Discussing the application of data analysis, reliability and validity, and assorted statistical tests used in health-related research
DNP 830 Topic 2 Data Interpretation Assignment
DNP 830 Topic 2 Data Interpretation Assignment
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 Data Interpretation Assignment
Discussing the application of data analysis, reliability and validity, and assorted statistical tests used in health-related research
Introduction
Data analysis is an important part of any research project. It helps to explain what the research found and identify any potential problems or limitations. The most common types of data analysis include assessing reliability and validity, performing statistical tests, and drawing conclusions based on your findings.
Data analysis
Data analysis is an integral part of health-related research. It is the process of converting raw data into information, which can either help you understand your results better or lead to new discoveries.
In this section, we will discuss how researchers use data analysis in their experiments and studies. We will also look at some important aspects of good data analysis so that you can make sure your own work meets certain standards before submitting it for publication or presentation at conferences like those we have talked about here today!
Assessment of reliability and validity
Reliability and validity are two of the main concerns in medical research. The reliability of a test is determined by its inter-rater reliability, which refers to the consistency between different raters. In other words, how consistent are two people’s readings?
The validity of a test refers to its ability to measure what it is supposed to measure (i.e., depression). It also considers whether or not there are differences between groups with similar characteristics (for example, people who suffer from depression).
Assorted statistical tests
It is important to understand the various statistical tests available for use in health-related research. These include descriptive statistics, inferential statistics and hypothesis testing. Descriptive statistics are used to describe the characteristics of data (e.g., mean, median and standard deviation). Inferential statistics allow us to make conclusions about other samples from our sample based on our knowledge of it’s properties (e.g., whether or not there are differences between two populations).
If we want to analyze a sample with more than one variable then we need to use inferential procedures such as regression analysis or association tests because these methods allow us test specific relationships between variables rather than simply looking at them as a whole entity without considering any relationships between them.
Importance of data analysis in health-related research
The importance of data analysis in health-related research is obvious. The validity and reliability of a study depends on how well it was conducted, and the results reported. It’s also important to know whether or not your study was successful at answering its intended questions or hypotheses, which means you need to be able to make sense out of your findings.
There are many different statistical tests (or methods) that can be used for this purpose: t-tests, chi-squared tests, ANOVA (analysis of variance), regression analysis etc., but one thing they all have in common is that they’re designed specifically for answering specific questions about your data set; these include whether its mean differs significantly from some other value; whether there are any significant differences between groups based on their mean scores; whether there’s evidence linking X variable(s) with Y outcome(s); whether Y outcome changes significantly after introducing Z intervention into program Y ran over time period T
Conclusion
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