Data Analytics
DNP 830 Topic 2 Discussions and Data Analytics
DNP 830 Topic 2 Discussions and Data Analytics
Topic 2 DQ 2
Discuss why quantitative method is the best method based on your project questions and data. Choose three potential designs that you could use for your project. Based on the three potential designs, determine potential analyses methods and why?
Topic 2 What Are the Data Saying?
General Requirements:
The DNP must have a basic understanding of statistical measurements and how they apply within the parameters of data management and analytics. This assignment will allow you to demonstrate your understanding of basic statistical tests and how to choose the appropriate test for the study being performed. You will also discuss the reliability and validity factors associated with the data sources used.
Use the following information to ensure successful completion of the assignment:
- Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
- This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
- You are required to submit this assignment to LopesWrite. Please refer to the directions in the Student Success Center.
Directions:
The purpose of this assignment is to write a paper including a comparison table. The table will be provided as an appendix to the final paper.
Using the GCU Library, locate a quantitative, qualitative, and mixed methods article related to your DPI topic. You may reuse articles from Topic 1 assignment.
Using the “Comparative Table Template,” create a table that compares quantitative, qualitative, and mixed methods articles. Provide the table as an appendix to the paper.
Organize the table according to the following.
- Type of article.
- Title of the article.
- Type of analysis: Describe the process of analysis. There can be more than one.
- Applicability of test: Describe other analyses that could be used and why?
- Reliability and validity: Describe either the reliability and validity measures of tools used in the study or the techniques used to enhance reliability and validity in the study.
Write a 1,000 to 1,250 word paper discussing the application of data analysis, reliability and validity, and assorted statistical tests used in health related research. Include the following in your paper:
- State the types of study used in each article.
- Discuss the types of statistical tests used within each article and why they have been chosen.
- Discuss the applicability of the chosen statistical test and why the statistical test was chosen.
- Discuss the differences between parametric and nonparametric tests and how they were applied in the articles you chose.
- Evaluate how the factors of reliability and validity are accounted for in the articles (of instruments and surveys or in the design and data collection conducted).
- Summarize how the chosen studies could be applied within the context of your practice.
Portfolio Practice Hours:
Practice immersion assignments are based on your current course objectives and is application based learning using your real-world practice setting. These assignments earn practice immersion hours and are indicated in the syllabus by a Portfolio Practice Hours statement, which reminds you, the learner, to enter in a corresponding case log in Typhon. Actual clock hours are entered, but the average hours associated with each practice immersion assignment is 10.
You are required to complete your assignment using real-world application. Real-world application requires the use of evidence-based data, contemporary theories, and concepts presented in the course. The culmination of your assignment must present a viable application in a current practice setting. For more information on parameters for practice immersion hours, please refer to DNP resources in the DC Network.
To earn portfolio practice hours, enter the following after the references section of your paper:
Practice Hours Completion Statement DNP-830
I, (INSERT NAME), verify that I have completed (NUMBER OF) clock hours in association with the goals and objectives for this assignment. I have also tracked said practice hours in the Typhon Student Tracking System for verification purposes and will be sure that all approvals are in place from my faculty and practice mentor. DNP 830 Topic 2 Discussions and Data Analytics.
Applications of data analysis
Introduction
Data analysis is a process that can help us understand human behavior, predict the future, and understand the past. Here are some of its main uses:
Data analysis can help detect patterns in human behavior.
Data analysis can help you understand the past and predict the future. It can also be used to understand human behavior, which is one of the most important uses for data analysis.
For example: If you want to know what people do when they’re out on their own in a public place like an airport or park, this is where we would use data analysis. This allows us to identify patterns in our observations that we didn’t notice before as well as make predictions about what might happen next based on those patterns (for example, if there’s someone who always goes into McDonalds then he/she might buy something).
Data analysis can help predict the future.
Data analysis can help predict the future. The ability to make predictions based on data analysis is one of the most important applications of data analytics, and it’s why we’re so excited about what’s coming next!
Predicting outcomes using data analysis is something everyone wants to be able to do, but predicting the future in any capacity requires a lot more than just looking at past trends. This can be difficult for businesses because there may be many factors involved (for example: how much money you spent last year compared with this year) or it might involve something specific like weather patterns or politics. In fact, if you’re trying to predict something specific like whether or not someone will quit their job after being laid off next month then there are probably better ways than just looking at historical trends between two points in time (which could lead them astray).
Data analysis can help us understand the past.
Data analysis can help us understand the past.
It’s important to remember that data analysis isn’t just about predictions and projections. It also helps us understand why something happened in the first place, or how it changed over time. For example:
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If you’re studying a historical event, like World War II or the Great Depression, data analysis can provide insight into how these events affected people’s lives at the time—and what could have been done differently if things had turned out differently (or better).
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If you’re analyzing customer behavior on social media platforms like Facebook or Instagram, data analysis will show whether certain trends are happening more frequently than others among your target audience—and why this may be happening!
Data analysis has three main uses — understanding behavior, predicting the future, and understanding the past.
Data analysis is a way of investigating data. It can help us understand the past, predict the future and understand behavior. Data analysis is used in many different fields, including science, finance and medicine.
Some examples of data analysis include:
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Understanding behavior – looking at how people behave when they are presented with various stimuli (such as advertisements or images)
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Predicting the future – using data to analyze trends in order to make predictions about what will happen next
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Understanding history – analyzing historical events like wars or economic crashes
Conclusion
Data analysis is an important tool in our society, and it can be used to help us make better decisions. I hope this article has given you a sense of how data analysis can help us understand the world around us.
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