Qualitative Data Collection

Qualitative Data Collection Instrument

Overview: Using the topic and research question you developed in week 1, you will design a qualitative instrument that could potentially answer your topic/research question if it were to be applied to a qualitative study. Keep in mind, this may take some stretching if you wrote your question leaning quantitatively. The purpose here is not to box you in but to ensure that you have a solid understanding of both methodologies.

Directions:

You will develop a word document to include:

1. View the rubric to make sure you understand the expectations of this assignment.

a. DSRT 837 Rubric Adapted from Doctoral Research Handbook.docx

2. Your research question in the form of a qualitative question (if it was not already).

3. An instrument or protocol (interview, ethnography, focus group protocol, etc) that could be used to answer the qualitative version of your research question.

4. A one paragraph description/justification of how your chosen instrument/protocol is the best choice for answering the qualitative version of your research question. This should include citations from the literature to support justification.

Week1 Research Question:

Using Data Science Techniques To Enhance Data Security in SMBs

Data science is being embedded into cyber security and data security. It is being used to identify the patterns of past attacks and predict the potential risks within the framework of the system. Machine learning is highly used in analyzing large data sets to find the patterns that spot an attack.

Due to the high licensing cost and contracts, many small and medium-scale businesses use open-source tools and applications. This tends to put these organizations in harm’s way. Due to the low volume of data or fewer users in the organization, the management will choose open-source software tools, which sometimes have fewer security protocols and high exposure to data breaches and security threats.

Research Questions:

· What drives SMBs to choose open-source technologies?

· How safe are these open-source data science technologies?

· What measures can be taken to aid SMBs in using open-source data science tools to protect their data better?