Assignment 4: Board Presentation

Assignment 2: Board Brief Part B- Data Analysis

LaCita Dedeaux

Doctor Bruce Smith

JWI Business Analytics and Capstone

Saturday, November 7, 2020

Data Sources and Reliability

The most critical to the analysis of the problem/opportunity identified in Assignment 1.

In the first assignment, the problem identified is the Rebranding of patient advocacy. There were

many critical points that I had come across while doing this assignment because I encounter issues

in data collection from the required audience. The collection of data from the selected population

decide the success and failure of the research. If anyone fails in collecting data, someone will

forget the aim of conducting the investigation, and the collection of data in this critical topic of

patient advocacy is essential (Cristina Davino, 2012 ). Because the collection of data is not only

from the patient's side, but the role of internal customers is also essential. Therefore, the collective

presentation of data in a required time appears to be the most critical part of the assignment due to

bad timing.

Sources that are used in Data Gathering

The sources that I use in the collection of data is both primary and secondary data. From the

secondary data collection, the items are collected to design a questionnaire that will help

accomplish the study's aim. Subsequently comes to the primary data collection in which first-hand

data is collected from the desired population. Therefore, the sources used are private hospitals

where I can easily access the patients, doctors, paramedical staff, and administrative staff who

benefit from patient rebranding advocacy.

Reliability of data and steps that can take for its validation

The validity and reliability of data cover the authenticity of the data. Therefore, I collect the data

after proper screening. Before the final questionnaire, I add a screening test that validates the data

only required person from the selected population will fill the form (Winnie Daamen, 2014). This

technique helps me save a lot of time, and it also helps validate that required data comes from the

desired population.

Unknown data and its impact on the analysis

As mentioned in the above question, a screening foam is present before the final questionnaire. I

calculate how many veterans are approached that did not come under the description of the

required population. In the end, I get the statistics of how many veterans I approached and how

many of them are the actual person needed. However, the statistics did not negatively impact my

analysis. Still, it supports me the probability of how many veterans I approached, and out of them,

the ratio of actual veterans affects the data.

Data Tools and Analysis

Drawing from the “Types of Analysis” guide and other resources from the course, what tools

and techniques that are used in the analysis

In the analysis section, I use the tools and techniques as per my study. I figured out three to four

correlation methods, descriptive statistics, and regression from the course material. Using this

technique, I select correlation because I want to find the relationship between the variables I want

to test. There are four variables of my study, and the correlation test helps a lot in evaluating

whether there exists a positive correlation or negative correlation. The second test that I use is

regression. Through regression analysis, I want to test my hypothesis for the Rebranding of patient

advocacy. Through course material, I evaluated that it is the right way to validate the idea. Lastly,

the descriptive statistics help me assess my study's audience and the maximum and the minimum

number of responses they gave.

Applying the analysis tools to data and explore the patterns, trends, and anomalies that are

still uncovering

After applying the desired tools, I only look forward to those points that I am looking forward to,

but there are many other points that I can also use from the data collected. For example, if a

particular hypothesis is not accepted, I will analyze the factor behind it that leads to the not

acceptance of this hypothesis. It will further help me uncover other points that need to be covered,

and it might lead me to explore the hidden factors of the study.

Patterns, trends, and anomalies shed light on the problem/opportunity identification:

From the analysis, the pattern that I found helps me uncover some of the hidden facts that the

Rebranding of patient advocacy helps a lot as it improves hospitals and other paramedical staff. It

also allows me to explore further how data accuracy can be assured because it is the only flaw I

find in this study. If data accuracy can be ensured, it will ultimately help develop big data to

eventually resolve significant medical issues. This data accuracy allows me to explore what is the

question that leads to data inaccuracy. Hence, if this problem is examined, it can resolve that

further enhance patient advocacy.

Charts that support the data

These are the four significant hypotheses of the study whose information is shown in an improved

understanding graph.

Rebranding of patient advocacy enhances the level of services.

Strongly Agree

Agree

Neutral

Disagree

Strongly Disagree

0 5 10 15 20 25

The graph mentioned above shows that the Rebranding of patient advocacy enhances services as

more than 20 respondents agree with this statement.

Rebranding of patient advocacy enhances the satisfaction level of patients.

Strongly Agree

Agree

Neutral

Disagree

Strongly Disagree

0 5 10 15 20 25

Chart Title

The graph mentioned above shows that the Rebranding of patient advocacy enhances patients'

satisfaction level as more than 20 respondents agree with this statement.

Rebranding of patient advocacy improves data accuracy.

Strongly Agree

Agree

Neutral

Disagree

Strongly Disagree

0 5 10 15 20 25

Chart Title

The graph mentioned above shows that the Rebranding of patient advocacy improves the data

accuracy as more than 20 respondents disagree with this statement.

Rebranding of patient advocacy improves the response rate of doctors and healthcare staff.

Strongly Agree

Agree

Neutral

Disagree

Strongly Disagree

0 5 10 15 20 25 30

Chart Title

The graph mentioned above shows that the Rebranding of patient advocacy improves doctors'

response rate, and healthcare staff agrees with this statement.

Conclusion and Action Items

Concluding the findings of my data analysis in a nutshell, I analyzed that regression analysis, and

correlation is beneficial in studying data as it gives a better insight into data. But still are many

points that need to be improved. For example, along with regression analysis, the T-Test can also

be used to test the hypothesis. For the better presentation of data, histograms can also be made so

that the curve will tell what to do for better performance.

References

1.

Cristina Davino, L. F. (2012 ). Survey Data Collection and Integration. Springer Science & Business Media,.

2. Winnie Daamen, C. B. (2014). Traffic Simulation and Data: Validation Methods and Applications. CRC

Press .