PUB-550: Application and Interpretation of Public Health Data

PUB-550: Application and Interpretation of Public Health Data

PUB-550: Application and Interpretation of Public Health Data
Topic 1: Data Management and Descriptive Statistics
Objectives:

Evaluate methods of data organization.
Compare characteristics of correlational, experimental, and quasi-experimental (observational) statistics variables.
Identify the four levels of measurement.
Differentiate between a population and a sample, and a parameter and a statistic (descriptive and inferential).
Explain the role of quantitative and qualitative methods and sciences in describing and assessing a population’s health. PUB-550: Application and Interpretation of Public Health Data
Evaluate public health data sources.
Apply methods to calculate and communicate descriptive statistics.

ORDER A CUSTOM-WRITTEN, PLAGIARISM-FREE PAPER HERE

Data Management

The purpose of this assignment is to practice organizing data through ordering and grouping variables.
Data often appear disordered and it is difficult to see any connections or relationships. Ordering the data by certain variables or grouping variables into specific categories, such as age or sex categories, can help bring clarity to the data. Knowing how to organize data is an important skill to initiate the analytical process.
For this assignment, students will use Excel and SPSS Statistics to order variables. Using the “Example Dataset,” complete the steps below using both Excel and SPSS Statistics. View the Excel and SPSS tutorials for assistance in completing this assignment. Submit one Word document and include a screen shot of the data after completing the first two steps of Part 1 in Excel and SPSS to compare your results. Use a second Word document to complete Part 2 of the assignment. PUB-550: Application and Interpretation of Public Health Data
Part 1: Ordering and Grouping Data Using Excel and SPSS
For Part 1, accomplish the following:

Order (sort) observations according to age.
Group observations by sex and investigate the age and income for males and females.
Create a new variable titled “Exercise Group” based on the variable “Minutes Exercise.” Use the following categories to create your groups: 1 = 0-30 minutes; 2 = 31-60 minutes; 3 = 61-90 minutes; 4 = 91-120 minutes; and 5 = 120+ minutes.

Part 2: Data Interpretation
Study the results of the dataset grouping and ordering. Discuss the following in a 500-750 word summary:

Describe the measurement levels for each of the variables in the dataset.
Discuss what you learned from ordering the data by age and why this information is important.
Describe the process you used to group the data in Excel and SPSS.
Describe what you learned by grouping the variables by category of exercise.
Are these data from a correlational study, experimental study, or quasi-experimental (observational) study? Discuss your rationale and identify a study question appropriate for this dataset. PUB-550: Application and Interpretation of Public Health Data

General Requirements
Submit the Word document to the instructor.
APA style is not required, but solid academic writing is expected.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
You are required to submit this assignment to LopesWrite. Refer to the LopesWrite Technical Support articles for assistance.

Attachments
PUB-550-RS-Example Dataset.xlsx

Rubric
Due Date: 05-Mar-2020 at

A local community organization was interested in learning about general health behaviors in the area and the relationships between health behaviors and environmental and social determinants. They decided to conduct a brief survey based on a convenient sample of people visiting the local shopping mall. They offered a $5 incentive for completing the survey. The Topic 1 Example dataset includes 30 observations from this survey. Use this data to complete the relevant assignments in this course.

Education Level

1
Less than High School

2
Graduated High School

3
Graduated College

Annual Income = US Dollars

 

ID
Sex
Smoker
Education_Level***
Minutes_Exercise
Age
Employed
Annual_Income*
Neighborhood

101
Female
No
2
90
45
Yes
51000
B

102
Male
No
2
50
58
No
23000
C

103
Female
Yes
3
65
31
Yes
35000
B

104
Male
No
1
20
54
No
10000
C

105
Female
Yes
1
50
30
Yes
28000
B

106
Female
Yes
2
25
18
No
5000
C

107
Female
No
3
110
39
Yes
46000
A

108
Male
Yes
1
50
37
Yes
36000
B

109
Female
Yes
2
40
44
Yes
51000
C

110
Male
No
2
80
24
No
12000
A

111
Female
No
3
120
42
Yes
78000
A

112
Male
No
1
80
50
Yes
34000
D

113
Female
Yes
1
60
20
No
15000
B

114
Male
No
3
150
35
Yes
28000
B

115
Male
No
2
75
61
Yes
28000
A

116
Male
No
1
80
59
No
24000
B

117
Female
No
2
110
36
Yes
55000
D

118
Male
Yes
3
80
35
Yes
62000
B

119
Male
Yes
2
100
29
No
32000
D

120
Female
No
1
0
32
No
7000
C

121
Female
Yes
2
50
26
No
17000
B

122
Female
No
3
200
42
Yes
64000
D

123
Male
No
2
60
52
No
5000
A

124
Male
No
1
65
49
No
14000
D

125
Female
No
1
40
21
No
20000
C

126
Male
Yes
3
65
48
Yes
72000
A

127
Female
Yes
3
70
40
Yes
85000
A

128
Female
No
1
45
53
No
15000
B

129
Male
No
3
75
46
Yes
64000
C

130
Male
Yes
3
50
42
Yes
27000
B

 

Topic 1 DQ 1

Mixed methods research is becoming an important approach in generating public health evidence. Based on the resources supplied, discuss the benefits of a mixed methods approach. Include an explanation of the differences between qualitative and quantitative research and the purpose of each.

Topic 1 DQ 1
Mixed methods research has become increasing popular, however the definition of mixed methods research has yet to be agreed upon (Ozawa & Pongpirul, 2014). Essentially, mixed methods research studies incorporate quantitative and qualitative data to utilize the strengths of both types of research methods (Ozawa & Pongpirul, 2014). In health systems, mixed methods research is critical because it allows researchers to see issues from various perspectives, contextualize information, have a better understanding of the issue, form results, quantify difficult measures, create illustrations for trends, and examine processes (Ozawa & Pongpirul, 2014).To make sense of the assembly of mixed method research designs, there are four categories; the triangulation design, the embedded design, the explanatory design, and the exploratory design (Almalki, 2016). The triangulation design is practical because this type of research gathers data from different sources and utilizes different methods, which all work together as well-organized design (Almalki, 2016). With the embedded design, less resources are needed, and it produces less data, making it easier for researchers to grasp (Almalki, 2016). The explanatory design is easy to implement, and it enables the focus of the research to be maintained (Almalki, 2016). With the exploratory design, separate stages are easy to apply, also qualitative information is acceptable to quantitative researchers (Almalki, 2016).Quantitative research regards the world as being outside of themselves. The purpose is to gain an understanding about the social world (Almalki, 2016). The qualitative approach gains a perspective of issues by investigating them in their own specific setting. The purpose is to observe occurrences and bring meaning to them (Almalki, 2016). The differences between quantitative and qualitative research is as follows:

Quantitative Approach
Qualitative Approach

Deductive
Inductive, with underlying assumptions reality is a social construct

Subdivides reality into smaller, manageable pieces
Places emphasis on exploring and understanding

Observations are made and hypotheses can be tested among variables
Variables are difficult to measure

Primacy of subject matter

Conclusions are made with regard to the hypothesis, following a series of observations and analysis of data
Data collected will consist of an insider’s viewpoint

(Almalki, 2016).
References
Almalki, S. (2016). Integrating Quantitative and Qualitative Data in Mixed Methods Research – Challenges and Benefits. Journal of Education and Learning. doi:10.5539/jel.v5n3p288. Retrieved from https://files.eric.ed.gov/fulltext/EJ1110464.pdf
Ozawa, S. & Pongpirul, K. (2014). 10 best resources on…mixed methods research in health systems. Health Policy and Planning. Retrieved from https://academic.oup.com/heapol/article/29/3/323/581455

ORDER A CUSTOM-WRITTEN, PLAGIARISM-FREE PAPER HERE
Re: Topic 1 DQ 1
The delivery of healthcare is becoming more complex as evidence by the rising number of individuals with comorbidities and the shift towards the quality of care versus quantity. Addressing challenges that are generated by this complex system requires research that not only produces statistical data, but also understands a population’s natural setting and provides insight how he research can be applied to that setting. Mixed methods research is becoming an important approach in generating public health evidence because it combines both qualitative and quantitative research. Qualitative research answers clinical question regarding meaning and quality improvement and provides descriptive data while quantitative research answers clinical question regarding therapy, etiology, diagnosis, prevention, and prognosis and produces numerical data (Winona State University, 2014). Favorable characteristics of mixed method research include consistency between the research question, purpose and methodological choices; verifiable and transparent techniques that demonstrate trustworthiness; potential for replicability; opportunity for self-correction; and ability to explain the phenomena under investigation (Newman and Hitchcock, 2012). Furthermore, benefits to mixed methods include answering questions that qualitative or quantitative research cannot answer alone; provides better understanding of connections or contradictions between qualitative and quantitative data; it gives participants an opportunity to have a voice and share the experience across the research process [which is important within public health]; it facilitates different avenues of exploration that enhance the quality of evidence and enables questions to be answered more deeply (Shorten & Smith, 2017). A mixed method approach uses the combine strengths of qualitative and quantitative data. Its unique design is appropirate to addressing complex public health issues.Hitchcock, J. H., & Newman, I. (2012). Applying an Interactive Quantitative-Qualitative Framework. Human Resource Development Review, 12(1), 36–52. https://doi.org/10.1177/1534484312462127Shorten, A., & Smith, J. (2017). Mixed methods research: expanding the evidence base. Evidence Based Nursing, 20(3), 74–75. https://doi.org/10.1136/eb-2017-102699Winona State University. (2014). Research Hub: Evidence Based Practice Toolkit: Levels of Evidence. Retrieved from Winona.edu website: https://libguides.winona.edu/c.php?g=11614&p=61584
 
1 DQ 2
Topic One, Discussion Question 2:Statistics are ways to summarize data in a way that will answer a specific question (Corty, 2016). There are several key words that help with defining statistics, such as population, sample, parameter and statistic.During investigation studies researchers look for subjects to study. These subjects from large groups called a population (Corty, 2016). If the research only wanted to look at a small group of this population, they would call that a sample (Corty, 2016).For example – If I were to do a research study on obesity, I could use the state of Kentucky as my population. However, if I wanted to only look at Shelbyville, Kentucky that would be a sample of Kentucky.Data from either the sample or the population which can be reduced to a simple number like an average to summarize the group (Corty, 2016). If it is characterizing the sample, it is called a statistic; if it is characterizing the population it is called a parameter. Sample statistics use Latin letters as their symbol and population parameters use Greek letters (Corty, 2016).Then there is descriptive and inferential statistics. Descriptive is the summary statement about the set of cases (Corty, 2016). It reduces a set of data to a meaningful value to describe the characteristics of the group being observed – for example: 63% of the class were females. Inferential statistics uses a sample of cases to draw a conclusion about the larger population and reduces the data down to a single value that inferences about the population (generalization from the sample to a population – for example: Students who are female at GCU have a 15% higher GPA on average than males (Corty, 2016).Public health researchers often limit or rather stop their analyses to descriptive statistics—reporting frequencies, means and standard deviation (Guetterman, 2019). This allows for missed opportunities for more advanced analyses. “For example, knowing that patients have favorable attitudes about a treatment may be important and can be addressed with descriptive statistics. On the other hand, finding that attitudes are different (or not) between men and women and that difference is statistically significant may give even more actionable information to healthcare professionals” (Guetterman, 2019). This missing piece about differences can be addressed through inferential statistical tests (Guetterman, 2019). Therefore, both are extremely important to public health research.
References:
BUY A PLAGIARISM-FREE PAPER NOW 
Corty, E. (2016). Using and interpreting statistics. A practical text for the behavioral, social, and health sciences 3rd Edition. Retrieved from https://viewer.gcu.edu/GGdEcj
Guetterman, T., (2019). Basics of statistics for primary care research. Family Medicine Community Health. 7(2). Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6583801/

 

Practicing Application of Descriptive Statistics in Excel and SPSS

The purpose of this assignment is to compare basic functions in Excel and SPSS to calculate descriptive statistics and use this information to describe the sample.
For this assignment, students will utilize Excel with the Data Analysis ToolPak and SPSS Statistics and the “Example Dataset” to complete the assignment. Refer to the Topic Materials for assistance with enabling the Data Analysis ToolPak on a Mac or PC.
Part 1:
Complete the following steps in both Excel and IBM SPSS Statistics.

Calculate mean, median, and mode for the variables “Annual Income” and “Age.” Show the appropriate summary tables for these measures from both Excel and SPSS. Include the other descriptive statistics that are a part of the summary output in Excel and SPSS.
Create histograms to show the distribution for “Annual Income” and “Age.” Copy and paste the histograms from Excel and export the histogram from SPSS into the Word document for this assignment.
Create frequency tables that include counts and percentages for smoking status, employment status, exercise level, and education level. Show the tables in the Word document for this assignment.

Part 2:
Based upon the Part 1 activities, write a 250-500 word interpretation that addresses the following.

Discuss the sampling strategy used in this study and if it resulted in a representative sample.
Discuss what you are able to ascertain about the sample from the descriptive statistics.
Explain what other variables the research team could have included to gain a better understanding of the population.

General Requirements
Submit one Word document for the Part 1 assignment content and a second Word document for Part 2 of the assignment.
APA style is not required, but solid academic writing is expected.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
You are not required to submit this assignment to LopesWrite.

Attachments
PUB-550-RS-Example Dataset.xlsx

 

Calculating Confidence Intervals

The purpose of this assignment is to practice calculating confidence intervals.
For this assignment, students will utilize Excel and SPSS Statistics and the “Example Dataset.”
Using the “Example Dataset,” complete the following:

Based on a normal distribution curve, calculate the probability of an individual being 60 years or older in this population. Show the Excel and SPSS formulas or your hand calculations. Include screenshots as needed to illustrate this.
Using the sample standard deviation of age as an estimate of the population standard deviation, calculate by hand the standard error of the mean. Show your calculations and the answer.
Calculate by hand a 95% confidence interval for “Age” based on the sample mean. Use SPSS to verify your answer. Include your calculations and screenshots of the SPSS output.
Interpret the confidence interval for age and explain the three pieces of information needed to calculate a confidence interval.

Submit one Word document that includes all of the assignment deliverables.
APA style is not required, but solid academic writing is expected.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
You are not required to submit this assignment to LopesWrite.

Attachments
PUB-550-RS-Example Dataset.xlsx

 

Topic 2 DQ 1

P-values and confidence intervals are both used in hypothesis testing. Explain three reasons why it may be preferable to report a confidence interval over a P-value. Provide a specific example to justify your reasons.

Topic 2 DQ 1
De Prel et al. (2009) study found the following: P-values in scientific studies are used to determine whether a null hypothesis formulated before the performance of the study is to be accepted or rejected. In exploratory studies, p-values enable the recognition of any statistically noteworthy findings. Confidence intervals provide information about a range in which the true value lies with a certain degree of probability, as well as about the direction and strength of the demonstrated effect. This enables conclusions to be drawn about the statistical plausibility and clinical relevance of the study findings. It is often useful for both statistical measures to be reported in scientific articles, because they provide complementary types of information (p.335).
 
According to de Prel et al. (2009) “For example, there might be no difference between two antihypertensives with respect to their ability to reduce blood pressure. The alternative hypothesis (H1) then states that there is a difference between the two treatments. This can either be formulated as a two-tailed hypothesis (any difference) or as a one-tailed hypothesis (positive or negative effect). In this case, the expression “one-tailed” means that the direction of the expected effect is laid down when the alternative hypothesis is formulated (p.335).
 
Reference
du Prel, J. B., Hommel, G., Röhrig, B., & Blettner, M. (2009). Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications. Deutsches Arzteblatt international, 106(19), 335–339. doi:10.3238/arztebl.2009.0335
 
ORDER A PLAGIARISM-FREE PAPER NOW 
 
Topic 2 DQ 2
The Central Limit Theorem is the fundamental theorem of statistics. In a nutshell, it says that for independent and identically distributed data whose variance is finite, the sampling distribution of any mean becomes more nearly normal (i.e., Gaussian) as the sample size grows (Chang, Wu, Ho and Chen, 2008). The sample mean ¯xn will then approach the population mean µ, in distribution. More formally, where N (0, 1) is the normal distribution and the symbol “d” in the equality means in distribution. σn is the standard deviation of a sampling distribution, σ is the standard deviation of the entire population the study (and which is often not known), and n the sample size. So, sample means vary less than individual measurements. (The square of the standard deviation is the variance.). The sampling distribution is a notional (imaginary) distribution from a very large number of samples, each one of size n, which approaches a normal distribution in the limit of large n. In practice, the Central Limit Theorem holds for n as low as 30, unless there are exceptional circumstances—e.g., when the population distribution is highly skewed—in which case higher values are needed. So, σn measures how widely the sample means of size n vary around the population mean µ (which is approached in the limit of large n). As expected, the results suggest that the distribution of the sample mean better approximates the normal distribution as the sample size increases. The results indicate that the true distribution of the sample mean when the sample is taken from a highly skewed distribution better approximates the normal distribution as the thickness of the tail of the population distribution increases.
Chang, H. J., Wu, C. H., Ho, J. F., & Chen, P. (2008). On sample size in using central limit theorem for gamma distribution
 
Topic 3: Hypothesis Testing
Objectives:

Evaluate the importance of hypothesis testing in statistics and public health research.

 

Hypothesis Testing

The purpose of this assignment is to evaluate the steps of hypothesis testing.
Hypothesis testing is a central component in understanding and interpreting public health research. Research questions are applied through a testable hypothesis established before the data is collected. It provides the rationale for the study and guides the researcher in what tests to use and how to interpret the results.
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

 

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
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
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

 
ORDER A PLAGIARISM-FREE PAPER NOW 
 
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
 
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.

 

Application of the t-Test

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.”
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 250-500 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. PUB-550: Application and Interpretation of Public Health Data
APA style is not required, but solid academic writing is expected.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
You are not required to submit this assignment to LopesWrite.

Attachments
PUB-550-RS-Example Dataset.xlsx

 

Topic 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
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

Is this the question you were looking for? If so, place your order here to get started!