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:
  1. Evaluate methods of data organization.
  2. Compare characteristics of correlational, experimental, and quasi-experimental (observational) statistics variables.
  3. Identify the four levels of measurement.
  4. Differentiate between a population and a sample, and a parameter and a statistic (descriptive and inferential).
  5. 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
  6. Evaluate public health data sources.
  7. Apply methods to calculate and communicate descriptive statistics.

Data Management

The purpose of this 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:

  1. Order (sort) observations according to age.
  2. Group observations by sex and investigate the age and income for males and females.
  3. 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:

  1. Describe the measurement levels for each of the variables in the dataset.
  2. Discuss what you learned from ordering the data by age and why this information is important.
  3. Describe the process you used to group the data in Excel and SPSS.
  4. Describe what you learned by grouping the variables by category of exercise.
  5. 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 for assistance.

Attachments

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:
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