often including quantitative analysis.

On the other hand, qualitative methods are concerned with expanding the knowledge about a subject without the attempt to quantify its factors. Examples are action research, case studies, focus groups, observations, and qualitative surveys or interviews (Dawson, 2015; Kaplan & Maxwell, 2005; Ralph et al., 2020).

Questionnaires are an interesting research method type because they can provide quantitative and qualitative data in a reasonably short time (Anon, 2022a). However, I realized that it could be utilized in unethical ways. One example is called Sugging which is the topic I prepared for one of the reflective activities.

Research is unreliable if it can’t be replicated by other researchers, and its findings can be of limited use if they can’t be applied to similar situations  (Anon, 2022b). It is fascinating to realize such facts. I can think of reliability as distributed proof of the validity of a particular research endeavor. i.e., when a research result methods and results are replicable by other researchers, this indicates that the methodology and implementation of the original idea were valid, not tampered with, and can be applied to different situations.

In this module, I had applicable knowledge with practical exercises that helped formulate a deeper understanding of the inferential statistics topic. I had to opportunity to explore the DCOVA framework for defining (D), collecting (C), organizing (O), visualizing (V), and analyzing (A) data to get valuable insights (Berenson et al., 2015). Also, I learned that accepting a null hypothesis doesn't mean its absolute correctness but the absence of an adequate amount of evidence to warrant its rejection. (Berenson et al., 2015). This, in my opinion, brings us back to the importance of reliable and valid research to accumulate the necessary evidence to answer important questions.

Extracting meaningful insights from quantitative data can be difficult without adequate visualization. It enhances the comprehension and communication of results (Embarak, 2018). In this module, I had a chance to practice constructing bar charts and histograms which increased my understanding of the topic and my appreciation of the importance of data visualization.

Qualitative data analysis takes a different route without statistical analysis. This process is focused on extracting themes from data and synthesizing meaning with the help of standard software packages like word processors or more specialized software made for that purpose (Learning for Action, n.d). It was my first time exploring how to analyze qualitative data systematically. This helped me appreciate this kind of research that I may consider in the future.

Research consumes time in its planning and implementation. Also, it is meaningless if it can’t be shared with others (Dawson, 2015). I realized in this module the importance of acquiring the skills needed for researchers to publish their work. Those include good planning, time management, and project management skills, which are not easy to master but require practice and repetition. I had the opportunity to watch a TED Talk about the mind of procrastinators (Urban, 2016). This talk helped me to think about the goal of implementing a project rather than focusing on small steps that can be boring and less motivating.

Although an ePortfolio can be a daunting task to start with, it becomes easier if done gradually. I was not able to upload materials regularly during the modules. However, I documented my artifacts and the notes I took during the course. This proved to be handy.

I also learned that ePortfolios could serve several purposes, including learning, self-assessment, and employment (Miller & Morgaine, 2009; Moon, 2004; Weber, 2018). In addition, I believe that the process of ePortfolio teaches students discipline and time management skills too.

At the end of the module, I learned that projects might have different levels of complexity. With increased complexity comes the need for more structured project management. Learning about the “Hedgehog Syndrome” was interesting, where mistakes are repeated without an effort to reflect on previous mistakes (Maylor, 2010). I am planning to start my own software project in the next few months, for which I am eager to apply the knowledge I learned about project management and risk assessment. However, it will also be challenging as I must balance between being realistic and motivated.

References

Anon (2022a) Interviews and Survey Design [Lecturecast]. RMPP PCOM7E January 2023 Research Methods and Professional Practice January 2023. University of Essex Online.

Anon (2022b) Validity and Generalisability [Lecturecast]. RMPP PCOM7E January 2023 Research Methods and Professional Practice January 2023. University of Essex Online.

Berenson, M. L., Levine, D. M. & Szabat, K. A. (2015) Basic Business Statistics: Concepts and Applications. 13th global edition ed. Harlow, UK: Pearson Education.

Available from: https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1419545 [Accessed 27 March 2023].

Business Research Methodology (n.d) Research Design - Research Methodology. Available from: https://research-methodology.net/research-methodology/research-design/#_ftn2 [Accessed 7 February 2023].

Cast (n.d) Risk Management in Software Development and Software Engineering Projects. Available from: https://www.castsoftware.com/glossary/software-risk-management [Accessed 10 April 2023].

Dawson, C. W. (2015) Projects in computing and information systems : a student's guide. Third edition. ed. Harlow, England: Pearson.

Available from: the Vitalsource Bookshelf [Accessed 3 February 2023].

Embarak, O. (2018) The Importance of Data Visualization in Business Intelligence. United States: Apress L. P.

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Hepburn, B. & Andersen, H. (2015) Scientific method. Stanford Encyclopedia of Philosophy -).

Kaplan, B. & Maxwell, J. A. (2005) Qualitative Research Methods for Evaluating Computer Information Systems. 2nd ed. New York, USA: Springer.

Available from: https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=264813.

Learning for Action (n.d) Analyzing Qualitative Data. Available from: http://learningforaction.com/analyzing-qualitative-data [Accessed 27 March 2023].

Marichetty, U. K. (2017) The Use of Effective Risk Management in Cloud Computing Projects. Harrisburg University of Science and Technology.Available from: https://digitalcommons.harrisburgu.edu/pmgt_dandt/23/.

Maylor, H. (2010) Project Management. 4th ed. Harlow, UK: Pearson Education.

Available from: https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=5173571 [Accessed 16 April 2023].

Miessler, D. (2020) The Difference Between Deductive and Inductive Reasoning | Daniel Miessler. Available from: https://danielmiessler.com/blog/the-difference-between-deductive-and-inductive-reasoning/ [Accessed 31 January 2023].

Miller, R. & Morgaine, W. (2009) The benefits of e-portfolios for students and faculty in their own words. Peer review : emerging trends and key debates in undergraduate education 11(1): 8.

Moon, J. A. (2004) A Handbook of Reflective and Experiential Learning Theory and Practice. United Kingdom: Taylor & Francis Group.

Phillips, E. M., Pugh, D. S., Bartlett, A. & Lewis, J. (2007) How to get a PhD: a handbook for students and their supervisors.

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Ralph, P., et al. (2020) ACM SIGSOFT Empirical Standards. Available  [Accessed 23 February 2023].

Urban, T. (2016) Inside the Mind of a Master Procrastinator. Available from: https://www.ted.com/talks/tim_urban_inside_the_mind_of_a_master_procrastinator?language=en [Accessed 17 April 2023].

Weber, K. (2018) Employer Perceptions of an Engineering Student’s Electronic Portfolio. International Journal of ePortfolio 8(1): 57-71.