Analyzing & Visualizing Assignment 1 and discussion 3
Running head: SOCIAL VULNERABILITY INDEX 1
SOCIAL VULNERABILITY INDEX 3
Analyzing & Visualizing Data (ITS-530-05)
University of the Cumberlands
Dr. Kathy McClure
I. Exclusion of Metrics in Predicting Social Vulnerability Index
· Makes it hard for the CDC to calculate the level of aid required
· The subjects being studied may not give enough detail on language limitation
· Assessment of the vulnerability of a community in case of a disaster requires more well-defined metrics.
Every community needs independent evaluation to find out the vulnerability of the said area. Two of the contributing factors for this analysis are language limitations and minority status. The information may lack credibility because of the fear of reprisal for individuals that fall into these categories. Exploration of the physical and social characteristics, excluding these metrics, can offer insights on the general impact of the Social Vulnerability Index.
The methods utilized to gather information for addressing the research problem and the questions that stem from it will be qualitative. The reports released by the CDC in the year 2018 will play an important role in helping the researcher get the right information.
The results of the study will be achieved by coding and tallying. The researcher will source data from different reports and attempt to find similarities in what challenges the exclusion of metrics pose in the determination of community SVIs.
The results will be helpful in assisting future researchers/stakeholders within and without the CDC to use different skills so as not to exclude important data set aspects.
VI. Recommendations for Future Analysis
The recommendations that this research will propose to revolve around using the most significant possible samples and the blinding of respondents. In this way, it will be simpler to ward off the possibility of respondent bias. In addition to that, statistical validity will also be guaranteed.
The conclusion section of the paper will summarize the content of the entire paper from the introductory section to the recommendations section.