PUB-550 Application and Interpretation of Public Health Data Course Assignments & Discussions Study Guide

Table of Contents

PUB-550 Application and Interpretation of Public Health Data Course Assignments & Discussions Study Guide

PUB-550 Application and Interpretation of Public Health Data Course Description

This course introduces learners to the application and interpretation of data to assess, design, and justify public health programs. Learners learn the basics of data management and statistical analysis using real-life public health data sets. Learners consider the implications of crafting a clear research question, identifying available and quality data, applying appropriate data analysis methods, and effectively communicating the results. Research standards and ethics are emphasized in contributing to evidence-based public health practice. Prerequisite: PUB-540.

PUB-550: Application and Interpretation of Public Health Data

Topic 1: Data Management and Descriptive Statistics Assignment

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.
Evaluate public health data sources.
Apply methods to calculate and communicate descriptive statistics.

PUB-550 Data Management Assignment

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.

You can also read another study guide on nursing assignments for students from another post on PUB-655 International Perspectives in Community Health Course Assignments & Discussions Study Guide.

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.

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

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PUB-550 Data Management Assignment Rubric

Course Code	Class Code	Assignment Title	Total Points
PUB-550	PUB-550-O500	Data Management	40.0

Criteria	Percentage	Unsatisfactory (0.00%)	Less than Satisfactory (74.00%)	Satisfactory (79.00%)	Good (87.00%)	Excellent (100.00%)	Comments	Points Earned
Content	100.0%
Part 1 – Excel and SPSS Screenshots	20.0%	Screenshots illustrating the use of Excel and SPSS to order and group variables are not included.	Screenshots illustrating the use of Excel and SPSS to order and group variables are incomplete or incorrect.	Screenshots illustrating the use of Excel and SPSS to order and group variables are partially complete and correct.	Screenshots illustrating the use of Excel and SPSS to order and group variables are mostly complete and correct.	Screenshots illustrating the use of Excel and SPSS to order and group variables are complete and correct.

Part 2 – Measurement Levels	15.0%	Description of the measurement levels for each of the variables in the dataset is not included.	Description of the measurement levels for each of the variables in the dataset is incomplete or incorrect.	Description of the measurement levels for each of the variables in the dataset is included but lacks explanation and relevant details.	Description of the measurement levels for each of the variables in the dataset is complete and includes explanation and relevant details.	Description of the measurement levels for each of the variables in the dataset is extremely thorough with substantial explanation and relevant details.

Part 2 – Ordering the Data	15.0%	Discussion of what was learned from ordering the data by age and why this information is important is not included.	Discussion of what was learned from ordering the data by age and why this information is important is incomplete or incorrect.	Discussion of what was learned from ordering the data by age and why this information is important is included but lacks explanation and relevant details.	Discussion of what was learned from ordering the data by age and why this information is important is complete and includes explanation and relevant details.	Discussion of what was learned from ordering the data by age and why this information is important is extremely thorough with substantial explanation and relevant details.

Part 2 – Process Used for Grouping Data	15.0%	Description of the process used to group the data in Excel and SPSS is not included.	Description of the process used to group the data in Excel and SPSS is incomplete or incorrect.	Description of the process used to group the data in Excel and SPSS is included but lacks explanation and relevant details.	Description of the process used to group the data in Excel and SPSS is complete and includes explanation and relevant details.	Description of the process used to group the data in Excel and SPSS is extremely thorough with substantial explanation and relevant details.

Part 2 – Grouping Variables by Category	15.0%	Description of what was learned from grouping variables by the category of exercise is not included.	Description of what was learned from grouping variables by the category of exercise is incomplete or incorrect.	Description of what was learned from grouping variables by the category of exercise is included but lacks explanation and relevant details.	Description of what was learned from grouping variables by the category of exercise is complete and includes explanation and relevant details.	Description of what was learned from grouping variables by the category of exercise is extremely thorough with substantial explanation and relevant details.

Part 2 – Study Discussion	15.0%	Discussion of whether study is correlational, experimental, or quasi-experimental; the rationale for the study type; and identification of a study question appropriate for the dataset is not included.	Discussion of whether study is correlational, experimental, or quasi-experimental; the rationale for the study type; and identification of a study question appropriate for the dataset is incomplete or incorrect.	Discussion of whether study is correlational, experimental, or quasi-experimental; the rationale for the study type; and identification of a study question appropriate for the dataset is included but lacks explanation and justification.	Discussion of whether study is correlational, experimental, or quasi-experimental; the rationale for the study type; and identification of a study question appropriate for the dataset is complete and includes explanation and justification.	Discussion of whether study is correlational, experimental, or quasi-experimental; the rationale for the study type; and identification of a study question appropriate for the dataset is extremely thorough and includes substantial explanation and justification.

Mechanics of Writing (includes spelling, punctuation, grammar, language use)	5.0%	Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.	Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.	Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.	Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.	Writer is clearly in command of standard, written, academic English.

Total Weightage	100%

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: Data Management and Descriptive Statistics Discussions

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.

PLEASE:

– minimum of 250 words or more

– strong academic writing / APA style (please use in-text citing and References at end )

– used attached resources or additional ( must be scholarly articles only no older than 5 years ) and reference and in-text cite.

– please be original writing and must answer all parts of question for full credit.

Resources: ( please use 2/3 of the follow to answer (validate) question with additional resources if preferable )

Read “Integrating Quantitative and Qualitative Data in Mixed Methods Research – Challenges and Benefits,” by Almalki, from Journal of Education and Learning (2016).

URL:http://files.eric.ed.gov/fulltext/EJ1110464.pdf

Read “Mapping the Developing Landscape of Mixed Methods Research,” by Creswell, from SAGE Handbook of Mixed Methods in Social and Behavioral Research (2010).

URL:http://methods.sagepub.com.lopes.idm.oclc.org/book/sage-handbook-of-mixed-methods-social-behavioral-research-2e/n2.xml

Read “10 Best Resources on…Mixed Methods Research in Health Systems,” by Ozawa and Pongpirul, from Health Policy Plan (2014).

URL:https://academic.oup.com/heapol/article/29/3/323/581455/10-best-resources-on-mixed-methods-research-in

Read “Guidance for Using Mixed Methods Design in Nursing Practice Research,” by Chiang-Hanisko, Newman, Dyess, Duangporn, and Liehr, from Applied Nursing Research (2016).

URL:http://www.sciencedirect.com.lopes.idm.oclc.org/science/article/pii/S0897189715002402

Read “A General Inductive Approach for Analyzing Qualitative Evaluation Data,” by Thomas, from American Journal of Evaluation (2006).

URL:http://journals.sagepub.com/doi/pdf/10.1177/1098214005283748

Read “Pathways to Malaria Persistence in Remote Central Vietnam: A Mixed Method Study of Health Care and the Community,” by Morrow, Nguyen, Curuana, Biggs, Doan, and Nong, from BMC Public Health (2009).

URL:https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-9-85

Topic 1 DQ 1 Example Answer

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

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.

References:

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

Topic 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:

Corty, E. (2016). Using and interpreting statistics. A practical text for the behavioral, social, and health sciences 3^rd 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/

PUB-550 Practicing Application of Descriptive Statistics in Excel and SPSS Assignment

Description

IMPORTANT NOTE REGARDING WORD LIMIT REQUIREMENTS:

Please note that each and every assignment has its own word limit.

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 links below 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.

You are required to cite at least FOUR sources to complete this assignment. Sources must be published within the last 5 years and appropriate for the discussion question criteria and public health content.

Prepare this assignment according to the guidelines found in the APA Style Guide. An abstract is not required.

While APA style is required, solid academic writing is expected as well, and documentation of sources should be presented using APA formatting guidelines.

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

PLEASE make sure APA citation and permalink for articles are complete and correct.

PLEASE add the links/sites below to the reference list if you use any of these readings and make sure everything is in proper APA format.

https://apastyle.apa.org/learn/quick-guide-on-refe…

Read Chapters 4 and 5 in Using and Interpreting Statistics: A Practical Text for the Behavioral, Social, and Health Sciences.

URL:

http://www.gcumedia.com/digital-resources/macmillan-learning/2016/using-and-interpreting-statistics_a-practical-text-for-the-behavioral-social-and-health-sciences_ebook_3e.php

View “The Best Stats You’ve Ever Seen,” by Rosling (2006), located on the TED website.

URL:

https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen

View the following tutorials from the SPSS Tutorials media.

Frequency Tables and Histograms
Descriptive Statistics

URL:

http://lc.gcumedia.com/psy380/spss-tutorials/v1.1/

View the following terms in The Visual Learner: Statistics media.

Confounding
Histogram

View the following calculations in The Visual Learner: Statistics media.

Frequency Distribution
Measure of Center ‘Mean’
Measure of Center ‘Median’
Measure of Center ‘Mode’
Range and Standard Deviation
Variance

URL:

http://lc.gcumedia.com/hlt362v/the-visual-learner/the-visual-learner-v2.1.html

The “Sampling Distribution” simulation, located on the Rice Virtual Last in Statistics website, will be used for a discussion question in this topic.

URL:

http://onlinestatbook.com/stat_sim/sampling_dist/index.html

For assistance with using Excel software, use the “Excel Training: Create and Format PivotTables and Pivot Charts” video library, located on the Microsoft website.

URL:

https://support.office.com/en-us/article/Video-Create-PivotTables-74ce8afc-2446-4816-80ee-20ca7fb71793?ui=en-US&rs=en-US&ad=US

MUST have at least 4 citations with the page numbers and 4 references in APA format.(The List of References should not be older than 2016 and should not be included in the word count.)

Be sure to support your postings and responses with specific references to the Learning Resources.

It is important that you cover all the topics identified in the assignment. Covering the topic does not mean mentioning the topic BUT presenting an explanation from the context of ethics and the readings for this class

I am a stickler for good organization in everything. I do not want to have to dig for your answers. For instance, if an assignment asks you to provide three examples of something, I suggest that you number them 1-3 so I can find them easily. I also expect that when you submit something as a narrative, you pay attention to how you organize your thoughts: use paragraphs with a topic sentence and supporting sentences; and change paragraphs whenever you introduce a new idea. Also, if there are multiple parts to an assignment, use sub-heads within the paper to organize them.

To get maximum points you need to follow the requirements listed for this assignments 1) look at the word/page limits 2) review and follow APA rules 3) create subheadings to identify the key sections you are presenting and 4) Free from typographical and sentence construction errors.

REMEMBER IN APA FORMAT JOURNAL TITLES AND VOLUME NUMBERS ARE ITALICIZED.

PUB-550 Practicing Application of Descriptive Statistics in Excel and SPSS Assignment Rubric

Course Code	Class Code	Assignment Title	Total Points
PUB-550	PUB-550-O500	Practicing Application of Descriptive Statistics in Excel and SPSS	40.0

Criteria	Percentage	Unsatisfactory (0.00%)	Less than Satisfactory (74.00%)	Satisfactory (79.00%)	Good (87.00%)	Excellent (100.00%)	Comments	Points Earned
Content	100.0%
Part 1 – Summary Tables	20.0%	Summary tables illustrating mean, median, and mode for the Annual Income and Age variables in Excel and SPSS are not included.	Summary tables illustrating mean, median, and mode for the Annual Income and Age variables in Excel and SPSS are incomplete or incorrect.	Summary tables illustrating mean, median, and mode for the Annual Income and Age variables in Excel and SPSS are partially complete and correct.	Summary tables illustrating mean, median, and mode for the Annual Income and Age variables in Excel and SPSS are mostly complete and correct.	Summary tables illustrating mean, median, and mode for the Annual Income and Age variables in Excel and SPSS are complete and correct.

Part 1 – Histograms	15.0%	Histograms showing the distribution of Annual Income and Age in Excel and SPSS are not included.	Histograms showing the distribution of Annual Income and Age in Excel and SPSS are incomplete or incorrect.	Histograms showing the distribution of Annual Income and Age in Excel and SPSS are partially complete and correct.	Histograms showing the distribution of Annual Income and Age in Excel and SPSS are mostly complete and correct.	Histograms showing the distribution of Annual Income and Age in Excel and SPSS are complete and correct.

Part 1 – Frequency Table	15.0%	Calculations for the frequency table, including counts and percentages for smoking status, employment status, exercise level, and education level in Excel and SPSS, are not included.	Calculations for the frequency table, including counts and percentages for smoking status, employment status, exercise level, and education level in Excel and SPSS, are incomplete or incorrect.	Calculations for the frequency table, including counts and percentages for smoking status, employment status, exercise level, and education level in Excel and SPSS, are partially complete and correct.	Calculations for the frequency table, including counts and percentages for smoking status, employment status, exercise level, and education level in Excel and SPSS, are mostly complete and correct.	Calculations for the frequency table, including counts and percentages for smoking status, employment status, exercise level, and education level in Excel and SPSS, are complete and correct.

Part 2 – Sampling Strategy	15.0%	Discussion of the sampling strategy used and whether it resulted in a representative sample is not included.	Discussion of the sampling strategy used and whether it resulted in a representative sample is incomplete or incorrect.	Discussion of the sampling strategy used and whether it resulted in a representative sample is included but lacks explanation and justification.	Discussion of the sampling strategy used and whether it resulted in a representative sample is complete and includes explanation and justification.	Discussion of the sampling strategy used and whether it resulted in a representative sample is extremely thorough with substantial explanation and justification.

Part 2 – Descriptive Statistics	15.0%	Discussion about what was able to be ascertained about the sample from the descriptive statistics is not included.	Discussion about what was able to be ascertained about the sample from the descriptive statistics is incomplete or incorrect.	Discussion about what was able to be ascertained about the sample from the descriptive statistics is included but lacks explanation and relevant details.	Discussion about what was able to be ascertained about the sample from the descriptive statistics is complete and includes explanation and relevant details.	Discussion about what was able to be ascertained about the sample from the descriptive statistics is extremely thorough with substantial explanation and relevant details.

Part 2 – Other Variables	15.0%	Explanation of other variables the research team could have included to gain better understanding of the population is not included.	Explanation of other variables the research team could have included to gain better understanding of the population is incomplete or incorrect.	Explanation of other variables the research team could have included to gain better understanding of the population is included but lacks relevant details and justification.	Explanation of other variables the research team could have included to gain better understanding of the population is complete and includes relevant details and justification.	Explanation of other variables the research team could have included to gain better understanding of the population is extremely thorough and includes substantial relevant details and justification.

Mechanics of Writing (includes spelling, punctuation, grammar, language use)	5.0%	Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.	Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.	Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.	Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.	Writer is clearly in command of standard, written, academic English.

Total Weightage	100%

PUB-550 Calculating Confidence Intervals Assignment

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

PUB-550 Calculating Confidence Intervals Assignment Rubric

Course Code	Class Code	Assignment Title	Total Points
PUB-550	PUB-550-O500	Calculating Confidence Intervals	70.0

Criteria	Percentage	Unsatisfactory (0.00%)	Less than Satisfactory (74.00%)	Satisfactory (79.00%)	Good (87.00%)	Excellent (100.00%)	Comments	Points Earned
Content	100.0%
Question 1	15.0%	Calculation of probability of an individual being 60 years or older, including associated hand calculations and screenshots, is not included.	Calculation of probability of an individual being 60 years or older, including associated hand calculations and screenshots, is incomplete or incorrect.	Calculation of probability of an individual being 60 years or older, including associated hand calculations and screenshots, is partially complete and correct.	Calculation of probability of an individual being 60 years or older, including associated hand calculations and screenshots, is mostly complete and correct.	Calculation of probability of an individual being 60 years or older, including associated hand calculations and screenshots, is complete and correct.

Question 2	15.0%	Mean and standard deviation for Age and associated Excel output table are not included.	Mean and standard deviation for Age and associated Excel output table are incomplete or incorrect.	Mean and standard deviation for Age and associated Excel output table are partially complete and correct.	Mean and standard deviation for Age and associated Excel output table are mostly complete and correct.	Mean and standard deviation for Age and associated Excel output table are complete and correct.

Question 3	15.0%	Hand calculations for the standard error of the mean are not included.	Hand calculations for the standard error of the mean are incomplete or incorrect.	Hand calculations for the standard error of the mean are partially complete and correct.	Hand calculations for the standard error of the mean are mostly complete and correct.	Hand calculations for the standard error of the mean are complete and correct.

Question 4	15.0%	Hand calculations for a 95% confidence interval for Age and SPSS output screenshots verifying the answer are not included.	Hand calculations for a 95% confidence interval for Age and SPSS output screenshots verifying the answer are incomplete or incorrect.	Hand calculations for a 95% confidence interval for Age and SPSS output screenshots verifying the answer are partially complete and correct.	Hand calculations for a 95% confidence interval for Age and SPSS output screenshots verifying the answer are mostly complete and correct.	Hand calculations for a 95% confidence interval for Age and SPSS output screenshots verifying the answer are complete and correct.

Question 5	35.0%	Interpretation of the confidence interval for age, explanation of the three pieces of information needed to calculate the confidence interval, and discussion of the age cut-off are not included.	Interpretation of the confidence interval for age, explanation of the three pieces of information needed to calculate the confidence interval, and discussion of the age cut-off are incomplete or incorrect.	Interpretation of the confidence interval for age, explanation of the three pieces of information needed to calculate the confidence interval, and discussion of the age cut-off are included but lack details and supporting information.	Interpretation of the confidence interval for age, explanation of the three pieces of information needed to calculate the confidence interval, and discussion of the age cut-off are complete and include details and supporting information.	Interpretation of the confidence interval for age, explanation of the three pieces of information needed to calculate the confidence interval, and discussion of the age cut-off are extremely thorough with substantial details and supporting information.

Mechanics of Writing (includes spelling, punctuation, grammar, language use)	5.0%	Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.	Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.	Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.	Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.	Writer is clearly in command of standard, written, academic English.

Total Weightage	100%

Topic 2: Data Management and Descriptive Statistics Discussions

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 Example Answer

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

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

PUB-550 Topic 3: Hypothesis Testing Assignment

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.

Identify a peer-reviewed article from one of the data sources available on the “Data Resource Document.” You will need to visit the various websites listed on the document to search for a research article that is of interest to you. Include the link to the selected article in a 750-1,000 word summary that address the following as it relates to the article.

What test statistic did the researchers use? What did the authors set as the significance level of the test statistic?
What was the null hypothesis? What was the alternative hypothesis? Were these clearly stated in the article, or did you have to extrapolate based on the background in the article?
Discuss potential bias that may have resulted from the study design and data collection that could have affected the validity of the test.
Summarize the story told by the data used in this study as it applies to the larger population
Comment on whether the authors used the interpretation approach discussed in the textbook, including (a) summarize why the study was done, (b) discuss the factual results, (c) explain what the results mean, and (d) make suggestions for future research.

Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

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-Data Resource Document.docx

PUB-550 Topic 3: Hypothesis Testing Assignment

Course Code	Class Code	Assignment Title	Total Points
PUB-550	PUB-550-O500	Hypothesis Testing	100.0

Criteria	Percentage	Unsatisfactory (0.00%)	Less than Satisfactory (74.00%)	Satisfactory (79.00%)	Good (87.00%)	Excellent (100.00%)	Comments	Points Earned
Content	100.0%
Link to Peer-Reviewed Article	5.0%	Link to selected peer-reviewed article is not included.	NA	NA	NA	Link to selected peer-reviewed article is complete and correct.

Test Statistic	15.0%	Discussion of test statistic and significance level used by the researcher is not included.	Discussion of test statistic and significance level used by the researcher is incomplete or incorrect.	Discussion of test statistic and significance level used by the researcher is included but lacks explanation.	Discussion of test statistic and significance level used by the researcher is complete and includes explanation.	Discussion of test statistic and significance level used by the researcher is extremely thorough and includes substantial explanation.

Hypotheses	20.0%	Discussion of the null and alternative hypotheses and whether or not these were clearly stated in the article is not included.	Discussion of the null and alternative hypotheses and whether or not these were clearly stated in the article is incomplete or incorrect.	Discussion of the null and alternative hypotheses and whether or not these were clearly stated in the article is included but lacks explanation.	Discussion of the null and alternative hypotheses and whether or not these were clearly stated in the article is complete and includes explanation.	Discussion of the null and alternative hypotheses and whether or not these were clearly stated in the article is extremely thorough and includes substantial explanation.

Potential Bias	15.0%	Discussion of potential bias that may have resulted from the study design and data collection and could have affected the validity of the test is not included.	Discussion of potential bias that may have resulted from the study design and data collection and could have affected the validity of the test is incomplete or incorrect.	Discussion of potential bias that may have resulted from the study design and data collection and could have affected the validity of the test is included but lacks explanation and relevant details.	Discussion of potential bias that may have resulted from the study design and data collection and could have affected the validity of the test is complete and includes explanation and relevant details.	Discussion of potential bias that may have resulted from the study design and data collection and could have affected the validity of the test is extremely thorough with substantial explanation and relevant details.

Data Story	15.0%	Summary of the story told by the data used in the study as it applies to the larger population is not included.	Summary of the story told by the data used in the study as it applies to the larger population is incomplete or incorrect.	Summary of the story told by the data used in the study as it applies to the larger population is included but lacks explanation and relevant details.	Summary of the story told by the data used in the study as it applies to the larger population is complete and includes explanation and relevant details.	Summary of the story told by the data used in the study as it applies to the larger population is extremely thorough with substantial explanation and relevant details.

Interpretation Approach	20.0%	Discussion of whether the author used the interpretation approach discussed in the textbook is not included.	Discussion of whether the author used the interpretation approach discussed in the textbook is incomplete or incorrect.	Discussion of whether the author used the interpretation approach discussed in the textbook is included but lacks explanation and relevant details.	Discussion of whether the author used the interpretation approach discussed in the textbook is complete and includes explanation and relevant details.	Discussion of whether the author used the interpretation approach discussed in the textbook is extremely thorough with substantial explanation and relevant details.

Mechanics of Writing (includes spelling, punctuation, grammar, language use)	5.0%	Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.	Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.	Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.	Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.	Writer is clearly in command of standard, written, academic English.

Documentation of Sources (citations, footnotes, references, bibliography, etc., as appropriate to assignment and style)	5.0%	Sources are not documented.	Documentation of sources is inconsistent or incorrect, as appropriate to assignment and style, with numerous formatting errors.	Sources are documented, as appropriate to assignment and style, although some formatting errors may be present.	Sources are documented, as appropriate to assignment and style, and format is mostly correct.	Sources are completely and correctly documented, as appropriate to assignment and style, and format is free of error.

Total Weightage	100%

PUB-550 Topic 3: Data Resource Document

CDC WONDER

Explore the CDC WONDER website.

https://wonder.cdc.gov/

Gapminder

Explore the Gapminder website.

http://www.gapminder.org/

Behavioral Risk Factor Surveillance System

Explore the Behavioral Risk Factor Surveillance System, located on the CDC website.

https://www.cdc.gov/brfss/index.html

PovcalNet

Explore the PovcalNet: An Online Analysis Tool for Global Poverty Monitoring page of The World Bank website.

http://iresearch.worldbank.org/PovcalNet/home.aspx

Demographic and Health Survey Data

Explore the Demographic and Health Survey (DHS) data website.

http://dhsprogram.com/data/

Alcohol-Related Disease Impact Application

Explore the Alcohol-Related Disease Impact Application (ARDI) page of the CDC website.

https://nccd.cdc.gov/DPH_ARDI/default/default.aspx

Public Health Partners

Explore the Health Data Tools and Statistics page of the Public Health Partners website. This site provides many public health data sources.

https://phpartners.org/health_stats.html

National Health Interview Survey

Explore the National Health Interview Survey page of the CDC website. Review the documents.

https://www.cdc.gov/nchs/nhis/index.htm

Global School-Based Student Health Survey

Explore the purpose and methodology of the international Global School-Based Student Health Survey, located on the World Health Organization (WHO) website.

http://www.who.int/chp/gshs/en/

Youth Risk Behavior Surveillance System

Explore the methods, data, and documentation of the Youth Risk Surveillance System, located on the CDC website.

https://www.cdc.gov/healthyyouth/data/yrbs/

Topic 3: Data Management and Descriptive Statistics Discussions

Topic 3 DQ 1

Discuss the four potential outcomes of hypothesis testing and describe what is meant by type 1 and type 2 errors. Provide an example of when these errors might occur.

Topic 3 DQ 1 Example Answer

Banerjee et al., (2009) study found the following: Hypothesis testing is an important activity of empirical research and evidence-based medicine. A well worked up hypothesis is half the answer to the research question. For this, both knowledge of the subject derived from extensive review of the literature and working knowledge of basic statistical concepts are desirable. The present paper discusses the methods of working up a good hypothesis and statistical concepts of hypothesis testing (p.127)

Banerjee et al., (2009) study found the following: Just like a judge’s conclusion, an investigator’s conclusion may be wrong. Sometimes, by chance alone, a sample is not representative of the population. Thus, the results in the sample do not reflect reality in the population, and the random error leads to an erroneous inference. A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population. Although type I and type II errors can never be avoided entirely, the investigator can reduce their likelihood by increasing the sample size (the larger the sample, the lesser is the likelihood that it will differ substantially from the population) (p.127).

Banerjee et al., (2009) study found the following: False-positive and false-negative results can also occur because of bias (observer, instrument, recall, etc.). (Errors due to bias, however, are not referred to as type I and type II errors.) Such errors are troublesome, since they may be difficult to detect and cannot usually be quantified (p.127).

Reference

Banerjee, A., Chitnis, U. B., Jadhav, S. L., Bhawalkar, J. S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Industrial psychiatry journal, 18(2), 127–131. doi:10.4103/0972-6748.62274

Topic 3 DQ 1

The four potential outcome of hypothesis testing are

Correct inference: Conclude that there is an association when one does exist in the population.
Correct inference:Conclude that there is no association when one does not exist in the population.
Incorrect inference (type 1): Conclude that there is an association when there actually is none (false positive).
Incorrect inference (type 2): Conclude that there is no association when there is one (false negative) (Banerjee, Chitnis, Jadhav, Bhawalkar, & Chaudhruy, 2009)

When the sample is not representative of the population this leads to an erroneous inference and type 1 or type 2 errors. A type 1 error is a false positive, or an investigator rejecting a null hypothesis that is actually true (Banerjee, Chitnis, Jadhav, Bhawalkar, & Chaudhruy, 2009). A type 2 error is the opposite a false negative, an investigator rejecting a null hypothesis that is actually false in the population. These errors are impossible to completely avoid but the likelihood can be decreased by increasing the sample size and (Banerjee, Chitnis, Jadhav, Bhawalkar, & Chaudhruy, 2009).

Bibliography

Banerjee, A., Chitnis, U., Jadhav, S., Bhawalkar, J., & Chaudhruy, S. (2009). Hypothesis Testing, type 1 and type II errors. Indian Psychiatry , 127-131.

Topic 3 DQ 2

Review the Healthy People 2020 website. Identify one of the health issues and propose a scenario that would use a z-test as the first step in the six steps of hypothesis testing. Discuss the remaining five steps based on your scenario, including clearly articulating the null and alternative hypotheses for your scenario.

Topic 3 DQ 2 Example Answer

Sphweb (n.d) study found the following: The Centers for Disease Control (CDC) reported on trends in weight, height and body mass index from the 1960’s through 2002.¹ The general trend was that Americans were much heavier and slightly taller in 2002 as compared to 1960; both men and women gained approximately 24 pounds, on average, between 1960 and 2002. In 2002, the mean weight for men was reported at 191 pounds. Suppose that an investigator hypothesizes that weights are even higher in 2006 (i.e., that the trend continued over the subsequent 4 years). The research hypothesis is that the mean weight in men in 2006 is more than 191 pounds. The null hypothesis is that there is no change in weight, and therefore the mean weight is still 191 pounds in 2006 (n.d).

Sphweb (n.d) study found the following: In order to test the hypotheses, we select a random sample of American males in 2006 and measure their weights. Suppose we have resources available to recruit n=100 men into our sample. We weigh each participant and compute summary statistics on the sample data. Suppose in the sample we determine the following:

n=100
s=25.6

Sphweb (n. d). study found the following: Do the sample data support the null or research hypothesis? The sample mean of 197.1 is numerically higher than 191. However, is this difference more than would be expected by chance? In hypothesis testing, we assume that the null hypothesis holds until proven otherwise. We therefore need to determine the likelihood of observing a sample mean of 197.1 or higher when the true population mean is 191 (i.e., if the null hypothesis is true or under the null hypothesis). We can compute this probability using the Central Limit Theorem. Specifically, (n.d.).

Review of the “Nutrition and Weight Status” on the Healthy People 2020

Obesity in Adults (NWS-9)

Healthy People 2020 objective NWS-9 tracks the proportion of adults with obesity (BMI ≥ 30).
- HP2020 Baseline: In 2005–2008, the rate of obesity was 33.9% among adults aged 20 years and over (age adjusted).
- HP2020 Target: 30.5%, a 10% improvement over the baseline.
- Most Recent: In 2013–2016, the rate of obesity was 38.6% among adults aged 20 years and over (age adjusted).
Males aged 20 years and over had a lower rate of obesity than females (36.5% versus 40.5%, age adjusted) in 2013–2016. The rate for females was 11.0% higher than that for males.
Among racial and ethnic groups, the non-Hispanic Asian population had the lowest (best) rate of obesity, 12.5% of adults aged 20 years and over (age adjusted) in 2013–2016. Rates (age adjusted) for other racial and ethnic groups were:
- 0% among the non-Hispanic black population; more than 3.5 times the best group rate
- 9% among the Hispanic population; more than 3.5 times the best group rate
- 1% among the non-Hispanic white population; 3 times the best group rate

Reference

Explore the Healthy People 2020 website.

URL:

https://www.healthypeople.gov/

http://www.real-statistics.com/hypothesis-testing/null-hypothesis/

http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_HypothesisTest-Means-Proportions/BS704_HypothesisTest-Means-Proportions_print.html

PUB-550 Application of the t-Test Assignment

Topic 4: The t-Test

Objectives:

Differentiate the use of three types of t-tests.
Explain the assumptions of the t-test.
Interpret t-test results to determine the difference in means.

Description

IMPORTANT NOTE REGARDING WORD LIMIT REQUIREMENTS:

Please note that each and every assignment has its own word limit.

The purpose of this assignment is to learn how to apply the t-test to a sample dataset.

For this assignment, students will use IBM SPSS Statistics and the “Example Dataset.” attached

Using the “Example Dataset” and SPSS, apply the t-test to assess the following statement: “Men and women have different incomes in this city.”

Show your calculations and copy of the SPSS output in a Word document.

In a separate 300-500 Word document, address the following questions:

Describe what t-test is the most appropriate and explain why. Discuss whether you used a one-tailed or two-tailed test and explain why.
Using SPSS, calculate the t-test and provide the test statistic and critical value assuming an alpha of .05.
Calculate the effect size using r².
Interpret the results by (a) stating the reason the study or test was done, (b) presenting the main results, (c) explaining what the results mean, and (d) making suggestions for future research.

Submit both Word documents to the instructor.

General Requirements

You are required to cite at least FIVE sources to complete this assignment. Sources must be published within the last 5 years and appropriate for the discussion question criteria and public health content.

Prepare this assignment according to the guidelines found in the APA Style Guide. An abstract is not required.

While APA style is required, solid academic writing is expected as well, and documentation of sources should be presented using APA formatting guidelines.

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

PLEASE make sure APA citation and permalink for articles are complete and correct.

PLEASE add the links/sites below to the reference list if you use any of these readings and make sure everything is in proper APA format.

https://apastyle.apa.org/learn/quick-guide-on-refe…

Read Chapters 7-9 in Using and Interpreting Statistics: A Practical Text for the Behavioral, Social, and Health Sciences.

URL:

http://www.gcumedia.com/digital-resources/macmillan-learning/2016/using-and-interpreting-statistics_a-practical-text-for-the-behavioral-social-and-health-sciences_ebook_3e.php

View the following tutorials from the SPSS Tutorials media.

One-Sample t-test
Matched Pairs t-test
Independent Samples t-test

URL:

http://lc.gcumedia.com/psy380/spss-tutorials/v1.1/

View the following calculations in The Visual Learner: Statistics media.

Introduction to t-test
One Sample t-test
Two Sample t-test

URL:

http://lc.gcumedia.com/hlt362v/the-visual-learner/the-visual-learner-v2.1.html

MUST have 300 to 500 WORDS and at least 5 citations with the page numbers and 5 references in APA format. (The List of References should not be older than 2016 and should not be included in the word count.)

Be sure to support your postings and responses with specific references to the Learning Resources.

REMEMBER IN APA FORMAT JOURNAL TITLES AND VOLUME NUMBERS ARE ITALICIZED.

PUB-550 Application of the t-Test Assignment Rubric

Course Code	Class Code	Assignment Title	Total Points
PUB-550	PUB-550-O500	Application of the t-Test	70.0

Criteria	Percentage	Unsatisfactory (0.00%)	Less than Satisfactory (74.00%)	Satisfactory (79.00%)	Good (87.00%)	Excellent (100.00%)	Comments	Points Earned
Content	100.0%
t-Test Selection	25.0%	Discussion and justification of which t-test is the most appropriate is not included.	Discussion and justification of which t-test is the most appropriate is incomplete or incorrect.	Discussion and justification of which t-test is the most appropriate is included but lacks explanation and supporting details.	Discussion and justification of which t-test is the most appropriate is complete and includes explanation and supporting details.	Discussion and justification of which t-test is the most appropriate is extremely thorough and includes substantial explanation and supporting details.

Test Statistic and Critical Value	25.0%	The t-test calculations, SPSS output, and critical value are not included.	The t-test calculations, SPSS output, and critical value are incomplete or incorrect.	The t-test calculations, SPSS output, and critical value are partially complete and correct.	The t-test calculations, SPSS output, and critical value are mostly complete and correct.	The t-test calculations, SPSS output, and critical value are complete and correct.

Effect Size	15.0%	Effect size calculation is not included.	Effect size calculation is incomplete or incorrect.	Effect size calculation is partially complete and correct.	Effect size calculation is mostly complete and correct.	Effect size calculation is complete and correct.

Interpretation of Results (a) stating the reason the study or test was done, (b) presenting the main results, (c) explaining what the results mean, and (d) making suggestions for future research	30.0%	Interpretation of results, including steps a-d, is not included.	Interpretation of results, including steps a-d, is incomplete or incorrect.	Interpretation of results, including steps a-d, is included but lacks explanation and relevant details.	Interpretation of results, including steps a-d, is complete and includes explanation and relevant details.	Interpretation of results, including steps a-d, is extremely thorough with substantial explanation and relevant details.

Mechanics of Writing (includes spelling, punctuation, grammar, language use)	5.0%	Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.	Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.	Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.	Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.	Writer is clearly in command of standard, written, academic English.

Total Weightage	100%

Topic 4: Data Management and Descriptive Statistics Discussions

Topic 4 DQ 1

Compare the three types of t-tests by discussing when each is most appropriate to use and which types of questions each type of t-test best answers. Include specific examples to illustrate the appropriate use of each test.

Topic 4 DQ 1 Example Answer

In statistics, t-tests are a type of hypothesis test that allows you to compare means. They are called t-tests because each t-test boils your sample data down to one number, the t-value. If you understand how t-tests calculate t-values, you’re well on your way to understanding how these tests work.

In this series of posts, I’m focusing on concepts rather than equations to show how t-tests work. However, this post includes two simple equations that I’ll work through using the analogy of a signal-to-noise ratio (Editor, M.B.,2019, n.d.).

Both the signal and noise values are in the units of your data. If your signal is 6 and the noise is 2, your t-value is 3. This t-value indicates that the difference is 3 times the size of the standard error. However, if there is a difference of the same size but your data have more variability (6), your t-value is only 1. The signal is at the same scale as the noise (Editor, M.B.,2019, n.d.).

In this manner, t-values allow you to see how distinguishable your signal is from the noise. Relatively large signals and low levels of noise produce larger t-values. If the signal does not stand out from the noise, it’s likely that the observed difference between the sample estimate and the null hypothesis value is due to random error in the sample rather than a true difference at the population level (Editor, M.B.,2019, n.d.). PUB-550: Application and Interpretation of Public Health Data

Many people are confused about when to use a paired t-test and how it works. I’ll let you in on a little secret. The paired t-test and the 1-sample t-test are actually the same test in disguise! As we saw above, a 1-sample t-test compares one sample mean to a null hypothesis value. A paired t-test simply calculates the difference between paired observations (e.g., before and after) and then performs a 1-sample t-test on the differences (Editor, M.B.,2019, n.d.).

Understanding that the paired t-test simply performs a 1-sample t-test on the paired differences can really help you understand how the paired t-test works and when to use it. You just need to figure out whether it makes sense to calculate the difference between each pair of observations (Editor, M.B.,2019, n.d.).

Reference

Editor, M. B. (n.d.). Understanding t-Tests: 1-sample, 2-sample, and Paired t-Tests. Retrieved September 27, 2019, from https://blog.minitab.com/blog/adventures-in-statistics-2/understanding-t-tests-1-sample-2-sample-and-paired-t-tests.

Topic 4 DQ 2

Step 2 of hypothesis testing involves reviewing the assumptions of the test you selected. Discuss the three assumptions of the t-test. Provide an example of the assumption that is not robust to violations and a situation when the assumption is violated. PUB-550: Application and Interpretation of Public Health Data

Topic 4 DQ 2 Example Answer

Hoekstra (2012) study found the following:Using a statistical test is one of the frequently mentioned methods of checking for violations of assumptions (for an overview of statistical methodology textbooks that directly or indirectly advocate this method, see e.g., Hayes and Cai, 2007). However, it has also been argued that it is not appropriate to check assumptions by means of tests (such as Levene’s test) carried out before deciding on which statistical analysis technique to use because such tests compound the probability of making a Type I error (e.g., Schucany and Ng, 2006). Even if one desires to check whether or not an assumption is met, two problems stand in the way. First, assumptions are usually about the population, and in a sample the population is by definition not known. For example, it is usually not possible to determine the exact variance of the population in a sample-based study, and therefore it is also impossible to determine that two population variances are equal, as is required for the assumption of equal variances (also referred to as the assumption of homogeneity of variances) to be satisfied. Second, because assumptions are usually defined in a very strict way (e.g., all groups have equal variances in the population, or the variable is normally distributed in the population), the assumptions cannot reasonably be expected to be satisfied(p.1)Hoekstra (2012) study found the following:The assumptions of normality and of homogeneity of variances are required to be met for the t-test for independent group means, one of the most widely used statistical tests (Hayes and Cai, 2007), as well as for the frequently used techniques ANOVA and regression (Kashy et al., 2009). The assumption of normality is that the scores in the population in case of a t-test or ANOVA, and the population residuals in case of regression, be normally distributed. The assumption of homogeneity of variance requires equal population variances per group in case of a t-test or ANOVA, and equal population variances for every value of the independent variable for regression. Although researchers might be tempted to think that most statistical procedures are relatively robust against most violations, several studies have shown that this is often not the case, and that in the case of one-way ANOVA, unequal group sizes can have a negative impact on the technique’s robustness (e.g., Havlicek and Peterson, 1977; Wilcox, 1987; Lix et al., 1996)(p.2)

Reference

Hoekstra, R., Kiers, H. A., & Johnson, A. (2012). Are assumptions of well-known statistical techniques checked, and why (not)?. Frontiers in psychology, 3, 137. doi:10.3389/fpsyg.2012.00137

PUB-550 Application of ANOVA Assignment

Topic 5: ANOVA Testing

Objectives:

Compare and contrast the types of ANOVA tests and their application.
Apply the results of an ANOVA to determine statistical difference between means and potential interactions.
PUB-550: Application and Interpretation of Public Health Data

Description

The purpose of this assignment is to calculate and interpret an ANOVA table. For this assignment, use IBM SPSS Statistics.

Part 1:

Using the “Example Dataset,” assess this statement using ANOVA: “People with different levels of education exercise for different amounts of time during the week.”

Select and conduct the appropriate ANOVA test to assess the statement. Export the ANOVA table to a Word document.

Part 2:

In 300-500 words, discuss the following regarding the use of ANOVA.

Describe when the use of ANOVA is more appropriate than the use of a t-test.
Describe which ANOVA test you used and why.
Interpret the results by (a) stating the reason the study or test was done; (b) presenting the main results, including explaining the within and between subjects variation and the F-ratio from the ANOVA table; (c) explaining what the results mean, including discussing whether there is a statistically significant difference between education groups and amount of exercise; and (d) making suggestions for future research.

Submit one Word document for the Part 1 assignment content and a second Word document for Part 2 of the assignment. Submit both Word documents to the instructor.

General Requirements

Prepare this assignment according to the guidelines found in the APA Style Guide. An abstract is not required.

While APA style is required, solid academic writing is expected as well, and documentation of sources should be presented using APA formatting guidelines.

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

PLEASE make sure APA citation and permalink for articles are complete and correct.

PLEASE add the links/sites below to the reference list if you use any of these readings and make sure everything is in proper APA format.

https://apastyle.apa.org/learn/quick-guide-on-refe…

Read Chapters 10-12 in Using and Interpreting Statistics: A Practical Text for the Behavioral, Social, and Health Sciences.

URL:

http://www.gcumedia.com/digital-resources/macmillan-learning/2016/using-and-interpreting-statistics_a-practical-text-for-the-behavioral-social-and-health-sciences_ebook_3e.php

View the following tutorials from the SPSS Tutorials media.

One-Way ANOVA

Two-Way ANOVA-Between Groups

URL:

http://lc.gcumedia.com/psy380/spss-tutorials/v1.1/

View the following calculations in The Visual Learner: Statistics media.

One Way ANOVA

Effect Size One Way ANOVA

Two Way ANOVA

Effect Size Two Way ANOVA

URL:

http://lc.gcumedia.com/hlt362v/the-visual-learner/the-visual-learner-v2.1.html

MUST have 300 to 500 WORDS and at least 5 citations with the page numbers and 5 references in APA format.(The List of References should not be older than 2016 and should not be included in the word count.)

Be sure to support your postings and responses with specific references to the Learning Resources.

REMEMBER IN APA FORMAT JOURNAL TITLES AND VOLUME NUMBERS ARE ITALICIZED.

PUB-550 Application of ANOVA Assignment Rubric

Course Code	Class Code	Assignment Title	Total Points
PUB-550	PUB-550-O500	Application of ANOVA	70.0

Criteria	Percentage	Unsatisfactory (0.00%)	Less than Satisfactory (74.00%)	Satisfactory (79.00%)	Good (87.00%)	Excellent (100.00%)	Comments	Points Earned
Content	100.0%
Part 1 – ANOVA Table	20.0%	ANOVA table documenting the ANOVA test conducted using SPSS is not included.	ANOVA table documenting the ANOVA test conducted using SPSS is incomplete or incorrect.	ANOVA table documenting the ANOVA test conducted using SPSS is partially complete and correct.	ANOVA table documenting the ANOVA test conducted using SPSS is mostly complete and correct.	ANOVA table documenting the ANOVA test conducted using SPSS is complete and correct.

Part 2 – Appropriate Use of ANOVA	20.0%	Discussion of when the use of ANOVA is more appropriate than the use of a t-test is not included.	Discussion of when the use of ANOVA is more appropriate than the use of a t-test is incomplete or incorrect.	Discussion of when the use of ANOVA is more appropriate than the use of a t-test is included but lacks explanation and supporting details.	Discussion of when the use of ANOVA is more appropriate than the use of a t-test is complete and includes explanation and supporting details.	Discussion of when the use of ANOVA is more appropriate than the use of a t-test is extremely thorough and includes substantial explanation and supporting details.

Part 2 – ANOVA Test Selection	20.0%	Description and justification of the ANOVA test are not included.	Description and justification of the ANOVA test are incomplete or incorrect.	Description and justification of the ANOVA test are included but lack explanation and supporting details.	Description and justification of the ANOVA test are complete and include explanation and supporting details.	Description and justification of the ANOVA test are extremely thorough and include substantial explanation and supporting details.

Part 2 – Interpretation of Results (a) stating the reason the study or test was done, ( b) presenting the main results, including explaining the within and between subjects variation and the F-ratio from the ANOVA table, (c) explaining what the results mean, including discussing whether there is a statistically significant difference between education groups and amount of exercise, and (d) making suggestions for future research	35.0%	Interpretation of results, including steps a-d, is not included.	Interpretation of results, including steps a-d, is incomplete or incorrect.	Interpretation of results, including steps a-d, is included but lacks explanation and relevant details.	Interpretation of results, including steps a-d, is complete and includes explanation and relevant details.	Interpretation of results, including steps a-d, is extremely thorough with substantial explanation and relevant details.

Mechanics of Writing (includes spelling, punctuation, grammar, language use)	5.0%	Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.	Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.	Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.	Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.	Writer is clearly in command of standard, written, academic English.

Total Weightage	100%

Topic 5: Data Management and Descriptive Statistics Discussions

Topic 5 DQ 1

Compare the various types of ANOVA by discussing when each is most appropriate for use and which types of research questions each best answers. Include specific examples to illustrate the appropriate use of each test and how interaction is assessed using ANOVA.

Topic 5 DQ 1

Analysis of variance (ANOVA) is a unit of statistical tests used to compare the means of two or more groups (Corty, 2016). There are two types of tests: between subjects, one-way ANOVA and between subjects, two-way ANOVA. ‘Between subjects’ means independent samples and ‘way’ means explanatory view. ‘Way’ can be grouping variables or independent variables (Corty, 2016).

The one-way ANOVA is a statistical test used when comparing the means of two or more independent samples when there is only one explanatory variable (Corty, 2016). A one-way ANOVA is most appropriate when used to assess the differences in one continuous variable between one grouping variable (Statistics Solutions, 2020). One- way ANOVA allows more groups to be compared at once, allowing more complex questions to be addressed (Corty, 2016). For example, a one-way ANOVA would be appropriate if the goal of research is to assess for differences in job satisfaction levels between ethnicities (Statistics Solutions, 2020). This type of example would require a question regarding one dependent variable, job satisfaction, and one independent variable, ethnicity.

The two-way ANOVA allows researchers to examine the impact of two explanatory variables at one time (Corty, 2016). The two-way ANOVA is most appropriate to use when there are two or more influencing factors at one time. A two-way ANOVA answer the most complex questions involving multiple influencing factors (Corty, 2016). For example, a researcher performed a study on factors that influence altruism and has interest in both how the children are reared and what their nervous systems are like, nurture vs. nature (Corty, 2016). The study was preformed using adoptive children. Below is the design stud with multiple levels of altruism as the influencing factors:

Adoptive Parents

High on Altruism

Adoptive Parents

Medium on Altruism

Adoptive Parents

Low on Altruism

Birth Parents

High Altruism

Birth Parents

Low Altruism

(Corty, 2016)

References

Corty, E.W. (2016). Using and Interpreting Statistics: A Practical Text for the Behavioral, Social, and Health Sciences. (3^rd Ed.) New York, NY: Worth Publishers

Statistics Solutions. (2020). The Various Forms of ANOVA. Retrieved from https://www.statisticssolutions.com/the-various-forms-of-anova/ PUB-550: Application and Interpretation of Public Health Data

Topic 5 DQ 2

Different types of software can be used for data management. Compare Excel and SPSS and discuss specific SPSS software features that make it preferable to Excel for data management. Provide examples illustrating when electing to use SPSS could be preferable to Excel and vice versa.

5 DQ 2

When it comes to statistical analysis there are a few different types of software that can be used for data management. There are two types used in the PUB 550 course at Grand Canyon University. The first is Excel, which is a spreadsheet software that can also be used for statistical analysis. The other is SPSS, Statistical Package for Social Sciences, is an actual statistical analysis software (Statistics Solutions, 2019). Excel is an easy to use software that allows researchers to format data into a table format or spreadsheets, with rows and columns, and then filter the data using formulas (Mittermeier, 2019). The primary purpose of Excel is to create records of data along with manipulation of the data into visual analysis in preparation for formal presentations and reports. SPSS is specifically made for statistical analysis. The software has been used by researchers for decades to perform quantitative analysis of data, allowing for import of statistical packages from other databases and spreadsheets (Statistics Solutions, 2019). Excel utilizes formulas to perform analyses, that the user is expected to be knowledgeable of, whereas SPSS has specific tools to recode and transform variables without additional knowledge of the user required. SPSS is specific to the social sciences as it allows for comparative studies and statistical techniques at a large scale, although limited as it is unable to perform analyses for large data sets from the medical field for clinical data (Statistics Solutions, 2019). PUB-550: Application and Interpretation of Public Health Data. Although both software are capable of aiding researchers in performing statistical analysis on data they have collected, one is significantly more useful in the type of analysis researchers in public health and other social sciences need. SPSS is designed for data analysis in the social sciences and overall is the most user friendly and forgiving, as errors of accidental overwrites or sorting can be avoided unlike Excel worksheets (Mittermeier, 2019).

References

Mittermeier, E. (2019). Why you should move from Excel to SPSS. Retrieved from https://www.2×4.de/2019/06/11/why-you-should-move-from-excel-to-spss/

Statistics Solutions. (2019). SPSS statistics help. Retrieved from https://www.statisticssolutions.com/spss-statistics-help/

PUB-550 Application of the Pearson Correlation Coefficient and the Chi-Square Test Assignment

Topic 6: Regression

Objectives:

Apply the steps of a regression analysis to determine the linear regression equation and its appropriateness based on the data.
Interpret regression output to predict changes in a dependent variable based on changes in one or more predictor variables.

Description

IMPORTANT NOTE REGARDING WORD LIMIT REQUIREMENTS:

Please note that each and every assignment has its own word limit.

The purpose of this assignment is to practice calculating and interpreting the Pearson correlation coefficient and a chi-square test of independence.

For this assignment, complete Problems 13.132 and 15.88 in the textbook which are also listed below. Include your process for conducting the calculations. You can complete the calculations by hand or using Excel or SPSS. If you use Excel or SPSS, copy and paste your output results into a Word document.

When addressing each textbook problem, provide a response for each of the six steps of hypothesis testing listed below.

Pick a test.
Check the assumptions.
List the hypotheses.
Set the decision rule.
Calculate the test statistic.
Interpret the results. (What was done? What was found? What does it mean? What suggestions exist for future research?)

Submit a Word document with your problem answers to each of the six steps. If Excel or SPSS was used to complete the assignment, submit the second Word document containing the screenshots to the instructor.

QUESTION 13.132

A sociologist wanted to see if there was a relationship between a family’s educational status and the eliteness of the college that their oldest child attended. She measured educational status by counting how many years of education beyond high school the parents had received. In addition, she measured the eliteness of the school by its yearly tuition, in thousands (e.g., 5 = $5,000). She

obtained a random sample of 10 families.

	1	2	3	4	5	6	7	8	9	10
YEARS POST-HS EDUCATION	0	7	8	8	4	5	12	17	8	2
YEARLY TUITION	12	26	33	18	20	7	15	38	41	5

QUESTION 15.88

A political scientist developed a theory that after an election, supporters of the losing candidate

removed the bumper stickers from their cars faster than did supporters of the winning candidate. The day before a presidential election, he randomly selected parking lots, and at each selected parking lot, he randomly selected one car with a bumper sticker and recorded which candidate it supported. The day after the election, he followed the same procedure with a new sample of randomly selected parking lots. For both days, he then classified the bumper stickers as supporting the winning or losing candidate. Below are the results. Use hypothesis testing to see if a difference exists between how winners and losers behave.

	OBSERVED FREQUENCIES	OBSERVED FREQUENCIES
	WIN	LOSE
BEFORE	34	32
AFTER	28	10

General Requirements

Prepare this assignment according to the guidelines found in the APA Style Guide. An abstract is not required.

While APA style is required, solid academic writing is expected as well, and documentation of sources should be presented using APA formatting guidelines.

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.

PLEASE make sure APA citation and permalink for articles are complete and correct.

PLEASE add the links/sites below to the reference list if you use any of these readings and make sure everything is in proper APA format.

https://apastyle.apa.org/learn/quick-guide-on-refe…

Read Chapters 13 and 15 in Using and Interpreting Statistics: A Practical Text for the Behavioral, Social, and Health Sciences.

URL:

http://www.gcumedia.com/digital-resources/macmillan-learning/2016/using-and-interpreting-statistics_a-practical-text-for-the-behavioral-social-and-health-sciences_ebook_3e.php

MUST have at least 5 citations with the page numbers and 5 references in APA format.(The List of References should not be older than 2016 and should not be included in the word count.)

Be sure to support your postings and responses with specific references to the Learning Resources.

REMEMBER IN APA FORMAT JOURNAL TITLES AND VOLUME NUMBERS ARE ITALICIZED.

PUB-550 Application of the Pearson Correlation Coefficient and the Chi-Square Test Assignment Rubric

Course Code	Class Code	Assignment Title	Total Points
PUB-550	PUB-550-O500	Application of the Pearson Correlation Coefficient and the Chi-Square Test	70.0

Criteria	Percentage	Unsatisfactory (0.00%)	Less than Satisfactory (74.00%)	Satisfactory (79.00%)	Good (87.00%)	Excellent (100.00%)	Comments	Points Earned
Content	100.0%
Hand Calculations, Excel Output, or SPSS Output	25.0%	Hand calculations, Excel output, or SPSS output illustrating calculations for problems 13.132 and 15.88 are not included.	Hand calculations, Excel output, or SPSS output illustrating calculations for problems 13.132 and 15.88 are incomplete or incorrect.	Hand calculations, Excel output, or SPSS output illustrating calculations for problems 13.132 and 15.88 are partially complete and correct.	Hand calculations, Excel output, or SPSS output illustrating calculations for problems 13.132 and 15.88 are mostly complete and correct.	Hand calculations, Excel output, or SPSS output illustrating calculations for problems 13.132 and 15.88 are complete and correct.

Six Steps of Hypothesis Testing for Problem 13.132	35.0%	Responses for the six steps of hypothesis testing related to Problem 13.132 are not included.	Responses for the six steps of hypothesis testing related to Problem 13.132 incomplete or incorrect.	Responses for the six steps of hypothesis testing related to Problem 13.132 are partially complete and correct.	Responses for the six steps of hypothesis testing related to Problem 13.132 are mostly complete and correct.	Responses for the six steps of hypothesis testing related to Problem 13.132 are complete and correct.

Six Steps of Hypothesis Testing for Problem 15.88	35.0%	Responses for the six steps of hypothesis testing related to Problem 15.88 are not included.	Responses for the six steps of hypothesis testing related to Problem 15.88 incomplete or incorrect.	Responses for the six steps of hypothesis testing related to Problem 15.88are partially complete and correct.	Responses for the six steps of hypothesis testing related to Problem 15.88 are mostly complete and correct.	Responses for the six steps of hypothesis testing related to Problem 15.88 are complete and correct.

Mechanics of Writing (includes spelling, punctuation, grammar, language use)	5.0%	Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.	Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.	Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.	Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.	Writer is clearly in command of standard, written, academic English.

Total Weightage	100%

Topic 7: Data Management and Descriptive Statistics Discussions

Topic 7 DQ 1

Correlation is a common statistic to measure a general linear relationship between two variables. Explain why correlation does not equal causation. Describe the data characteristics necessary to calculate a Pearson correlation coefficient. Design a study that would apply the Pearson correlation coefficient as an appropriate statistic.

Topic 7 DQ 1 Example Answer

Correlation between two variables proves only that there is an association it doesn’t guarantee that one causes the other (Corty, 2016). If two variables vary together systematically a cause and effect relationship may exist but there does not have to be, it is possible there is a third variable (Corty, 2016). When an association is demonstrated further research is needed to assess the strength of the relationship this is where the Pearson test comes in. The Pearson test calculates a correlation coefficient that summarizes the strength of the linear relationship between two variables, a strong relationship would suggest causation (Corty, 2016).

Data necessary to calculate a Pearson correlation coefficient must be interval and/or ratio, for an ordinal variable the Spearman rank order test can be used and for two nominal variables the chi-square test can be used (Corty, 2016).

A study that would be appropriate for the Pearson correlation coefficient would be a study of the need for prescription glasses and its relationship to age.

References

Corty, E. (2016). Using and interpreting statistics : a practical text for the behavioral, social, and health sciences. New York: Worth Publishers.

Topic 7 DQ 2

Describe the conditions in which a nonparametric test would be a better selection than a parametric test. Illustrate your ideas with a specific example of when you would use each type of test using similar variables for each example.

Topic 7 DQ 2 Example Answer

Parametric tests should only be used when assumptions about the parameters are met (Corty, 2016). Nonparametric tests do not have to meet these same assumptions. There are two circumstances in which a nonparametric test should be used: PUB-550: Application and Interpretation of Public Health Data

The outcome variable is ordinal or nominal (Corty, 2016).
During an experiment, if a nonrobust assumption is violated, the researcher can revert back to a nonparametric test from a parametric test (Corty, 2016).

Nonparametric tests are less restricted by assumptions and relatively simple to conduct, making them desirable. Although, they are often not as influential on the null hypothesis as parametric test (Corty, 2016). The reason nonparametric tests have less power is that they only contain nominal or ordinal data rather than interval/ratio data. Nominal and ordinal numbers contain less information, thus giving nonparametric tests less power (Corty, 2016). Generally, researchers prefer parametric tests, but when the assumptions are not met, nonparametric tests are used.

When the outcome is an ordinal variable or a rank, it is appropriate to use a nonparametric test. For example, a clinical trial is performed where study participants are asked to rate illness symptoms on severity for six weeks for a specific, assigned treatment. Symptom severity is measured on a 5-point ordinal scale with the following response options:

Symptoms got much worse
Symptoms are slightly worse
No change
Slightly improved
Much improved

(Sullivan, 2016).

Outcomes that are ordinal, ranked, subject to outliers or measured imprecisely are difficult to analyze with parametric tests without making major assumptions (Sullivan, 2016). An appropriate and most effective test for the example above is the nonparametric test.

Parametric tests can also be used for ordinal variables as long as the ordinal variables are continuous. For example, an experiment analyzing the weight and height of firefighters could use a parametric test because the ordinal variables are continuous.

References

Corty, E.W. (2016). Using and Interpreting Statistics: A Practical Text for the Behavioral, Social, and Health Sciences. (3^rd Ed.) New York, NY: Worth Publishers

Sullivan, L. (2016). Nonparametric Tests. Retrieved from http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Nonparametric/BS704_Nonparametric_print.html PUB-550: Application and Interpretation of Public Health Data

PUB-550 Benchmark – Analyzing and Reporting Data Assignment

Topic 8: Analyzing and Reporting Results

Objectives:

Apply hypothesis testing steps to a data set.
Communicate scientific information for public health practice.
Select quantitative and qualitative data collection methods appropriate for a given public health context.
Analyze quantitative and qualitative data using biostatistics, informatics, computer-based programming, and software, as appropriate.
Interpret results of data analysis for public health research, policy, or practice.

Analyzing and Reporting Data – Overview

The purpose of this assignment is to give you experience conducting a basic secondary data analysis using real-world surveillance data. Secondary data analysis is faster and cheaper to conduct compared to primary data collection. However, there are also significant limitations. The data were likely collected for a different purpose, and may not include the specific variables required to answer your question. The sampling strategy might not be random and may not be representative of your target population. These are examples of such limitations you should be aware of as you work with existing data.

A key question is whether the data should determine the research question, or if the research question should determine the type of data you use. In practice, you would want your research question or hypothesis to determine the dataset you select. In this assignment, you are limited to three datasets and may need to adjust your initial research question to accommodate one of the three datasets. Avoid “mining” for significant results and stick to your initial research question as much as possible. For this project, you will select one of the three example datasets to complete a basic analysis and communicate your findings through a scientific poster presentation.

These steps will help you get started:

Review the websites for each of the three datasets listed below. Be sure to understand the purpose of the survey, the sample used in the survey, and the main focus areas of each survey. Review the documentation provided on the websites to get to know the story behind the data and understand the population before reviewing the data.
Select the dataset that is most appropriate for your interest area.
Open the data in SPSS and get to know the data by reviewing the variables in “Variable View” mode. This view will allow you to read the variable labels and response labels for each variable.
Based on your research interest and question, select variables that will help increase your understanding about that topic.
Arrange the data as needed to organize and clean data, allowing you to focus on your specific question. Remember to save your analytic data file as a new file in case you need to go back to the original file. It is good practice to continually save new versions of the data file as you work with and manipulate the data.
Follow the hypothesis testing steps to carry out your secondary data analysis.

For additional information on conducting a secondary data analysis, read the Topic Material, “Conducting High-Value Secondary Dataset Analysis: An Introductory Guide and Resources.”

Dataset Documents

Demographic and Health Survey

The Demographic and Health Survey is a global monitoring survey administered by USAID. The sample dataset is the model data set put together by USAID to explore DHS data. The sample data is not from a specific country or year, but it gives you an idea of what can be obtained from various countries through these datasets. The datasets are free and publically available once you register with USAID to access the DHS data. For the purpose of this assignment, treat this dataset as coming from a country of your choice. Access to the Model Questionnaire, Recode Manual, and Data Video Tutorials, including a video on the sampling strategy,is found at http://dhsprogram.com/data/model-datasets.cfm.

Note: You do not need to worry about weighting strategies for this assignment.

Use the http://dhsprogram.com/data/Using-DataSets-for-Analysis.cfm link to review the “Step-by-Step Introduction to Analyzing DHS Data”for tips on how to access your own dataset for future use and to see what resources are available to help you navigate the model dataset for this assignment:

Youth Risk Behavior Surveillance System (YRBSS)

The Youth Risk Behavior Survey is a national survey monitoring health behaviors among youth and young adults. It is administered by the Centers for Disease Control and Prevention. The example dataset for this assignment comes from the National Survey (not combined) dataset for 2015. General information about the survey is found at https://www.cdc.gov/healthyYouth/data/yrbs/index.htm.

Documentation and questionnaires can be found by accessing the “YRBSS Data and Documentation: website athttps://www.cdc.gov/healthyyouth/data/yrbs/data.htm.

Please read the 2015 YRBS Data User’s Guide, listed in the “National YRBS Datasets and Documentation” page athttps://www.cdc.gov/healthyyouth/data/yrbs/pdf/2015/2015_yrbs-data-users_guide_smy_combined.pdf.

The dataset includes calculated variables not found in the questionnaire that you might find helpful in determining your analysis for this assignment.The crosswalk to match the questions with the dataset can be found by viewing the “YRBS Questionnaire Content – 1991-2017” found athttps://www.cdc.gov/healthyyouth/data/yrbs/pdf/2017/yrbs_questionnaire_content_1991-2017.pdf.

National Health Interview Survey (NHIS)

The NHIS began in 1957, and has been used to monitor the health of the United States ever since. It is a household-level survey administered by the U.S. Census Bureau. Key topics in the survey include doctor’s visits, medical conditions, health insurance, and health behaviors. General information about the survey, including the sample design and data collection procedures, can be found at https://www.cdc.gov/nchs/nhis/about_nhis.htm.

A Survey Descriptionof the 2015National Health Interview Survey can be found at ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2015/srvydesc.pdf.

The sample dataset is from the 2015 adult survey athttps://www.cdc.gov/nchs/nhis/nhis_2015_data_release.htm.

Some of the variables have been deleted to decrease the size of the file, but none of the observations have been dropped. Please review the “2015 National Health Interview Survey (NHIS) Public Use Data Release” document at ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2015/readme.pdf.

Review the “2015 NHIS Public Use Variable Summary” at ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2015/samadult_summary.pdf.

After you identify a few variables you are interested in, review the complete description of the variable in the variable layout document atftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2015/samadult_layout.pdf.

Checking the variable frequencies will help you determine the range of answers for each variable of interest, including the number of missing observations. If the number missing is high, consider using another variable. Variable frequencies can be found at ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2015/samadult_freq.pdf.

PUB-550 Benchmark Analyzing and Reporting Data Assignment Description

The purpose of this assignment is analyze a dataset and report findings to relevant stakeholders.

Read the Topic Material “Analyzing and Reporting Data – Overview” prior to beginning the assignment.

For this assignment, you will use IBM SPSS Statistics and one of the three datasets to conduct a basic statistical analysis and report the results by creating a scientific poster. MPH students can use this assignment as an introduction to the option of a larger secondary data analysis for their capstone project.

Select a public health topic that is of interest to you and complete the following using the “GCU MPH Poster Template.”

Review the three datasets provided in the Topic Materials and brainstorm at least one potential research question that could be answered by one of the datasets. The question should be based on a topic that is of interest to you and is supported by existing literature.
Select the dataset and organize the data to complete the analysis.
Provide descriptive statistics that summarize the sample.
Select an appropriate statistical test and conduct the analysis.
Interpret the results of your analysis.
Prepare your poster presentation putting the results in the context of the larger story surrounding the purpose for the analysis. Consider the data source, assumptions, hypotheses, decision rule, and interpretation.

In addition to the poster, write a 1,000-word summary of the analysis that addresses the questions below. The summary should include additional discussion notes you would include for each section if you were presenting your poster at a professional conference. Typically, a poster presentation involves presenting your poster to small groups and providing an oral explanation as the audience reviews your poster.

Summary of the data source.
Variables used in the analysis.
The six steps of hypothesis testing.

Prepare and submit the PowerPoint poster and summary as two separate documents.

Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

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.

AttachmentsPUB-550-RS-National-Health-Interview-Survey-Dataset.sav
PUB-550-RS-Analyzing-and-Reporting-Data-Overview.docx
PUT-550-RS-Demographhic-and-Health-Survey-Dataset.sav
PUB-550-RS-Youth-Risk-Behavior-Surveillance-System-Dataset.sav
PUB-550-RS-GCU-MPH-Poster-Template.pptx

PUB-550 Benchmark – Analyzing and Reporting Data Assignment GCU MPH Poster Template

Title of Project

Presenter Name (contact information) Grand Canyon University, Phoenix Arizona

Collage of Nursing and Health Care Professions

Introduction and Problem

To start using this template you first need to delete this content and any other unwanted contents of this page. Keep the poster title and the purple section headers.

The purple headers are used to identify and separate the main topics of your presentation. The most commonly used headers in poster presentations are provided, but you can change these headers to fit your dissertation

Move the header copies approximately to where you think they need to be on the poster, so you can get a better sense of the overall poster layout. It will help you organize your content.

You can now start adding your text. To add text use the text tool to draw a text box starting from the left edge of a column to the right edge and start typing in your text. You can also paste the text you may have already copied from another source

Repeat the process throughout the poster as needed.

In the Introduction, describe the importance of the study in public health, the problem it addresses, and the research question with corresponding hypotheses for the study.

Methods

In the Methods sections, discuss the data source and sample, the variables used in the analysis, the test statistic and corresponding equations important to understand the analysis.

Results

It is highly recommended to use the largest images you have access to for your poster. Avoid images downloaded from the web and avoid copying and pasting images instead of using the “Insert” command. To insert an image to your poster go to INSERT>PICTURE>FROM FILE.

To import charts and graphs from Excel, Word or other applications, go to EDIT>COPY, copy your chart and come back to PowerPoint. Go to EDIT>PASTE and paste the chart on the poster. You can scale your charts and tables proportionally by holding down the Shift key and dragging in or out one of the corners.

In the Results section, show the factual results of the analysis, including any charts or graphs important to communicate the results. Include descriptive statistics if they are helpful to understand nuances of the sample relative to the population.

Discussion

The Discussion section explain the results and states the implications for public health practice or policy. Discuss any potential limitations and strengths.

Conclusions and Future Research

In the conclusion, summarize the key findings and potential areas for future research.

References

PUB-550 Benchmark – Analyzing and Reporting Data Assignment Rubric

Course Code	Class Code	Assignment Title	Total Points
PUB-550	PUB-550-O500	Benchmark – Analyzing and Reporting Data	200.0

Criteria	Percentage	Unsatisfactory (0.00%)	Less than Satisfactory (74.00%)	Satisfactory (79.00%)	Good (87.00%)	Excellent (100.00%)	Comments	Points Earned
Content	100.0%
Poster Template	15.0%	Completed poster template is not included.	Completed poster template is incomplete or incorrect.	Completed poster template is partially complete and correct.	Completed poster template is mostly complete and correct.	Completed poster template is complete and correct.

Descriptive Statistics (C. 2.3)	15.0%	High quality descriptive statistics that summarize the sample are not included.	High quality descriptive statistics that summarize the sample are incomplete or incorrect.	High quality descriptive statistics that summarize the sample are partially complete and correct.	High quality descriptive statistics that summarize the sample are mostly complete and correct.	High quality descriptive statistics that summarize the sample are complete and correct.

Statistical Test, Analysis, and Interpretation (C. 2.3 and .2.4)	15.0%	Discussion of the selected statistical test, analysis, and interpretation of results is not included.	Discussion of the selected statistical test, analysis, and interpretation of results is incomplete or incorrect.	Discussion of the selected statistical test, analysis, and interpretation of results is partially complete and correct.	Discussion of the selected statistical test, analysis, and interpretation of results is mostly complete and correct.	Discussion of the selected statistical test, analysis, and interpretation of results is complete and correct.

Data Source Summary (C. 2.2)	15.0%	Summary of the data source is not included.	Summary of the data source is incomplete or incorrect.	Summary of the data source is included but lacks explanation and relevant supporting details.	Summary of the data source is complete and includes explanation and relevant supporting details.	Summary of the data source is extremely thorough and includes substantial explanation and relevant supporting details.

Variables	15.0%	Discussion of variables used in the analysis is not included.	Discussion of variables used in the analysis is incomplete or incorrect.	Discussion of variables used in the analysis is included but lacks explanation and relevant supporting details.	Discussion of variables used in the analysis is complete and includes explanation and relevant supporting details.	Discussion of variables used in the analysis is extremely thorough and includes substantial explanation and relevant supporting details.

Six Steps of Hypothesis Testing (C. 2.3 and 2.4)	15.0%	Explanation of the six steps of hypothesis testing is not included.	Explanation of the six steps of hypothesis testing is incomplete or incorrect.	Explanation of the six steps of hypothesis testing is included but lacks supporting details.	Explanation of the six steps of hypothesis testing is complete and includes supporting details.	Explanation of the six steps of hypothesis testing is extremely thorough and includes substantial supporting details.

Mechanics of Writing (includes spelling, punctuation, grammar, language use)	5.0%	Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.	Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.	Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.	Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.	Writer is clearly in command of standard, written, academic English.

Documentation of Sources (citations, footnotes, references, bibliography, etc., as appropriate to assignment and style)	5.0%	Sources are not documented.	Documentation of sources is inconsistent or incorrect, as appropriate to assignment and style, with numerous formatting errors.	Sources are documented, as appropriate to assignment and style, although some formatting errors may be present.	Sources are documented, as appropriate to assignment and style, and format is mostly correct.	Sources are completely and correctly documented, as appropriate to assignment and style, and format is free of error.

Total Weightage	100%

Topic 8: Data Management and Descriptive Statistics Discussions

Topic 8 DQ 1

Reporting public health information requires a clear understanding of the various statistical methods used to draw conclusions. These methods are then communicated within the larger story surrounding the public health issue. Identify a public health report or article and discuss what you would do differently to improve understanding and application if you were the author. Post the permalink to your article or report in the Main Forum.

Topic 8 DQ 1 Example Answer

The world today does not have a shortage of public health issues. Researchers are constantly gathering more data to develop prevention and protection programs. In order to properly accomplish this, it is necessary to have a clear understanding of statistical methods to draw accurate conclusions. It is possible to find statistics on nearly all public health issues, which is why is it critical for the scientists and professionals to have a grasp on all statistical methods available.

One public health issue that is relevant today is obesity. The general population is aware that poor nutrition, lack of physical activity and obesity cause a number of health-related issues, however obesity is getting worse all over the world. An article that addresses the issue, published by the World Health Organization, is ‘Obesity and Overweight’. The link to the article is https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.

Key facts from the article:

Obesity has tripled worldwide since 1975
In 2016, 1.9 million adults over the age of 18 were considered overweight; 650 million were obese
40 million children under the age of 5 were overweight or obese. PUB-550: Application and Interpretation of Public Health Data.
In 2016, over 340 million children and adolescents aged 5-19 were obese or overweight

(WHO, 2020).

The article references multiple statistics regarding population obesity. The article also defines obesity and overweight in order to understand what determines who falls into the obese category and who falls into the overweight category. WHO also shared recent global estimates:

In 2016, more than 1.9 billion adults aged 18 years and older were overweight. Of these over 650 million adults were obese.
In 2016, 39% of adults aged 18 years and over (39% of men and 40% of women) were overweight.
Overall, about 13% of the world’s adult population (11% of men and 15% of women) were obese in 2016.
The worldwide prevalence of obesity nearly tripled between 1975 and 2016

(WHO, 2020).

While the information is interesting and useful, it would be helpful for the reader if WHO discussed the application used to develop the statistical data. WHO also shared the cause and prevention tactics for obesity. It would be helpful if WHO developed predictions for the next five years if people followed obesity prevention guidelines verses if these guidelines were not followed; all while providing the method to creating the statistical data. This would help the reader to better understand the importance of the public health issue. The sample size and surveillance methods should also be shared regarding obesity data retrieval.

Reference

World Health Organization (WHO). (2020). Obesity and overweight. Retrieved from https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight PUB-550: Application and Interpretation of Public Health Data

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PUB-550 Application and Interpretation of Public Health Data Course Assignments & Discussions Study Guide