Reference no: EM132892478

Overview

Murphy's law, "If it can go wrong, it will," can also be applied to research. Because we're only human and prone to making errors, it's helpful to document our research process with supporting documentation. Supporting documentation can include codebooks, lessons-learned documents, project management files, regulatory documents, and so on. Creating your own supporting documentation for data is an iterative process that may be done throughout the entire research process-both before you gather data and while you are gathering it-as you are likely to discover new things along the way.

The benefits of supporting documentation are multifold:

• It can help us keep track of the steps we took during research.
• It can allow us to summarize what worked and what did not.
• It can help others (such as supervisors, clients, and stakeholders) to understand our processes, such as how we handled the data.

As a data analyst for Vision Analytics, you previously diagnosed a problem for your client. In addition to the primary data set, the client has shared a codebook with you. This assignment will give you an opportunity to review the codebook associated with the data set and revise the codebook so it includes only information that is relevant to addressing the organizational problem.

Please note: In Project Three (due in Module Seven), you will submit a revised codebook of the data.

Reference

Khurana, S. (2019, July 30). 10 versions of Murphy's Law for universal truths. Retrieved from https://www.thoughtco.com/murphys-laws-explain-unfathomable-truths-2832861

Prompt

• Begin by identifying the client you decided to work with for the course projects.
• For context, be sure to include at least two examples of the research questions you plan to test to address their organizational problem.

Consider the data analysis requirements to test these research questions, including which variables of interest (for quantitative data) or units of analysis (for qualitative data) you will need to analyze and address the problem.

Review the codebook that aligns to your client's problem. The codebooks include information about the organization's data set from a relevant database.

• Western Forest Service: Forest Cover Type Data Set Codebook
• La Banca Central Bank: Synthetic Financial Data Set Codebook for Fraud Detection
• Create a revised codebook of the data you will use from the data analysis requirements you have identified as aligning to your research questions. Be sure to only include the essential information as it relates to your research questions.
• Describe your process for revising the codebook. Consider the following:
• Is the data clearly defined? Does it align to your research questions?
• Did you adjust any of the naming conventions? Why?
• Would another researcher not associated with the project be able to understand the codebook? Is there any information you need to add to ensure the data is transparent?
• Are there any ethical considerations you need to be mindful of, such as privacy issues, anonymity, or bias?