Quantitative Research

Quantitative research involves theories and empirical research. Scientific research consists of a dialogue between theory and empirism (Mamia, n.d). Descriptive research on what exist and explanatory research on why are types of research conducted by social scientist (Mamia, n.d). Often at work, management ask for ways to quantify work to establish performance measures. Expressing work in numeric form is always a plus for leadership beyond management to justify changes and problems that require attention.

To express something numerically, I naturally think of an equation or formula, with variables. In quantitative research, variables are observable and measurable characteristic of an observation unit, such as individual, group, event, etc (Mamia, n.d). Quantitative research incorporates a methodological process stating with an ideas, then formulating a problem, research questions and research design planning, etc. In many classes starting with high school chemistry, statistics and college work design classes, designed several assignments that require data collecting, understanding trends shown below and analyzing data that results in new research discoveries or aligns with existing research finding new characteristics using statistical analysis. I like to believe all quantitative research starts with exploring problems found in previous research, but its more apparent in the work environment I’m currently apart of and more accepting to reinvent the wheel.

Data Trends 

When I say reinvent the wheel, I mean to start from scratch when some scholars such as Baker (2012) would agree that technologies from one research may aid another’s research. In Baker’s (2012) article, it’s stated there is a growing assumption among researchers that data and computing resources should be readily reused, repurposed and extend by other scientists (p. 41). I’m not a scientist but in my experience I don’t see this same assumption growing among leaders in large corporations because they working groups, often in silos, remaining “insiders” to an issue that exclusively impacts their teams. I recently reached out a few “insiders” after hearing about a job performance measuring tool they use frequently. This sparked the interest of my management, more so they wanted to have their own tool. Only attracted to the glitter and glamour, there was no attempt to reason through criteria currently used in the measuring tool to repurpose for another team. Management proceed to call upon those to help develop a new tool for the team, and unfortunately I wasn’t apart of the “dream team”.

For personal awareness, I reached out to the creator to ask them about the tool and discovered they were about to make a drastic shift into a new tool unfamiliar to those within our regional location. I reached out to a few members of the “dream team” and received blank stares so I thought to pursue my discussion with the creator to see if someone from my team can sit in to observe, which the creator has mentioned inviting me but still has to send out the invite. The creator mentioned this project is funded by the information technology (IT) team for their group. For this particular situation, it will be useful to apply quantitative methods to repurpose existing criteria from the soon to be old tool and refine the criteria to be used in new tool to assist our group. At the moment, the manager insist on creating new criteria but without understanding how the existing data for the other group is used. I plan to continue learning how to incorporate quantitative research methods and design in to my current job role. This would likely require face to face interviews, and then statistical analysis against the existing criteria. 

Baker, M. (2012). Quantitative data: Learning to share. Nature Methods, 9(1), 39-41. doi:http://dx.doi.org.ezproxy.libproxy.db.erau.edu/10.1038/nmeth.1815

Mamia, T. (n.d). Quantitative Research Methods [PowerPoint slides]. PDF File