Word Count : 600 words
Learning Outcomes Assessed:
- Critique original research data sets relevant to their field of study selecting appropriate statistical methods
- Discuss the relevance, validity and reliability of statistical methods in the context of experimental design
- Evaluate and interpret scientific information and data, both qualitative and quantitative, relevant to applications of their subject area
The effectiveness of Covid-19 vaccination has been studied. First, prior to it being approved through clinical trials. However, with the rolling out of the vaccination by countries, the effectiveness studies have moved outside of lab-controlled environment.
While the vaccination cannot cure Covid-19, the use of it is linked to some level of prevention and in reduction in more severe symptoms such as hospitalisation and death.
As of now two major studies have been conducted to measure the real-world effectiveness of the vaccines and compared the data from clinical trial results. Both studies have shown that the real world roll out can mirror the same result obtained during clinical trials. The studied vaccines performed well in preventing severe covid-19 and deaths in adults.The data presented to you are taken from United Kingdom during the peak of second wave of Covid-19. As UK’s peak approached, we became one of the first few countries to roll out the use of vaccine. Two separate data sets are given where:
- The first data set related to the onset of the peak and roll out of the vaccine in a small scale.
- The second data set represent the peak of the second wave and smooth roll out of vaccines to more vulnerable adults.
Your task is to analyse the study data and write your recommendations for the director of NHS in a short, non-technical report (approx. 600 words only) on how effective vaccine was in preventing deaths during the peak of second wave. Your results should be clear enough to allow the director to understand the role vaccine in preventing Covid-19 related death in the UK.
Whatever your recommendations are – they must be clearly supported by your calculations using the supplied study data.
You should include a full numerical summary of the data in the study. You should include an explanation of the factors, treatments and any lurking variables which may be present in the study
and any future research. You should highlight any possible errors in measurement, data anomalies or outliers and describe their effect on your conclusions. You should describe any correlation between the variables, if present.
You should summarize any findings in the form of clear, supported recommendations that allow the Director to decide policy for the future.
Any calculations must only use the methods and techniques described to you on this course.
You must produce a maximum of 2 graphical representations of the given data from the following options:
2. Box Plot
3. Modified Box Plot
4. Cumulative Relative Frequency
[For example, you may choose: 1 Box Plot and 1 CRF – or – 1 Modified Box Plot and 1 Histogram but no more than 2 in total]
You must also conduct the following two tests (with the given limits in brackets).
1. Linear Regression (with a maximum of 2 correlations, 2 scatter plots and 2 residual plots)
2. 2 Sample Confidence Interval (with a maximum of 2 [2 sample] intervals)
a. Note: depending on circumstance or purpose, there is no ‘best’ confidence level, your choice of confidence level must be justified based on your purpose.
You should comment on the validity and meaning of any test you conduct in the context of the test result.
All calculations and statistical tests must be conducted using Microsoft Excel. Use of any other software is not permitted.
After submission, any spreadsheets you use (your .xls or .xlsx files) must be available to your tutor upon request however your report only requires the output graphs and tables printed within a Microsoft Word (.docx) file, in an Appendix.
Your investigation should be presented in two main sections:
1: You should include a full numerical summary of the data in the study. You should include an explanation of the factors, types and any lurking variables which may be present in the study and any future research. You should highlight any possible errors in measurement, data anomalies or outliers and describe their effect on your conclusions. You should describe any correlation between the variables, if present.
Whatever your recommendations are – they must be clearly supported by your calculations using the supplied study data
2: Your task is to analyse the study data and present your recommendations
You should summarize any findings in the form of supported recommendations. The clarity of these recommendations and their reasons are paramount. Therefore, you should not use any statistical ‘jargon’ in your recommendations or in your justification for your recommendations.
Referencing style: Not applicable