The Final Project Data Analysis reinforces these critical sk…

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Guidelines for Submission: Your data analysis should be approximately 3–5 pages long (including graphs or spreadsheet with calculations), double-spaced, 12- point Times New Roman font, with one-inch margins and citations in APA format. Be sure to use language and a style appropriate for a non-technical audience.

IHP 525 Final Project Data Analysis Guidelines and Rubric

Overview Now that you have submitted your article review, you will submit Your Final Project Data Analysis. The Final Project Article Review was an opportunity to demonstrate your ability to interpret statistics included in an article. The Final Project Data Analysis is a chance to show that you know how to choose the correct statistics to analyze a set of data and calculate these using software.

Regardless of their field of interest, health professionals across disciplines need to be able to run basic biostatistical calculations to describe a set of data.

The Final Project Data Analysis reinforces these critical skills by asking you to conduct your own analysis of a small data set, explain the basic parameters of the data, graph it, and run simple tests. You will present this data analysis in a brief statistical report, using language appropriate to a non-technical audience.

The Final Project Data Analysis consists of four milestones, submitted in Modules Two, Three, Five, and Seven. The final submission occurs in Module Nine.

In this assignment, you will demonstrate your mastery of the following course outcomes:

Perform basic, context-appropriate statistical calculations and hypothesis testing in accurately analyzing biostatistical data

Interpret key biostatistical metrics, methods, and data for addressing population-based health problems

Communicate biostatistical results, procedures, and analysis to other health professionals and the general public for informing their decisions related to population-based health problems

Prompt Biostatisticians are constantly called upon to analyze data in order to help researchers and health officials answer critical questions about populations’ health. For this assessment, you will imagine you are a biostatistical consultant on a small study for a local health organization. In the Assignments Guidelines and Rubrics area of the course, you will use the Data Analysis Data Set and Data Analysis Data Description, along with some background information on how and when the data was collected and the general research question the organization is interested in answering. This is often the way you will receive data in the real world.

Your task is to help the organization answer their question by critically analyzing the data. You will compute your chosen statistics, interpret the results, and present the results and recommendations to non-technical decision makers in the form of a data analysis. Keep in mind that it is your job to do this from a statistical standpoint. Be sure to justify your conclusions and recommendations with appropriate statistical support.

Specifically, you must address the critical elements listed below. Most of the critical elements align with a particular course outcome (shown in brackets).

I. Introduction A. State the overall health question you have been asked to address in your own words. Be sure you capture the key elements of the question,

using language that a non-technical audience can understand. B. Assess the collected data. Use this section to layout the source, parameters, and any limitations of your data. Specifically, you should:

1. Describe the key features of your data set. Be sure to assess how these features affect your analysis. 2. Analyze the limitations of the data set you were provided and how those limitations might affect your findings. Justify your response.

C. Process: Propose how you will go about answering the health question you were asked to address based on the data set provided.

II. Data Analysis A. Graphs: In this section, you will use graphical displays to examine the data.

1. Create at least one graph that gives a sense of the potential relationship between the two variables that form your chosen health question. Include the graph and discuss why you selected it as opposed to others.

B. Conduct an appropriate statistical test to answer your health question. C. Explain why this test is the best choice in this context. D. Analysis of Biostatistics: Use this section to describe your findings from a statistical standpoint. Be sure to:

1. Present key biostatistics from the graph(s) and statistical tests and explain what they mean. Be sure to include a spreadsheet showing your work or a copy of your StatCrunch output as an appendix.

2. What statistical inferences or conclusions can you draw based on the results of your statistical test and graph? Justify your response.

III. Conclusions and Recommendations A. How do the findings help answer your overall health question? Remember to use brief, non-technical language to ensure audience

understanding. B. Recommend areas for further research based on your findings. Remember to use brief, non-technical language to ensure audience

understanding.

Milestones Milestone One: Select Health Question In Module Two, you will identify the health question you will be researching for instructor feedback and approval. Milestone One should be several sentences in length. This milestone is graded with the Milestone One Rubric.

Milestone Two: Describe the Data In Module Three, you will describe the key features of the data set, including limitations that might exist. Milestone Two should be one or two paragraphs in length. This milestone is graded with the Milestone Two Rubric.

Milestone Three: Process and Calculations In Module Five, you will create a table in which you propose the calculations (descriptive statistics and statistical test) and graph(s) you will need to perform to answer the health question you are investigating. Then you will complete the table. For Milestone Three, you will submit this completed table. This milestone is graded with the Milestone Three Rubric.

Milestone Four: Data Analysis In Module Seven, you will submit the data analysis section of Final Project Data Analysis. This section includes the graph(s) and statistics you conducted on the data set to answer the health question. This milestone is graded with the Milestone Four Rubric.

Final Project Data Analysis In Module Nine, complete the conclusions section and the rest of the completed data analysis (including graph(s), statistical output for chosen test, or Excel spreadsheet with calculations). You have been working on this analysis in Milestones One through Four. Before submitting, revise each section of the analysis based on the feedback you received from your instructor and peers. It should be a complete, polished artifact containing all of the critical elements of the final product. It should reflect the incorporation of feedback gained throughout the course. This submission will be graded using the Final Project Data Analysis Rubric.

Deliverables Milestone Deliverable Module Due Grading

One Select Health Question Two Graded separately; Milestone One Rubric

Two Describe the Data Three Graded separately; Milestone Two Rubric

Three Process and Calculations Five Graded separately; Milestone Three Rubric

Four Data Analysis Seven Graded separately; Milestone Four Rubric

Final Submission: Final Project Data Analysis Nine Graded separately; Final Project Data Analysis Rubric

Final Project Data Analysis Rubric Guidelines for Submission: Your data analysis should be approximately 3–5 pages long (including graphs or spreadsheet with calculations), double-spaced, 12- point Times New Roman font, with one-inch margins and citations in APA format. Be sure to use language and a style appropriate for a non-technical audience.

Critical Elements Exemplary Proficient Needs Improvement Not Evident Value

Introduction: Health Question

[IHP-525-05]

N/A States overall health question in own words, capturing key elements of question while using language appropriate for a non-technical audience

States overall health question in own words, but response contains inaccuracies, omits key details, or does not use language appropriate for a non-technical audience

Does not state overall health question in own words

8

Introduction: Data: Key Features [IHP- 525-03]

Meets “Proficient” criteria and demonstrates a sophisticated awareness of the features’ influence

Describes key features of data set and assesses how features affect analysis

Describes key features of data set and assesses how features affect analysis, but response contains inaccuracies or omits key details

Does not describe key features of data set and assess how features affect analysis

9

Introduction: Data: Limitations

[IHP-525-05]

Meets “Proficient” criteria and justification provides keen insight into how data quality affects findings

Analyzes limitations of data set provided and how those affect findings, justifying response

Analyzes limitations of data set and how those affect findings, but does not justify response, response contains inaccuracies, or justification is illogical

Does not analyze limitations of data set and how those affect findings

8

Introduction: Process [IHP-525-02]

Meets “Proficient” criteria and process proposed is well- aligned with health question, taking most direct path to answer

Proposes process of answering health question based on the data set provided

Proposes process of answering health question based on data set, but response contains inaccuracies, omits key procedures, or procedures suggested are inappropriate

Does not propose process of answering health question based on data set

9

Data Analysis: Graphs: Graph [IHP-525-02]

Meets “Proficient” criteria and graph incorporates appropriate scaling and is exceptionally well-tailored to the intended audience

Creates a graph that gives a sense of the potential relationship between two variables that form the chosen health question and discusses why this graph was selected over others

Creates a graph that gives a sense of the potential relationship between two variables that form the health question and discusses why this graph was selected over others, but graph is inappropriate, reasons ar e illogical, or response contains inaccuracies

Does not create a graph that gives a sense of the potential relationship between two variables

9

Critical Elements Exemplary Proficient Needs Improvement Not Evident Value Data Analysis: Test

[IHP-525-02] Meets “Proficient” criteria and demonstrates an astute ability to accurately and effectively conduct the test

Conducts appropriate statistical test accurately to answer chosen health question

Conducts statistical test to answer chosen health question, but response contains inaccuracies or test is not conducted appropriately

Does not conduct appropriate statistical test to answer chosen health question

9

Data Analysis: Best Choice

[IHP-525-02]

Meets “Proficient” criteria and makes cogent connections between the test and graph or data

Explains why test is the best choice in this context

Explains why test is the best choice in this context, but explanation is cursory or contains inaccuracies

Does not explain why test is the best choice in this context

9

Data Analysis: Analysis: Biostatistics

[IHP-525-03]

Meets “Proficient” criteria and explanation effectively communicates meaning of the calculations in audience- appropriate language

Presents graph and statistical test results, including spreadsheet showing work or computer output, and accurately explains what chosen calculations mean

Presents graph and statistical test results, including spreadsheet showing work or computer output, and explains what they mean, but response contains inaccuracies or omits key details

Does not present graph and statistical test results, including spreadsheet showing work or computer output and does not explain what these calculations mean

9

Data Analysis: Analysis: Statistical

Inferences [IHP-525-03]

Meets “Proficient” criteria and thoroughly explains how these statistics define the population

Draws appropriate statistical inferences based on statistical hypothesis test results and graph and justifies response

Draws appropriate statistical inferences based on test results and graph, but does not justify response, justification is illogical, or response contains inaccuracies

Does not draw appropriate statistical inferences or conclusions based on test results and graph

9

Conclusions: Findings [IHP-525-05]

Meets “Proficient” criteria and demonstrates a complex grasp of elements necessary to answer the overall health question

Assesses how findings help answer overall health question, using brief non-technical language

Assesses how findings help answer overall question, but does not use brief, non-technical language, or response contains inaccuracies

Does not assess how findings help answer overall health question

8

Conclusions: Recommendations

[IHP-525-05]

Meets “Proficient” criteria and recommendations including what additional information would help better answer question

Recommends areas for further research based on findings, using brief, non-technical language

Recommends areas for further research based on findings, including additional information to better answer question, but does not use brief, non-technical language, recommendations are illogical, or response contains inaccuracies

Does not recommend areas for further research based on findings, including additional information that would help better answer question

8

Articulation of Response

Submission is free of errors related to citations, grammar, spelling, syntax, and organization and is presented in a professional and easy to read format

Submission has no major errors related to citations, grammar, spelling, syntax, or organization

Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas

Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas

5

Critical Elements Exemplary Proficient Needs Improvement Not Evident Value Total 100%

IHP 525 Final Project Data Analysis Guidelines and Rubric

Overview

Prompt

Milestones

Milestone One: Select Health Question

Milestone Two: Describe the Data

Milestone Three: Process and Calculations

Milestone Four: Data Analysis

Final Project Data Analysis

Deliverables

Final Project Data Analysis Rubric

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Milestone One Health Question Selection

Deanna Buchanan

Milestone One Health Question Selection

Myocardial infarctions (MIs), another name for heart attacks, are common in people 60 and older. Blockage in a coronary artery reduces blood flow to the heart, leading to cardiac arrest. Plaque, which is made up of cholesterol, fat, and other chemicals, builds up over time in the coronary arteries and causes their obstruction (Gerstman, 2015). This research aims to determine whether or not heart attack victims’ hospital stays are affected by their gender. The data used in this report was collected from the Worcester Heart Attack Study (WHAS100) by Hosmer et al. (2008). The research question is, “Does gender influence the length of stay (LOS) of myocardial infarction patients (MI)?” Acute care hospitalized patients with myocardial infarction will have their length of stay (LOS) and gender analyzed using the Stat Crunch software (Pearson Education, 2014) to see whether or not these factors have a significant effect on the patient’s outcomes (MI). The test will be administered separately to male and female patients using a two-sample t-test.

References

Gerstman, B.B. (2015). Basic biostatistics: Statistics for public health practice (2nd ed.). Jones and Bartlett Learning.

Hosmer, D.W., Lemeshow, S. & May, S. (2008). Applied survival analysis: Regression modeling of time to event data (2nd ed.). John Wiley and Sons Inc.

Pearson Education (2014). Stat Crunch (Version June 2016 update) [Computer software]. New Pearson Corporation.

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3

Data Analysis Milestone Two

Deanna Buchanan

1

SNHU

Data Key Features

The evaluation is being carried out on data from the Worcester Heart Attack Study (WHAS100) conducted by the Department of Cardiology at the University of Massachusetts Medical School (Hosmer, Lemeshow & May, 2008). The appendix table shows that the dataset has 100 observations and 9 variables. The data was collected over a 9-year period starting in 1995 and focused on patients who suffered from myocardial infarction and were admitted to Worcester hospitals. The analysis will only use two variables – gender and length of stay (LOS). Gender is a categorical variable, with males coded as 0 and females coded as 1. LOS is a continuous variable with a mean of 6.32 and a standard deviation of 3.34. For LOS, the mean value is more indicative of the whole data set for men than for females since the variation and standard deviation are lower.

Limitations

The evident limitation of the data set is that the number of male observations (65) is approximately twice the number of female observations (35). This may introduce bias into the results, since it no longer accurately represents either male or female patients hospitalized due to myocardial infarction. Another drawback is that potential confounding factors such racial/ethnicity, treatment/medication disparities, insurance status, and preexisting/co-morbid illnesses were not included. These confounding variables might distort the analysis and findings of the statistical correlation between gender and hospital stay duration in patients with myocardial infarction (Gerstam, 2015).

References

Gerstman, B. B. (2015). Basic biostatistics: Statistics for public health practice. Jones & Bartlett Learning.

Hosmer, D. W., Lemeshow, S., & May, S. (2008). Applied survival analysis: Regression modeling of time to event data (2nd Ed.). New York, NY: John Wiley and Sons Inc.

Appendix

Table 1

Summary statistics for los:

Group by: gender