Using the topic and research question you developed, you will design a quantitative instrument that could potentially answer your topic/research question if it were to be applied to a quantitative 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. Topic : Enhancing Cyber Security In Healthcare -With The Help Of Machine Learning 1.Your research question in the form of a quantitative question (if it was not already). 2.An instrument or protocol (survey, questionaire, archival data, etc) that could be used to answer the quantitative version of your research question. *Special note for those using archival data, you will describe the process of data retrieval for your archival data. See examples to help. 3.A one paragraph description/justification of how your chosen instrument/protocol is the best choice for answering the quantitative version of your research question. 500 words apa format with references needed

Research Question: What is the impact of using machine learning algorithms on enhancing cyber security in the healthcare industry?

Quantitative Research Question: To what extent do machine learning algorithms contribute to enhancing cyber security in the healthcare industry?

Instrument: Survey

Description/Justification:

The chosen instrument to answer the quantitative version of the research question is a survey. A survey is a structured method of collecting data from a sample population to obtain specific information about their opinions, beliefs, attitudes, or behavior. In the context of this research question, a survey will allow us to gather data on the extent to which machine learning algorithms are used and their impact on enhancing cyber security in the healthcare industry.

The survey will consist of a set of close-ended questions that participants can respond to by selecting their answers from a predefined list of options. The questionnaire will be designed to measure several key variables related to machine learning algorithms and cyber security, such as the types of machine learning algorithms used, the level of implementation in healthcare organizations, and the perceived effectiveness in enhancing cyber security.

The advantage of using a survey as the instrument for this quantitative study is that it allows for a large-scale data collection from a diverse sample, thereby increasing the generalizability of the findings. By administering the survey to various stakeholders in the healthcare industry, including IT professionals, security analysts, and healthcare providers, we can obtain a comprehensive understanding of the use of machine learning algorithms and their impact on cyber security.

Additionally, a survey provides a systematic and standardized approach to data collection, enabling researchers to analyze the responses using statistical techniques. The quantitative data obtained from the survey can be subjected to statistical analysis, such as correlation analysis, regression analysis, or hypothesis testing, to determine the relationship between the variables and answer the research question.

The survey instrument will be designed to ensure reliability and validity. Reliability refers to the consistency and stability of the instrument, and it can be ensured through the use of clear and unambiguous questions, proper sequencing of items, and pilot testing. Validity refers to the extent to which the instrument measures what it intends to measure. To establish validity, the survey will be constructed based on a thorough review of existing literature and consultation with experts in the field.

In conclusion, a survey instrument is the most appropriate choice for answering the quantitative version of the research question on enhancing cyber security in the healthcare industry with the help of machine learning algorithms. It allows for the collection of data from a large and diverse sample, and its structured nature facilitates statistical analysis for determining the impact of machine learning algorithms on cyber security.