PUB-550: APPLICATION AND INTERPRETATION OF PUBLIC HEALTH DATA

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PUB-550: Application and Interpretation of Public Health Data

PUB-550: Application and Interpretation of Public Health Data
PUB-550: Application and Interpretation of Public Health Data
Topic 1: Data Management and Descriptive Statistics
Objectives:
Evaluate methods of data organization.
Compare characteristics of correlational, experimental, and quasi-experimental (observational) statistics variables.
Identify the four levels of measurement.
Differentiate between a population and a sample, and a parameter and a statistic (descriptive and inferential).
Explain the role of quantitative and qualitative methods and sciences in describing and assessing a population’s health. PUB-550: Application and Interpretation of Public Health Data
Evaluate public health data sources.
Apply methods to calculate and communicate descriptive statistics.
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Data Management
The purpose of this assignment is to practice organizing data through ordering and grouping variables.
Data often appear disordered and it is difficult to see any connections or relationships. Ordering the data by certain variables or grouping variables into specific categories, such as age or sex categories, can help bring clarity to the data. Knowing how to organize data is an important skill to initiate the analytical process.
For this assignment, students will use Excel and SPSS Statistics to order variables. Using the “Example Dataset,” complete the steps below using both Excel and SPSS Statistics. View the Excel and SPSS tutorials for assistance in completing this assignment. Submit one Word document and include a screen shot of the data after completing the first two steps of Part 1 in Excel and SPSS to compare your results. Use a second Word document to complete Part 2 of the assignment. PUB-550: Application and Interpretation of Public Health Data
Part 1: Ordering and Grouping Data Using Excel and SPSS
For Part 1, accomplish the following:
Order (sort) observations according to age.
Group observations by sex and investigate the age and income for males and females.
Create a new variable titled “Exercise Group” based on the variable “Minutes Exercise.” Use the following categories to create your groups: 1 = 0-30 minutes; 2 = 31-60 minutes; 3 = 61-90 minutes; 4 = 91-120 minutes; and 5 = 120+ minutes.
Part 2: Data Interpretation
Study the results of the dataset grouping and ordering. Discuss the following in a 500-750 word summary:
Describe the measurement levels for each of the variables in the dataset.
Discuss what you learned from ordering the data by age and why this information is important.
Describe the process you used to group the data in Excel and SPSS.
Describe what you learned by grouping the variables by category of exercise.
Are these data from a correlational study, experimental study, or quasi-experimental (observational) study? Discuss your rationale and identify a study question appropriate for this dataset. PUB-550: Application and Interpretation of Public Health Data
General Requirements
Submit the Word document to the instructor.
APA style is not required, but solid academic writing is expected.
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. Refer to the LopesWrite Technical Support articles for assistance.
Attachments
PUB-550-RS-Example Dataset.xlsx
Rubric
Due Date: 05-Mar-2020 at
A local community organization was interested in learning about general health behaviors in the area and the relationships between health behaviors and environmental and social determinants. They decided to conduct a brief survey based on a convenient sample of people visiting the local shopping mall. They offered a $5 incentive for completing the survey. The Topic 1 Example dataset includes 30 observations from this survey. Use this data to complete the relevant assignments in this course.
Education Level
1
Less than High School
2
Graduated High School
3
Graduated College
Annual Income = US Dollars
 
ID
Sex
Smoker
Education_Level***
Minutes_Exercise
Age
Employed
Annual_Income*
Neighborhood
101
Female
No
2
90
45
Yes
51000
B
102
Male
No
2
50
58
No
23000
C
103
Female
Yes
3
65
31
Yes
35000
B
104
Male
No
1
20
54
No
10000
C
105
Female
Yes
1
50
30
Yes
28000
B
106
Female
Yes
2
25
18
No
5000
C
107
Female
No
3
110
39
Yes
46000
A
108
Male
Yes
1
50
37
Yes
36000
B
109
Female
Yes
2
40
44
Yes
51000
C
110
Male
No
2
80
24
No
12000
A
111
Female
No
3
120
42
Yes
78000
A
112
Male
No
1
80
50
Yes
34000
D
113
Female
Yes
1
60
20
No
15000
B
114
Male
No
3
150
35
Yes
28000
B
115
Male
No
2
75
61
Yes
28000
A
116
Male
No
1
80
59
No
24000
B
117
Female
No
2
110
36
Yes
55000
D
118
Male
Yes
3
80
35
Yes
62000
B
119
Male
Yes
2
100
29
No
32000
D
120
Female
No
1
0
32
No
7000
C
121
Female
Yes
2
50
26
No
17000
B
122
Female
No
3
200
42
Yes
64000
D
123
Male
No
2
60
52
No
5000
A
124
Male
No
1
65
49
No
14000
D
125
Female
No
1
40
21
No
20000
C
126
Male
Yes
3
65
48
Yes
72000
A
127
Female
Yes
3
70
40
Yes
85000
A
128
Female
No
1
45
53
No
15000
B
129
Male
No
3
75
46
Yes
64000
C
130
Male
Yes
3
50
42
Yes
27000
B
 
Topic 1 DQ 1
Mixed methods research is becoming an important approach in generating public health evidence. Based on the resources supplied, discuss the benefits of a mixed methods approach. Include an explanation of the differences between qualitative and quantitative research and the purpose of each.
Topic 1 DQ 1
Mixed methods research has become increasing popular, however the definition of mixed methods research has yet to be agreed upon (Ozawa & Pongpirul, 2014). Essentially, mixed methods research studies incorporate quantitative and qualitative data to utilize the strengths of both types of research methods (Ozawa & Pongpirul, 2014). In health systems, mixed methods research is critical because it allows researchers to see issues from various perspectives, contextualize information, have a better understanding of the issue, form results, quantify difficult measures, create illustrations for trends, and examine processes (Ozawa & Pongpirul, 2014).To make sense of the assembly of mixed method research designs, there are four categories; the triangulation design, the embedded design, the explanatory design, and the exploratory design (Almalki, 2016). The triangulation design is practical because this type of research gathers data from different sources and utilizes different methods, which all work together as well-organized design (Almalki, 2016). With the embedded design, less resources are needed, and it produces less data, making it easier for researchers to grasp (Almalki, 2016). The explanatory design is easy to implement, and it enables the focus of the research to be maintained (Almalki, 2016). With the exploratory design, separate stages are easy to apply, also qualitative information is acceptable to quantitative researchers (Almalki, 2016).Quantitative research regards the world as being outside of themselves. The purpose is to gain an understanding about the social world (Almalki, 2016). The qualitative approach gains a perspective of issues by investigating them in their own specific setting. The purpose is to observe occurrences and bring meaning to them (Almalki, 2016). The differences between quantitative and qualitative research is as follows:
Quantitative Approach
Qualitative Approach
Deductive
Inductive, with underlying assumptions reality is a social construct
Subdivides reality into smaller, manageable pieces
Places emphasis on exploring and understanding
Observations are made and hypotheses can be tested among variables
Variables are difficult to measure
Primacy of subject matter
Conclusions are made with regard to the hypothesis, following a series of observations and analysis of data
Data collected will consist of an insider’s viewpoint
(Almalki, 2016).
References
Almalki, S. (2016). Integrating Quantitative and Qualitative Data in Mixed Methods Research – Challenges and Benefits. Journal of Education and Learning. doi:10.5539/jel.v5n3p288. Retrieved from https://files.eric.ed.gov/fulltext/EJ1110464.pdf
Ozawa, S. & Pongpirul, K. (2014). 10 best resources on…mixed methods research in health systems. Health Policy and Planning. Retrieved from https://academic.oup.com/heapol/article/29/3/323/581455
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Re: Topic 1 DQ 1
The delivery of healthcare is becoming more complex as evidence by the rising number of individuals with comorbidities and the shift towards the quality of care versus quantity. Addressing challenges that are generated by this complex system requires research that not only produces statistical data, but also understands a population’s natural setting and provides insight how he research can be applied to that setting. Mixed methods research is becoming an important approach in generating public health evidence because it combines both qualitative and quantitative research. Qualitative research answers clinical question regarding meaning and quality improvement and provides descriptive data while quantitative research answers clinical question regarding therapy, etiology, diagnosis, prevention, and prognosis and produces numerical data (Winona State University, 2014). Favorable characteristics of mixed method research include consistency between the research question, purpose and methodological choices; verifiable and transparent techniques that demonstrate trustworthiness; potential for replicability; opportunity for self-correction; and ability to explain the phenomena under investigation (Newman and Hitchcock, 2012). Furthermore, benefits to mixed methods include answering questions that qualitative or quantitative research cannot answer alone; provides better understanding of connections or contradictions between qualitative and quantitative data; it gives participants an opportunity to have a voice and share the experience across the research process [which is important within public health]; it facilitates different avenues of exploration that enhance the quality of evidence and enables questions to be answered more deeply (Shorten & Smith, 2017). A mixed method approach uses the combine strengths of qualitative and quantitative data. Its unique design is appropirate to addressing complex public health issues.Hitchcock, J. H., & Newman, I. (2012). Applying an Interactive Quantitative-Qualitative Framework. Human Resource Development Review, 12(1), 36–52. https://doi.org/10.1177/1534484312462127Shorten, A., & Smith, J. (2017). Mixed methods research: expanding the evidence base. Evidence Based Nursing, 20(3), 74–75. https://doi.org/10.1136/eb-2017-102699Winona State University. (2014). Research Hub: Evidence Based Practice Toolkit: Levels of Evidence. Retrieved from Winona.edu website: https://libguides.winona.edu/c.php?g=11614&p=61584
 
1 DQ 2
Topic One, Discussion Question 2:Statistics are ways to summarize data in a way that will answer a specific question (Corty, 2016). There are several key words that help with defining statistics, such as population, sample, parameter and statistic.During investigation studies researchers look for subjects to study. These subjects from large groups called a population (Corty, 2016). If the research only wanted to look at a small group of this population, they would call that a sample (Corty, 2016).For example – If I were to do a research study on obesity, I could use the state of Kentucky as my population. However, if I wanted to only look at Shelbyville, Kentucky that would be a sample of Kentucky.Data from either the sample or the population which can be reduced to a simple number like an average to summarize the group (Corty, 2016). If it is characterizing the sample, it is called a statistic; if it is characterizing the population it is called a parameter. Sample statistics use Latin letters as their symbol and population parameters use Greek letters (Corty, 2016).Then there is descriptive and inferential statistics. Descriptive is the summary statement about the set of cases (Corty, 2016). It reduces a set of data to a meaningful value to describe the characteristics of the group being observed – for example: 63% of the class were females. Inferential statistics uses a sample of cases to draw a conclusion about the larger population and reduces the data down to a single value that inferences about the population (generalization from the sample to a population – for example: Students who are female at GCU have a 15% higher GPA on average than males (Corty, 2016).Public health researchers often limit or rather stop their analyses to descriptive statistics—reporting frequencies, means and standard deviation (Guetterman, 2019). This allows for missed opportunities for more advanced analyses. “For example, knowing that patients have favorable attitudes about a treatment may be important and can be addressed with descriptive statistics. On the other hand, finding that attitudes are different (or not) between men and women and that difference is statistically significant may give even more actionable information to healthcare professionals” (Guetterman, 2019). This missing piece about differences can be addressed through inferential statistical tests (Guetterman, 2019). Therefore, both are extremely important to public health research.
References:
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Corty, E. (2016). Using and interpreting statistics. A practical text for the behavioral, social, and health sciences 3rd Edition. Retrieved from https://viewer.gcu.edu/GGdEcj
Guetterman, T., (2019). Basics of statistics for primary care research. Family Medicine Community Health. 7(2). Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6583801/
 
Practicing Application of Descriptive Statistics in Excel and SPSS
The purpose of this assignment is to compare basic functions in Excel and SPSS to calculate descriptive statistics and use this information to describe the sample.
For this assignment, students will utilize Excel with the Data Analysis ToolPak and SPSS Statistics and the “Example Dataset” to complete the assignment. Refer to the Topic Materials for assistance with enabling the Data Analysis ToolPak on a Mac or PC.
Part 1:
Complete the following steps in both Excel and IBM SPSS Statistics.
Calculate mean, median, and mode for the variables “Annual Income” and “Age.” Show the appropriate summary tables for these measures from both Excel and SPSS. Include the other descriptive statistics that are a part of the summary output in Excel and SPSS.
Create histograms to show the distribution for “Annual Income” and “Age.” Copy and paste the histograms from Excel and export the histogram from SPSS into the Word document for this assignment.
Create frequency tables that include counts and percentages for smoking status, employment status, exercise level, and education level. Show the tables in the Word document for this assignment.
Part 2:
Based upon the Part 1 activities, write a 250-500 word interpretation that addresses the following.
Discuss the sampling strategy used in this study and if it resulted in a representative sample.
Discuss what you are able to ascertain about the sample from the descriptive statistics.
Explain what other variables the research team could have included to gain a better understanding of the population.
General Requirements
Submit one Word document for the Part 1 assignment content and a second Word document for Part 2 of the assignment.
APA style is not required, but solid academic writing is expected.
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 not required to submit this assignment to LopesWrite.
Attachments
PUB-550-RS-Example Dataset.xlsx
 
Calculating Confidence Intervals
The purpose of this assignment is to practice calculating confidence intervals.
For this assignment, students will utilize Excel and SPSS Statistics and the “Example Dataset.”
Using the “Example Dataset,” complete the following:
Based on a normal distribution curve, calculate the probability of an individual being 60 years or older in this population. Show the Excel and SPSS formulas or your hand calculations. Include screenshots as needed to illustrate this.
Using the sample standard deviation of age as an estimate of the population standard deviation, calculate by hand the standard error of the mean. Show your calculations and the answer.
Calculate by hand a 95% confidence interval for “Age” based on the sample mean. Use SPSS to verify your answer. Include your calculations and screenshots of the SPSS output.
Interpret the confidence interval for age and explain the three pieces of information needed to calculate a confidence interval.
Submit one Word document that includes all of the assignment deliverables.
APA style is not required, but solid academic writing is expected.
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 not required to submit this assignment to LopesWrite.
Attachments
PUB-550-RS-Example Dataset.xlsx
 
Topic 2 DQ 1
P-values and confidence intervals are both used in hypothesis testing. Explain three reasons why it may be preferable to report a confidence interval over a P-value. Provide a specific example to justify your reasons.
Topic 2 DQ 1
De Prel et al. (2009) study found the following: P-values in scientific studies are used to determine whether a null hypothesis formulated before the performance of the study is to be accepted or rejected. In exploratory studies, p-values enable the recognition of any statistically noteworthy findings. Confidence intervals provide information about a range in which the true value lies with a certain degree of probability, as well as about the direction and strength of the demonstrated effect. This enables conclusions to be drawn about the statistical plausibility and clinical relevance of the study findings. It is often useful for both statistical measures to be reported in scientific articles, because they provide complementary types of information (p.335).
 
According to de Prel et al. (2009) “For example, there might be no difference between two antihypertensives with respect to their ability to reduce blood pressure. The alternative hypothesis (H1) then states that there is a difference between the two treatments. This can either be formulated as a two-tailed hypothesis (any difference) or as a one-tailed hypothesis (positive or negative effect). In this case, the expression “one-tailed” means that the direction of the expected effect is laid down when the alternative hypothesis is formulated (p.335).
 
Reference
du Prel, J. B., Hommel, G., Röhrig, B., & Blettner, M. (2009). Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications. Deutsches Arzteblatt international, 106(19), 335–339. doi:10.3238/arztebl.2009.0335
 
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Topic 2 DQ 2
The Central Limit Theorem is the fundamental theorem of statistics. In a nutshell, it says that for independent and identically distributed data whose variance is finite, the sampling distribution of any mean becomes more nearly normal (i.e., Gaussian) as the sample size grows (Chang, Wu, Ho and Chen, 2008). The sample mean ¯xn will then approach the population mean µ, in distribution. More formally, where N (0, 1) is the normal distribution and the symbol “d” in the equality means in distribution. σn is the standard deviation of a sampling distribution, σ is the standard deviation of the entire population the study (and which is often not known), and n the sample size. So, sample means vary less than individual measurements. (The square of the standard deviation is the variance.). The sampling distribution is a notional (imaginary) distribution from a very large number of samples, each one of size n, which approaches a normal distribution in the limit of large n. In practice, the Central Limit Theorem holds for n as low as 30, unless there are exceptional circumstances—e.g., when the population distribution is highly skewed—in which case higher values are needed. So, σn measures how widely the sample means of size n vary around the population mean µ (which is approached in the limit of large n). As expected, the results suggest that the distribution of the sample mean better approximates the normal distribution as the sample size increases. The results indicate that the true distribution of the sample mean when the sample is taken from a highly skewed distribution better approximates the normal distribution as the thickness of the tail of the population distribution increases.
Chang, H. J., Wu, C. H., Ho, J. F., & Chen, P. (2008). On sample size in using central limit theorem for gamma distribution
 
Topic 3: Hypothesis Testing
Objectives:
Evaluate the importance of hypothesis testing in statistics and public health research.
 
Hypothesis Testing
The purpose of this assignment is to evaluate the steps of hypothesis testing.
Hypothesis testing is a central component in understanding and interpreting public health research. Research questions are applied through a testable hypothesis established before the data is collected. It provides the rationale for the study and guides the researcher in what tests to use and how to interpret the results.

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