Data Analysis and Visualization Activity Guide

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Data Analysis and Visualization Activity
Due Sunday by 11:59pm Points 45Submitting a file uploadAvailable Jan 20 at 12am – Jan 26 at 11:59pm
Data & Analytics
Data Life Cycle
Data Visualizations
Data Storytelling
Data Analysis and Visualization Activity
Data & Analytics
Data is important, but it’s the way it’s presented that can bring about real change.
“Data science and data analytics have the potential to transform healthcare in many dimensions. Data science is the general process of converting raw data into actionable insights, meaning it offers a systematic approach to answering analytical questions to improve decision-making. Using analytical techniques, data science has the potential to provide powerful insights across and within patients, institutions, regions, and nations (Hardy, 2024, p. 358).”
Florence Nightingale was a pioneer in displaying data visually to promote sanitation reform. Learn more about her approach and the advantages of using data to promote change in the visual below:

Rubric
Data Analysis and Visualization
Data Analysis and Visualization
Criteria
Ratings
Pts
This criterion is linked to a Learning Outcome
Calculation of Mean Age
5 pts
Exemplary
Correct use of AVERAGE function and accurate calculation of mean age.
3 pts
Developing
Incorrect use of function leading to erroneous result.
0 pts
Does not meet expectations
Function not used or grossly incorrect calculation.
5 pts
This criterion is linked to a Learning Outcome
Calculation of Gender Distribution
5 pts
Exemplary
Correct use of COUNTIF function to accurately count males and females.
3 pts
Developing
Incorrect use of COUNTIF function, incorrect counts.
0 pts
Does not meet expectations
Gender distribution not calculated or grossly incorrect.
5 pts
This criterion is linked to a Learning Outcome
Calculation of Condition Prevalence
5 pts
Exemplary
Accurate use of COUNTIF for all conditions (falls, CAUTIs, medication errors, MRSA).
3 pts
Developing
One or mo.re conditions calculated incorrectly.
0 pts
Does not meet expectations
Major errors or conditions not calculated.
5 pts
This criterion is linked to a Learning Outcome
Creating a Pivot Table
5 pts
Exemplary
Pivot table correctly created with appropriate fields in Rows and Values.
3 pts
Developing
Pivot table has One or more errors in setup.
0 pts
Does not meet expectations
Pivot table not created or is unusable.
5 pts
This criterion is linked to a Learning Outcome
Percentage Calculation
5 pts
Exemplary
Correct calculation of percentages for each condition.
3 pts
Developing
Incorrect method used, affecting results.
0 pts
Does not meet expectations
Percentages not calculated or grossly incorrect.
5 pts
This criterion is linked to a Learning Outcome
Analysis of Data
5 pts
Exemplary
Clear, accurate analysis of data with insightful observations.
3 pts
Developing
Analysis is somewhat superficial or contains one or more errors.
0 pts
Does not meet expectations
Little to no analysis or analysis is incorrect.
5 pts
This criterion is linked to a Learning Outcome
Interpretation of Results
10 pts
Exemplary
Comprehensive and accurate interpretation of results with detailed summary.
5 pts
Developing
Interpretation is overly general or contains inaccuracies.
0 pts
Does not meet expectations
Little to no analysis or analysis is incorrect.
10 pts
This criterion is linked to a Learning Outcome
Data Visualizations
5 pts
Exemplary
Included accurate visualizations: pivot chart, bar chart, pie chart, line chart, and stacked column chart.
3 pts
Developing
Included inaccurate visualizations or are missing visualizations: pivot chart, bar chart, pie chart, line chart, and stacked column chart.
0 pts
Does not meet expectations
Did not submit visualizations: pivot chart, bar chart, pie chart, line chart, and stacked column chart.
5 pts
Total Points: 45
Data Visualizations
Data visualization is a powerful tool in nursing research, helping to make complex data more understandable and actionable.
Here are some key techniques used in this field:
1 Heat Maps: These are used to show the intensity of data points over a specific area, which can be particularly useful in identifying patterns in patient outcomes or the spread of diseases.
2 Scatter Plots: These plots help in identifying correlations between different variables, such as the relationship between patient age and recovery time.
3 Bar Charts and Histograms: These are commonly used to compare different groups or track changes over time, such as the effectiveness of different treatments.
4 Pie Charts: Although simple, pie charts can effectively show the proportion of different categories within a dataset, such as the distribution of different types of nursing interventions.
5 Box Plots: These are useful for displaying the distribution of data and identifying outliers, which can be critical in understanding variations in patient care.
6 Network Diagrams: These diagrams can illustrate relationships and interactions between different entities, such as healthcare providers and patients, or different symptoms and diagnoses.
7 Time Series Analysis: This technique is used to analyze data points collected or recorded at specific time intervals, helping to identify trends and patterns over time.
8 Geospatial Analysis: This involves mapping data to geographical locations, which can be particularly useful in public health nursing to track the spread of diseases or the availability of healthcare services.
Visualization Techniques for Nursing Research.pdf (nursingbigdata.org)
Links to an external site.
Digital Dashboards
Digital dashboards are another tool used to visually present data:
“Dashboard reporting systems play a significant role in reducing data fragmentation and in enhancing the communication of pertinent information. They are especially good at synthesizing “big data” into meaningful metrics that are needed to monitor and track quality indicators and performance metrics. Prior to the advent of clinical dashboards, an individual would be required to manually forage through the EHR in an effort to extract and aggregate insightful data needed to review health outcomes or measure health care delivery performance based on predetermined standards. Dashboards leverage visualization strategies to present data so that it is easily digested, reducing the cognitive burden needed to process the information (Khairat et al., 2018). Dashboard end users can include providers, care team members, administrators, researchers, and broader audiences with interests in public and population health, including disease surveillance. For providers, dashboards often serve as a means of automated audit and feedback, capable of summarizing one’s clinical performance as it relates to prescribing, screening, or performing recommended patient assessments (Burningham et al., 2020) (Hardy, 2024, p. 40).”
Example Performance Dashboards at Johns Hopkins:
5 Lessons for Creating Health Care Performance Dashboards | Voices for Safer Care (hopkinsmedicine.org)
Links to an external site.
https://nursingbigdatarepository.umn.edu/

5 Lessons for Creating Health Care Performance Dashboards

https://www.hopkinsmedicine.org/patient-safety
Data Storytelling
Data Storytelling, Lydia Hooper “Data Storytelling couples data visualization with compelling narratives that help audiences better comprehend and take action based on data analysis (Lydia Hooper, Data Storytelling: How to Tell a Story With Data – Venngage
Links to an external site.).”
Connecting data visualizations with stories brings facts to life.
“Storytelling with data in nursing is a powerful approach to enhance healthcare outcomes and patient care. By combining data with narrative elements, nurses and healthcare professionals can create compelling stories that provide context, highlight important issues, and drive meaningful change (Storytelling with Data in Healthcare
Links to an external site.).”
Data Storytelling:
1 Humanizes Data: Data storytelling goes beyond charts and numbers by adding context, history, and emotion. This approach helps to see the larger story behind the data, and the focus on the patient.
2 Builds Trust: By integrating stories with data, healthcare providers can build trust among care teams and patients.
3 Creates Meaning: Storytelling helps to keep people at the center of healthcare decision-making. It ensures that the personal and emotional aspects are considered along with the data, making the information more relatable and actionable.
4 Educational Tool: Stories can educate patients, families, and communities about health issues. They can also be a powerful tool for advocacy and policy-making.
5 Innovation and Design: In nursing, storytelling is used in innovation and design processes. Storytelling can help stakeholders connect with healthcare innovations and solutions.
How Data Storytelling Fuels Better Healthcare Outcomes (healthcatalyst.com)
Links to an external site.
Rubric
Data Analysis and Visualization
Data Analysis and Visualization
Criteria Ratings Pts
This criterion is linked to a Learning Outcome Calculation of Mean Age
5 pts
Exemplary
Correct use of AVERAGE function and accurate calculation of mean age.
3 pts
Developing
Incorrect use of function leading to erroneous result.
0 pts
Does not meet expectations
Function not used or grossly incorrect calculation.
5 pts
This criterion is linked to a Learning Outcome Calculation of Gender Distribution
5 pts
Exemplary
Correct use of COUNTIF function to accurately count males and females.
3 pts
Developing
Incorrect use of COUNTIF function, incorrect counts.
0 pts
Does not meet expectations
Gender distribution not calculated or grossly incorrect.
5 pts
This criterion is linked to a Learning Outcome Calculation of Condition Prevalence
5 pts
Exemplary
Accurate use of COUNTIF for all conditions (falls, CAUTIs, medication errors, MRSA).
3 pts
Developing
One or mo.re conditions calculated incorrectly.
0 pts
Does not meet expectations
Major errors or conditions not calculated.
5 pts
This criterion is linked to a Learning Outcome Creating a Pivot Table
5 pts
Exemplary
Pivot table correctly created with appropriate fields in Rows and Values.
3 pts
Developing
Pivot table has One or more errors in setup.
0 pts
Does not meet expectations
Pivot table not created or is unusable.
5 pts
This criterion is linked to a Learning Outcome Percentage Calculation
5 pts
Exemplary
Correct calculation of percentages for each condition.
3 pts
Developing
Incorrect method used, affecting results.
0 pts
Does not meet expectations
Percentages not calculated or grossly incorrect.
5 pts
This criterion is linked to a Learning Outcome Analysis of Data
5 pts
Exemplary
Clear, accurate analysis of data with insightful observations.
3 pts
Developing
Analysis is somewhat superficial or contains one or more errors.
0 pts
Does not meet expectations
Little to no analysis or analysis is incorrect.
5 pts
This criterion is linked to a Learning Outcome Interpretation of Results
10 pts
Exemplary
Comprehensive and accurate interpretation of results with detailed summary.
5 pts
Developing
Interpretation is overly general or contains inaccuracies.
0 pts
Does not meet expectations
Little to no analysis or analysis is incorrect.
10 pts
This criterion is linked to a Learning Outcome Data Visualizations
5 pts
Exemplary
Included accurate visualizations: pivot chart, bar chart, pie chart, line chart, and stacked column chart.
3 pts
Developing
Included inaccurate visualizations or are missing visualizations: pivot chart, bar chart, pie chart, line chart, and stacked column chart.
0 pts
Does not meet expectations
Did not submit visualizations: pivot chart, bar chart, pie chart, line chart, and stacked column chart.
5 pts
Total Points: 45

Struggling with where to start this assignment? Follow this guide to tackle your assignment easily!

If you’re unsure how to approach the Data Analysis and Visualization Activity, this guide will help you break it down into manageable steps.

1. Understand the Requirements

The activity involves analyzing data related to healthcare and visualizing it effectively. You will be using several functions in Excel, creating pivot tables, calculating percentages, and interpreting the data to showcase your findings. Make sure you are familiar with the different types of data visualizations mentioned in the assignment, such as heat maps, scatter plots, pie charts, and bar charts.

2. Gather the Data

Before you start, make sure you have access to the data you will be working with. This could include age, gender, health conditions (like falls or CAUTIs), and other relevant data. This information will be essential for your calculations and visualizations.

3. Step-by-Step Calculation

Mean Age: Use the AVERAGE function in Excel to calculate the mean age of your data set. Ensure the formula is correctly applied to the relevant column of ages.
Gender Distribution: Use the COUNTIF function to count how many males and females are in the dataset.
Condition Prevalence: Apply COUNTIF to calculate the prevalence of each condition (falls, CAUTIs, medication errors, MRSA).
Pivot Table: Create a pivot table in Excel. Add appropriate fields for Rows and Values to organize and summarize your data effectively.
Percentage Calculation: Calculate the percentage for each condition by dividing the count of each condition by the total number of cases.

4. Analysis of Data

Once you have completed the calculations, analyze your findings. What do the numbers tell you about the healthcare data? For example, are certain conditions more prevalent in specific genders or age groups? What insights can you draw from the data in terms of patient care or healthcare outcomes?

5. Interpret the Results

Provide a comprehensive interpretation of the results. Highlight the key findings, trends, and insights. Discuss how these results could impact healthcare practices, patient outcomes, or policy decisions. Make sure your interpretation is clear and detailed, addressing all relevant aspects of the data.

6. Create Data Visualizations

You will need to create several data visualizations:

Pivot Chart: Create a chart based on the pivot table you created.
Bar Chart: Use this to compare data, such as gender distribution or condition prevalence.
Pie Chart: Use this to show proportions, such as the percentage of patients with each condition.
Line Chart: This can be used to show trends over time, if applicable.
Stacked Column Chart: This chart type is useful for showing how different categories contribute to a total.

Make sure each visualization is accurate, clear, and relevant to the data you are analyzing.

7. Review and Finalize Your Work

After completing all the calculations, visualizations, and analyses, review your work. Ensure that all functions (AVERAGE, COUNTIF, etc.) have been used correctly and that your data visualizations are clear and accurate. Double-check your interpretation of the results to make sure it aligns with the data you’ve presented.

8. Submit Your Assignment

Once you’re confident in your work, save your file and submit it as per the instructions in your assignment.

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