As a consumer of research reports, the most important things for me in the methods and results section of a high-quality research report are the research design, the participants and data collection. The following are my reasons;

 

Research design

The research design could be likened to an architectural plan. In the domain of building construction, the architectural plan defines everything – cost, quality, usability, aesthetics and much more. In a similar fashion, the research design sets the stage for everything that follows in the methods and results section.

The research design should be relevant to the question(s) the researcher(s) is/are trying to find answers. It should be consistent with the study intent, and how data would be gathered and analysed.

 

 Participants and data collection

In my opinion, this remains the heart of a quantitative study. To illustrate this further, imagine gathering data about the world’s wealthiest in monetary value, from a sample of participants having a net worth below one million Canadian dollars. If this weren’t misleading enough, you can only expect that the results from such data analyses, would be both misleading and meaningless – a waste of everyone’s time!

This underscores the importance of having the right sampling strategy, sample size, reliable and valid data gathering instruments, and methods. Furthermore, the results from the data analysis would be of value only if relevant data was gathered from the right sample.

 

Question:

What role do software tools like python programming, hadoop, machine learning or others and others play in the area of data analysis? Specifically, how or where do such tools come into the process?

I stumbled into a blog on data science and the information I read therein is what has informed my question. I also noticed from the study I reviewed that only SPSS was used to analyse data.

 

Link to question : https://create.twu.ca/ldrs591-sp18/unit-5-learning-activities/