Solve problems relating to dependent and independent variables, grouped and ungr

Solve problems relating to dependent and independent variables, grouped and ungrouped data, locating data on a chart, and creating tables and graphs in SPSS.
Introduction
Note: The assessments in this course build upon each other, so you are strongly encouraged to complete them in sequence.
In this assessment, you will complete problems to apply correct order of operations in solving equations, identify appropriate scales of measurement, differentiate continuous versus discrete variables, create tables of frequency distributions, and create graphic visualizations of data.
Preparation
Note: The assessments in this course build upon each other, so you are strongly encouraged to complete them in sequence.
Download and save the Assessment 1: Basics of Research and Statistics, Frequency Distributions, Percentiles, and Graphical Representations [DOCX] worksheet. Complete the worksheet, and submit it for your assessment.
Note: The following statistical analysis software is required to complete your assessments in this course:
IBM SPSS Statistics Standard or Premium GradPack, version 24 or higher, for PC or Mac. Capella University arranges access to the more robust IBM SPSS Statistics Premium GradPack.
Visit the Resources for this assessment for guidance on how to download and install SPSS.
Competencies Measured
By successfully completing this assessment, you will demonstrate your proficiency in the course competencies through the following assessment scoring guide criteria:
Competency 1: Apply appropriate notation, terminology, procedures, and concepts to statistics problems.
Identify dependent and independent variables.
Explain the identification of types of data.
Locate data on a chart with one error.
Create an APA-formatted ascending frequency table in SPSS with one error.
Construct a bar graph in SPSS in APA format with one error.
Construct a pie chart in SPSS in APA format with one error.
Competency 2: Form logical conclusions about real world problems through descriptive and inferential statistical procedures.
Display data in SPSS in an APA-formatted table with one error.
Identify frequencies and percents with one error.
Competency 4: Apply quantitative analysis to individual, organizational, and social issues.
Describe the relationship between population and sample in an example.
Identify distribution type and number of people.
Assessment 1 – Basics of Research and Statistics, Frequency Distributions, Percentiles, and Graphical Representations
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
Problem Set 1.1: Identifying Variables (Dependent, Independent, Quasi-Independent)
Criterion: Identify dependent and independent variables.
Instructions: For the following list of examples, identify the dependent variable and independent (or quasi-independent) variable.
1. A researcher tests whether cocaine use increases impulsive behavior in a sample of cocaine-dependent and cocaine-inexperienced mice.
Independent Variable: ________
Quasi-Independent Variable: ________
Dependent Variable: ________
2. A professor tests whether students perform better on a multiple-choice or fill-in-the-blank test format.
Independent Variable: ________
Quasi-Independent Variable: ________
Dependent Variable: ________
3. A researcher tests whether smoking by parents influences children’s attitudes toward smoking behavior.
Independent Variable: ________
Quasi-Independent Variable: ________
Dependent Variable: ________
4. A social scientist tests whether attitudes toward morality differ based on political affiliation (Democrat or Republican).
Independent Variable: ________
Quasi-Independent Variable: ________
Dependent Variable: ________
5. A cultural researcher tests whether individuals from different cultures share or differ in the belief that dreams have meaning.
Independent Variable: ________
Quasi-Independent Variable: ________
Dependent Variable: ________
Problem Set 1.2: Understanding Sample and Population
Criterion: Describe the relationship between population and sample.
Instructions: Read the following and answer the question.
Height and educational attainment: Szklarska, Koziel, Bielicki, and Malina (2007) hypothesized that taller young men are more likely to move up the scale of educational attainment compared with shorter individuals from the same social background. They recruited 91,373 nineteen-year-old men to participate in the study.
Do these participants most likely represent a sample or population? Explain.
__________________________________________________________________________________________________________________________________________________________________________
Problem Set 1.3: SPSS Enter Data
Criterion: Enter and display data in SPSS.
Instructions: Use the supplied data to complete Steps 1–8.
Data: Five social media users spent the following number of minutes viewing Twitter:
15.21, 46.18, 12.45, 65.486, 26.852.
Steps:
1. Open SPSS.
2. Click New DataSet in the New Files area and then click Open.
3. Click the Variable View tab at the bottom.
4. In the cell under Name, type Minutes.
5. The variable of Minutes is continuous. In the Decimals column, type 2.
6. Click on the Data View tab at the bottom of the screen.
7. Enter data in the column labeled Minutes.
8. Take a screenshot of your data in SPSS and paste it below.
Problem Set 1.4.a: Grouped or Ungrouped
Criterion: Explain the identification of types of data.
Instruction: Fill in the table below. For each example, state whether it is grouped or ungrouped and why.
Example Grouped or Ungrouped Why
The time (in seconds) it takes 100 children to complete a cognitive skills game.
The number of single mothers with 1, 2, 3, or 4 children.
The number of teenagers who have experimented with smoking (yes, no).
The age (in years) of freshman students in a local college.
Problem Set 1.4.b: Understanding Descriptive and Interferential Statistics
Criterion: Explain the identification of types of data.
Instructions: Read the following and answer the question.
Gun ownership in the United States: Data from Gallup polls over a 40-year period show how gun ownership in the United States has changed. The results are described in the table below, with the percentage of Americans who own guns given in each of 5 decades:
Year %
1972 43
1982 42
1992 48
2002 40
2012 43
Source: Reported at http://www.gallup.com/poll/1645/Guns.aspx
1. Are the percentages reported here an example of descriptive statistics or interferential statistics? _____________________________________________________________
2. Based on the percentages given in the table, how has gun ownership in the United States changed over the past 40 years? ______________________________________________________________________
Problem Set 1.5: Reading a Chart
Criterion: Locate data on a chart.
Instructions: Read the following and answer the questions.
Participant Characteristics Count
Type Token
Sex
Women
Men
Unknown 24,541
23,617
479 878,261
751,188
927
Total 1,630,376
1. Do men or women in this sample speak more words overall (Token Count)? _______________
2. Do men or women in this sample speak more different words (Type Count)? _______________
Problem Set 1.6: Frequencies and Percents
Criterion: Identify frequencies and percents.
Instructions: State whether a cumulative frequency, relative frequency, relative percent, cumulative relative frequency, or cumulative percent is most appropriate for describing the following situations. For cumulative distributions, indicate whether these should be summarized from the top down or from the bottom up.
Data:
1. The frequency of businesses with at least 20 employees: ____________
2. The frequency of college students with less than a 3.0 GPA: ____________
3. The percentage of women completing 1, 2, 3, or 4 tasks simultaneously: ____________
4. The proportion of pregnancies performed in public or private hospitals: ____________
5. The percentage of alcoholics with more than 2 years of substance abuse: ____________
Problem Set 1.7: Understanding Percentages
Criterion: Identify distribution type and number of people.
Instructions: Read the following and answer the questions.
Perceptions of same-sex marriage: In June 2016, a CBS News poll asked a sample of adults worldwide whether it should be legal or not legal for same-sex couples to marry (reported at http://www.pollingreport.com). The opinions of adults worldwide were as follows: 58%, legal; 33%, not legal; and 9%, unsure/no answer.
1. What type of distribution is this? __________________________
2. Knowing that 1,280 adults were polled nationwide, how many Americans polled felt that same-sex couples should be allowed to legally marry? __________________________
Problem Set 1.8: Create an Ascending Frequency Table in SPSS
Criterion: Create an ascending frequency table in SPSS.
Instructions: Complete the following steps.
Data: The number of clicks per hour in forty different tweets: 1, 0, 8, 5, 2, 1, 8, 2, 0, 2, 6, 8, 7, 2, 0, 2, 7, 4, 6, 9, 3, 2, 9, 6, 9, 7, 5, 8, 8, 8, 9, 6, 5, 4, 8, 4, 5, 8, 5, 7
1. Open SPSS.
2. Click New Dataset in the New Files area and then click Open.
3. Click on the Variable View tab.
4. In the cell under Name, type Clicks.
5. The variable of Clicks is discrete, so enter 0 in the Decimals column.
6. Click on the Data View tab at the bottom of the screen.
7. Enter all 40 numbers from from the dataset of number of clicks per hour in the column labeled Clicks.
8. In the Toolbar, click Analyze, select Descriptive Statistics, and then select Frequencies.
9. Select Clicks and then click Arrow to send it over to the right side of the table.
10. Click OK. Copy and paste the ascending values frequency table into the Word document.
11. Go back to Data View, click Analyze, select Descriptive Statistics, and then select Frequencies.
Note: Your answers to this problem set should be two separate SPSS outputs. Save your Clicks data to use in the next two problems.
Problem Set 1.9: Construct a Bar Graph in SPSS
Criterion: Construct a bar graph in SPSS.
Instructions: The Clicks data from Problem Set 1.10 is discrete. Complete the following steps to create a bar chart to examine the data:
1. Go back to your SPSS Statistics Data Editor where your Clicks data should be displayed.
2. In the Toolbar, click Graphs, select Legacy Dialogs, and then select Bar.
3. Click Simple, then select Define. Select Clicks and then click Arrow to send it over to the Category Axis box.
4. Click OK. Copy and paste the bar graph below. (Hint: You might need to use Copy Special and click the .jpeg option.)
5. Optional to answer: What is the shape of the distribution?
Problem Set 1.10: Construct a Pie Chart in SPSS
Criterion: Construct a pie chart in SPSS.
Instructions: Complete the following steps to create a pie chart to examine the attendance data from Problem Set 1.10.
1. Go back to your SPSS Statistics Data Editor where your clicks data should be displayed.
2. Select Data View, click Graphs, select Legacy Dialogs, and then select Pie.
3. Click Summaries for groups of case and then select Define. Select Clicks and then click Arrow to send it over to the Define Slices By box.
4. Click OK. Copy and paste the pie graph below.

 

 

You will develop an analysis report, in five main sections, including (1) introd

You will develop an analysis report, in five main sections, including (1) introduction, (2) research method (which includes your research questions/research objective, description of your data set, and your method of analysis), (3) research results, (4) conclusions, and (5) health policy recommendations. This should be a 4 page analysis report.
Here are the main steps for this assignment.
Step 1: Develop your analysis based on your approved research topic for Assignment #2
Step 2: Develop your formal research question or research objective – derived from the topic
Step 3: Run the analysis using an analytical software package (Analysis ToolPak or R)
Step 4: Create your analysis report based on the following instructions for this assignment.
The Report Structure:
Start with a
1. Cover page (1 page, including running head).
Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.
In the title page include:
Title – this is your approved topic
Student name
Class name
Instructor name
Date
2. Introduction
Introduce the problem or topic being investigated. Including the relevant background information, for example;
Indicate why this is an issue or topic worth researching;
Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and
Specify how others have operationalized this concept and measured these phenomena
Note: Your introduction should not be more than one or two paragraphs.
Literature Review
There is no need for a literature review in this assignment
3. Research Question or Research Hypothesis
What is your Research Question or Research Hypothesis?
***Just in time information: Here are a few points for Research Question or Research Hypothesis
There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.
Examples of non-testable questions are:
How do managers feel about the reorganization?
What do residents feel are the most important problems facing the community?
Respondents’ answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.
In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:
Is there a significant difference between …?
Is there a significant relationship between …?
For example:
Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?
A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words “Is there” with the words “There is,” and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:
There is a significant relationship between the age of managers and their attitudes towards the reorganization.
It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words “no” or “not” to the statement. For example, the null hypotheses for the two examples would be:
There is no significant relationship between the age of managers
and their attitudes towards the reorganization.
There is no significant difference between white and minority residents
with respect to what they feel are the most important problems facing the community.
All statistical testing is done on the null hypothesis…never the hypothesis. The result of a statistical test will enable you to either:
1) reject the null hypothesis, or
2) fail to reject the null hypothesis. Never use the words “accept the null hypothesis.”
*Source: StatPac for Windows Tutorial. (2017). User’s Guide; Formulating Hypotheses from Research Questions. Retrieved May 17, 2019, from https://statpac.com/manual/index.htm?turl=formulatinghypothesesfromresearchquestions.htm
What does significance really mean?
“Significance is a statistical term that tells how sure you are that a difference or relationship exists. To say that a significant difference or relationship exists only tells half the story. We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship? After finding a significant relationship, it is important to evaluate its strength. Significant relationships can be strong or weak. Significant differences can be large or small. It just depends on your sample size.
To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:
P-value: The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.
Example of Welch Two Sample T-test from Exercise 1
The p-value from the above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).
Note: This is an example from the week1 exercise.
An example from Exercise 1
The p-value from the above example, 0.0001, indicates that we’d expect to see a meaningless (random) “number of the employees on payer” difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1).
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):
Confidence Interval Example
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):
Confidence Interval Example
The boundaries of this confidence interval around the difference also provide a way to see the upper [40.44] and lower bounds [-40.82].
As a summary:
“Statistically significant means a result is unlikely due to chance.
The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn’t a difference for all users.
Statistical significance doesn’t mean practical significance. Only by considering context can we determine whether a difference is practically significant; that is, whether it requires action.
The confidence interval around the difference also indicates statistical significance if the interval does not cross zero. It also provides likely boundaries for any improvement to aid in determining if a difference really is noteworthy.
With large sample sizes, you’re virtually certain to see statistically significant results, in such situations, it’s important to interpret the size of the difference”(“Measuring U”, 2019).
*Resource
Measuring U. (2019). Statistically significant. Retrieved May 17, 2019, from: https://measuringu.com/statistically-significant/
Small sample sizes often do not yield statistical significance; when they do, the differences themselves tend also to be practically significant; that is, meaningful enough to warrant action.
4. Research Method
Discuss your research method (in general). Describe the variable or variables that are being analyzed. Identify the statistical test(s) you propose to use to analyze these data and explain why you chose this test(s). Summarize your statistical alternative hypothesis. This section should include the following sub-sections:
a) Describe the Dataset
Example: The primary source of data will be HOSPITAL COMPARE MEDICARE DATA (APA formatted in-text citation). This dataset provides information on hospital characteristics, such as “Number of staffed beds, ownership, system membership, staffing by nurses and non-clinical staff, teaching status, percentage of discharge for Medicare and Medicaid patients, and information regarding the availability of specialty and high-tech services, as well as Electronic Medical Record (EMR) use”. (Describe dataset in 2-3 lines, Google the dataset and find the related website to find more information about the data).
Also, describe the sample size; for example, “The writer is using Medicare data-2013, this data includes 3000 obs. for all of the hospitals in the US.”
b) Describe Variables
Next, review the data you selected and select the variable(s) that may be used to quantitatively measure the concept(s) articulated in your research question or hypothesis.
Return to your Research Question or Hypothesis (as stated above) and evaluate it considering the variables you have selected. (See the sample Table 1).
Table 1. Listing and Definition of variables used for the analysis
Variable
Full – Complete Label
Type of Data
Source
Year
….
….
…..
Source: UMUC, 2019
***Just in time information:
To cite a dataset, you can go with two approaches:
First, look at the note in the dataset for example;
Medicare National Data by County. (2012). Dartmouth Atlas of Health Care, A
Second, use the online citation, for example:
Zare, H., (2019, May). MN Hospital Report Data. Data posted in University of Maryland University College HMGT 400 online classroom, archived at http://campus.umuc.edu
See two examples describing the variables from Minnesota Data below:
Sample Table 1. Listing and Definition of variables used in the analysis
Variable
Definition
Description
of code
Source
Year
hospital_beds
Total facility beds set up and staffed
at the end of the reporting period
Numeric
MN data
2013
year
FY
Categorical
MN data
2013
Source: UMUC, 2019
c) Describe the Research Method for Analysis
First, describe the research method in general (e.g., this is a quantitative method and then explain this method in about one paragraph).
Then, explain the statistical method you plan to use for your analysis (Refer to content in Week 3 on Biostatistics for information on various statistical methods you can choose from).
Example:
Hypothesis: AZ hospitals are more likely to have lower readmission rates for PN compared to CA.
Research Method: To determine whether Arizona hospitals are more likely to have lower readmission rate than California, we will use a t-test, to determine whether differences across hospital types are statistically significant (You can change the test depends on your analysis).
▪ Add one or two sentences on why you need to see the distribution of data before any analysis (e.g., check for outliers, finding the best-fit test; for example, if the data does not have a normal distribution, you can’t use the parametric test, etc.).
▪ Did you have to eliminate outliers? Indicate what you did.
d) Describe the statistical software package
Add one paragraph for the statistical package, e.g., Excel Analysis ToolPak or RStudio. If you use RStudio, please specify the label or name of the exact R script you used.
5. Results
Discuss your analysis findings considering the following:
▪ Upon analysis, how many observations did you find in your dataset and how many observations did you find for your selected variables, report the % of variable(s) that were missing.
Create tables and other summary tabulations, diagrams, and graphs to present the results of your statistical analysis. You may start with just statistical summaries of your data (summary tables, including N, mean, std. dev.). Then create additional tables to present results that relate directly to your research question(s). Make sure to completely and correctly label all the columns, rows, and titles in any of your tables. Label the tables in sequence (Table 1, Table 2, etc.) so that you can refer to them appropriately in your discussions and other subsequent sections of your analysis report. Then create appropriate graphs to depict your analysis results. The data in your results tables may be graphed to emphasize a point or relate directly to your research question(s). Once again, make sure to completely and correctly label all the axes, legends, and titles in any of your graphs. Label the graphs in sequence (Figure 1, Figure 2, etc.) so that you can refer to them appropriately in your discussions and other subsequent sections of your analysis report.
For example
Table 2. Descriptive analysis to compare % of BL in Medicare beneficiary, MD vs. VA- 2013
Variable
Obs.
Mean
SD
P-value
Per of Lipid in MD
24
83.20
2.32
0.4064
Per of Lipid in VA
124
82.69
4.41
Source: UMUC, 2019
When you have tables and graphs ready, discuss your findings as presented in those tables and graphs and state your statistical conclusion(s). That is, do the results present evidence in favor of the null hypothesis or evidence that contradicts the null hypothesis?
6. Conclusion and Discussion
Review your research question(s) or hypothesis.
How has your analysis informed your research question(s) or hypothesis? Present your conclusion(s) from the results (as presented above) and discuss the meaning of your conclusion(s) considering the research question(s) or hypothesis as presented in your introduction.
At the end of this section, add one or two sentences to discuss the limitations (including biases) associated with your present analysis and any other statements you think are important in understanding the results of this analysis.
References
Include a reference page listing the bibliographic information for all sources cited in this report. This information should be consistent with the requirements specified in the American Psychological Association (APA) format and style guide.

 

 

You will develop an analysis report, in five main sections, including (1) introd

You will develop an analysis report, in five main sections, including (1) introduction, (2) research method (which includes your research questions/research objective, description of your data set, and your method of analysis), (3) research results, (4) conclusions, and (5) health policy recommendations. This should be a 4 page analysis report.
Here are the main steps for this assignment.
Step 1: Develop your analysis based on your approved research topic for Assignment #2
Step 2: Develop your formal research question or research objective – derived from the topic
Step 3: Run the analysis using an analytical software package (Analysis ToolPak or R)
Step 4: Create your analysis report based on the following instructions for this assignment.
The Report Structure:
Start with a
1. Cover page (1 page, including running head).
Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.
In the title page include:
Title – this is your approved topic
Student name
Class name
Instructor name
Date
2. Introduction
Introduce the problem or topic being investigated. Including the relevant background information, for example;
Indicate why this is an issue or topic worth researching;
Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and
Specify how others have operationalized this concept and measured these phenomena
Note: Your introduction should not be more than one or two paragraphs.
Literature Review
There is no need for a literature review in this assignment
3. Research Question or Research Hypothesis
What is your Research Question or Research Hypothesis?
***Just in time information: Here are a few points for Research Question or Research Hypothesis
There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.
Examples of non-testable questions are:
How do managers feel about the reorganization?
What do residents feel are the most important problems facing the community?
Respondents’ answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.
In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:
Is there a significant difference between …?
Is there a significant relationship between …?
For example:
Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?
A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words “Is there” with the words “There is,” and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:
There is a significant relationship between the age of managers and their attitudes towards the reorganization.
It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words “no” or “not” to the statement. For example, the null hypotheses for the two examples would be:
There is no significant relationship between the age of managers
and their attitudes towards the reorganization.
There is no significant difference between white and minority residents
with respect to what they feel are the most important problems facing the community.
All statistical testing is done on the null hypothesis…never the hypothesis. The result of a statistical test will enable you to either:
1) reject the null hypothesis, or
2) fail to reject the null hypothesis. Never use the words “accept the null hypothesis.”
*Source: StatPac for Windows Tutorial. (2017). User’s Guide; Formulating Hypotheses from Research Questions. Retrieved May 17, 2019, from https://statpac.com/manual/index.htm?turl=formulatinghypothesesfromresearchquestions.htm
What does significance really mean?
“Significance is a statistical term that tells how sure you are that a difference or relationship exists. To say that a significant difference or relationship exists only tells half the story. We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship? After finding a significant relationship, it is important to evaluate its strength. Significant relationships can be strong or weak. Significant differences can be large or small. It just depends on your sample size.
To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:
P-value: The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.
Example of Welch Two Sample T-test from Exercise 1
The p-value from the above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).
Note: This is an example from the week1 exercise.
An example from Exercise 1
The p-value from the above example, 0.0001, indicates that we’d expect to see a meaningless (random) “number of the employees on payer” difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1).
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):
Confidence Interval Example
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):
Confidence Interval Example
The boundaries of this confidence interval around the difference also provide a way to see the upper [40.44] and lower bounds [-40.82].
As a summary:
“Statistically significant means a result is unlikely due to chance.
The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn’t a difference for all users.
Statistical significance doesn’t mean practical significance. Only by considering context can we determine whether a difference is practically significant; that is, whether it requires action.
The confidence interval around the difference also indicates statistical significance if the interval does not cross zero. It also provides likely boundaries for any improvement to aid in determining if a difference really is noteworthy.
With large sample sizes, you’re virtually certain to see statistically significant results, in such situations, it’s important to interpret the size of the difference”(“Measuring U”, 2019).
*Resource
Measuring U. (2019). Statistically significant. Retrieved May 17, 2019, from: https://measuringu.com/statistically-significant/
Small sample sizes often do not yield statistical significance; when they do, the differences themselves tend also to be practically significant; that is, meaningful enough to warrant action.
4. Research Method
Discuss your research method (in general). Describe the variable or variables that are being analyzed. Identify the statistical test(s) you propose to use to analyze these data and explain why you chose this test(s). Summarize your statistical alternative hypothesis. This section should include the following sub-sections:
a) Describe the Dataset
Example: The primary source of data will be HOSPITAL COMPARE MEDICARE DATA (APA formatted in-text citation). This dataset provides information on hospital characteristics, such as “Number of staffed beds, ownership, system membership, staffing by nurses and non-clinical staff, teaching status, percentage of discharge for Medicare and Medicaid patients, and information regarding the availability of specialty and high-tech services, as well as Electronic Medical Record (EMR) use”. (Describe dataset in 2-3 lines, Google the dataset and find the related website to find more information about the data).
Also, describe the sample size; for example, “The writer is using Medicare data-2013, this data includes 3000 obs. for all of the hospitals in the US.”
b) Describe Variables
Next, review the data you selected and select the variable(s) that may be used to quantitatively measure the concept(s) articulated in your research question or hypothesis.
Return to your Research Question or Hypothesis (as stated above) and evaluate it considering the variables you have selected. (See the sample Table 1).
Table 1. Listing and Definition of variables used for the analysis
Variable
Full – Complete Label
Type of Data
Source
Year
….
….
…..
Source: UMUC, 2019
***Just in time information:
To cite a dataset, you can go with two approaches:
First, look at the note in the dataset for example;
Medicare National Data by County. (2012). Dartmouth Atlas of Health Care, A
Second, use the online citation, for example:
Zare, H., (2019, May). MN Hospital Report Data. Data posted in University of Maryland University College HMGT 400 online classroom, archived at http://campus.umuc.edu
See two examples describing the variables from Minnesota Data below:
Sample Table 1. Listing and Definition of variables used in the analysis
Variable
Definition
Description
of code
Source
Year
hospital_beds
Total facility beds set up and staffed
at the end of the reporting period
Numeric
MN data
2013
year
FY
Categorical
MN data
2013
Source: UMUC, 2019
c) Describe the Research Method for Analysis
First, describe the research method in general (e.g., this is a quantitative method and then explain this method in about one paragraph).
Then, explain the statistical method you plan to use for your analysis (Refer to content in Week 3 on Biostatistics for information on various statistical methods you can choose from).
Example:
Hypothesis: AZ hospitals are more likely to have lower readmission rates for PN compared to CA.
Research Method: To determine whether Arizona hospitals are more likely to have lower readmission rate than California, we will use a t-test, to determine whether differences across hospital types are statistically significant (You can change the test depends on your analysis).
▪ Add one or two sentences on why you need to see the distribution of data before any analysis (e.g., check for outliers, finding the best-fit test; for example, if the data does not have a normal distribution, you can’t use the parametric test, etc.).
▪ Did you have to eliminate outliers? Indicate what you did.
d) Describe the statistical software package
Add one paragraph for the statistical package, e.g., Excel Analysis ToolPak or RStudio. If you use RStudio, please specify the label or name of the exact R script you used.
5. Results
Discuss your analysis findings considering the following:
▪ Upon analysis, how many observations did you find in your dataset and how many observations did you find for your selected variables, report the % of variable(s) that were missing.
Create tables and other summary tabulations, diagrams, and graphs to present the results of your statistical analysis. You may start with just statistical summaries of your data (summary tables, including N, mean, std. dev.). Then create additional tables to present results that relate directly to your research question(s). Make sure to completely and correctly label all the columns, rows, and titles in any of your tables. Label the tables in sequence (Table 1, Table 2, etc.) so that you can refer to them appropriately in your discussions and other subsequent sections of your analysis report. Then create appropriate graphs to depict your analysis results. The data in your results tables may be graphed to emphasize a point or relate directly to your research question(s). Once again, make sure to completely and correctly label all the axes, legends, and titles in any of your graphs. Label the graphs in sequence (Figure 1, Figure 2, etc.) so that you can refer to them appropriately in your discussions and other subsequent sections of your analysis report.
For example
Table 2. Descriptive analysis to compare % of BL in Medicare beneficiary, MD vs. VA- 2013
Variable
Obs.
Mean
SD
P-value
Per of Lipid in MD
24
83.20
2.32
0.4064
Per of Lipid in VA
124
82.69
4.41
Source: UMUC, 2019
When you have tables and graphs ready, discuss your findings as presented in those tables and graphs and state your statistical conclusion(s). That is, do the results present evidence in favor of the null hypothesis or evidence that contradicts the null hypothesis?
6. Conclusion and Discussion
Review your research question(s) or hypothesis.
How has your analysis informed your research question(s) or hypothesis? Present your conclusion(s) from the results (as presented above) and discuss the meaning of your conclusion(s) considering the research question(s) or hypothesis as presented in your introduction.
At the end of this section, add one or two sentences to discuss the limitations (including biases) associated with your present analysis and any other statements you think are important in understanding the results of this analysis.
References
Include a reference page listing the bibliographic information for all sources cited in this report. This information should be consistent with the requirements specified in the American Psychological Association (APA) format and style guide.

 

 

You will develop an analysis report, in five main sections, including (1) introd

You will develop an analysis report, in five main sections, including (1) introduction, (2) research method (which includes your research questions/research objective, description of your data set, and your method of analysis), (3) research results, (4) conclusions, and (5) health policy recommendations. This should be a 4 page analysis report.
Here are the main steps for this assignment.
Step 1: Develop your analysis based on your approved research topic for Assignment #2
Step 2: Develop your formal research question or research objective – derived from the topic
Step 3: Run the analysis using an analytical software package (Analysis ToolPak or R)
Step 4: Create your analysis report based on the following instructions for this assignment.
The Report Structure:
Start with a
1. Cover page (1 page, including running head).
Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.
In the title page include:
Title – this is your approved topic
Student name
Class name
Instructor name
Date
2. Introduction
Introduce the problem or topic being investigated. Including the relevant background information, for example;
Indicate why this is an issue or topic worth researching;
Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and
Specify how others have operationalized this concept and measured these phenomena
Note: Your introduction should not be more than one or two paragraphs.
Literature Review
There is no need for a literature review in this assignment
3. Research Question or Research Hypothesis
What is your Research Question or Research Hypothesis?
***Just in time information: Here are a few points for Research Question or Research Hypothesis
There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.
Examples of non-testable questions are:
How do managers feel about the reorganization?
What do residents feel are the most important problems facing the community?
Respondents’ answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.
In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:
Is there a significant difference between …?
Is there a significant relationship between …?
For example:
Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?
A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words “Is there” with the words “There is,” and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:
There is a significant relationship between the age of managers and their attitudes towards the reorganization.
It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words “no” or “not” to the statement. For example, the null hypotheses for the two examples would be:
There is no significant relationship between the age of managers
and their attitudes towards the reorganization.
There is no significant difference between white and minority residents
with respect to what they feel are the most important problems facing the community.
All statistical testing is done on the null hypothesis…never the hypothesis. The result of a statistical test will enable you to either:
1) reject the null hypothesis, or
2) fail to reject the null hypothesis. Never use the words “accept the null hypothesis.”
*Source: StatPac for Windows Tutorial. (2017). User’s Guide; Formulating Hypotheses from Research Questions. Retrieved May 17, 2019, from https://statpac.com/manual/index.htm?turl=formulatinghypothesesfromresearchquestions.htm
What does significance really mean?
“Significance is a statistical term that tells how sure you are that a difference or relationship exists. To say that a significant difference or relationship exists only tells half the story. We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship? After finding a significant relationship, it is important to evaluate its strength. Significant relationships can be strong or weak. Significant differences can be large or small. It just depends on your sample size.
To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:
P-value: The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.
Example of Welch Two Sample T-test from Exercise 1
The p-value from the above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).
Note: This is an example from the week1 exercise.
An example from Exercise 1
The p-value from the above example, 0.0001, indicates that we’d expect to see a meaningless (random) “number of the employees on payer” difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1).
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):
Confidence Interval Example
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):
Confidence Interval Example
The boundaries of this confidence interval around the difference also provide a way to see the upper [40.44] and lower bounds [-40.82].
As a summary:
“Statistically significant means a result is unlikely due to chance.
The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn’t a difference for all users.
Statistical significance doesn’t mean practical significance. Only by considering context can we determine whether a difference is practically significant; that is, whether it requires action.
The confidence interval around the difference also indicates statistical significance if the interval does not cross zero. It also provides likely boundaries for any improvement to aid in determining if a difference really is noteworthy.
With large sample sizes, you’re virtually certain to see statistically significant results, in such situations, it’s important to interpret the size of the difference”(“Measuring U”, 2019).
*Resource
Measuring U. (2019). Statistically significant. Retrieved May 17, 2019, from: https://measuringu.com/statistically-significant/
Small sample sizes often do not yield statistical significance; when they do, the differences themselves tend also to be practically significant; that is, meaningful enough to warrant action.
4. Research Method
Discuss your research method (in general). Describe the variable or variables that are being analyzed. Identify the statistical test(s) you propose to use to analyze these data and explain why you chose this test(s). Summarize your statistical alternative hypothesis. This section should include the following sub-sections:
a) Describe the Dataset
Example: The primary source of data will be HOSPITAL COMPARE MEDICARE DATA (APA formatted in-text citation). This dataset provides information on hospital characteristics, such as “Number of staffed beds, ownership, system membership, staffing by nurses and non-clinical staff, teaching status, percentage of discharge for Medicare and Medicaid patients, and information regarding the availability of specialty and high-tech services, as well as Electronic Medical Record (EMR) use”. (Describe dataset in 2-3 lines, Google the dataset and find the related website to find more information about the data).
Also, describe the sample size; for example, “The writer is using Medicare data-2013, this data includes 3000 obs. for all of the hospitals in the US.”
b) Describe Variables
Next, review the data you selected and select the variable(s) that may be used to quantitatively measure the concept(s) articulated in your research question or hypothesis.
Return to your Research Question or Hypothesis (as stated above) and evaluate it considering the variables you have selected. (See the sample Table 1).
Table 1. Listing and Definition of variables used for the analysis
Variable
Full – Complete Label
Type of Data
Source
Year
….
….
…..
Source: UMUC, 2019
***Just in time information:
To cite a dataset, you can go with two approaches:
First, look at the note in the dataset for example;
Medicare National Data by County. (2012). Dartmouth Atlas of Health Care, A
Second, use the online citation, for example:
Zare, H., (2019, May). MN Hospital Report Data. Data posted in University of Maryland University College HMGT 400 online classroom, archived at http://campus.umuc.edu
See two examples describing the variables from Minnesota Data below:
Sample Table 1. Listing and Definition of variables used in the analysis
Variable
Definition
Description
of code
Source
Year
hospital_beds
Total facility beds set up and staffed
at the end of the reporting period
Numeric
MN data
2013
year
FY
Categorical
MN data
2013
Source: UMUC, 2019
c) Describe the Research Method for Analysis
First, describe the research method in general (e.g., this is a quantitative method and then explain this method in about one paragraph).
Then, explain the statistical method you plan to use for your analysis (Refer to content in Week 3 on Biostatistics for information on various statistical methods you can choose from).
Example:
Hypothesis: AZ hospitals are more likely to have lower readmission rates for PN compared to CA.
Research Method: To determine whether Arizona hospitals are more likely to have lower readmission rate than California, we will use a t-test, to determine whether differences across hospital types are statistically significant (You can change the test depends on your analysis).
▪ Add one or two sentences on why you need to see the distribution of data before any analysis (e.g., check for outliers, finding the best-fit test; for example, if the data does not have a normal distribution, you can’t use the parametric test, etc.).
▪ Did you have to eliminate outliers? Indicate what you did.
d) Describe the statistical software package
Add one paragraph for the statistical package, e.g., Excel Analysis ToolPak or RStudio. If you use RStudio, please specify the label or name of the exact R script you used.
5. Results
Discuss your analysis findings considering the following:
▪ Upon analysis, how many observations did you find in your dataset and how many observations did you find for your selected variables, report the % of variable(s) that were missing.
Create tables and other summary tabulations, diagrams, and graphs to present the results of your statistical analysis. You may start with just statistical summaries of your data (summary tables, including N, mean, std. dev.). Then create additional tables to present results that relate directly to your research question(s). Make sure to completely and correctly label all the columns, rows, and titles in any of your tables. Label the tables in sequence (Table 1, Table 2, etc.) so that you can refer to them appropriately in your discussions and other subsequent sections of your analysis report. Then create appropriate graphs to depict your analysis results. The data in your results tables may be graphed to emphasize a point or relate directly to your research question(s). Once again, make sure to completely and correctly label all the axes, legends, and titles in any of your graphs. Label the graphs in sequence (Figure 1, Figure 2, etc.) so that you can refer to them appropriately in your discussions and other subsequent sections of your analysis report.
For example
Table 2. Descriptive analysis to compare % of BL in Medicare beneficiary, MD vs. VA- 2013
Variable
Obs.
Mean
SD
P-value
Per of Lipid in MD
24
83.20
2.32
0.4064
Per of Lipid in VA
124
82.69
4.41
Source: UMUC, 2019
When you have tables and graphs ready, discuss your findings as presented in those tables and graphs and state your statistical conclusion(s). That is, do the results present evidence in favor of the null hypothesis or evidence that contradicts the null hypothesis?
6. Conclusion and Discussion
Review your research question(s) or hypothesis.
How has your analysis informed your research question(s) or hypothesis? Present your conclusion(s) from the results (as presented above) and discuss the meaning of your conclusion(s) considering the research question(s) or hypothesis as presented in your introduction.
At the end of this section, add one or two sentences to discuss the limitations (including biases) associated with your present analysis and any other statements you think are important in understanding the results of this analysis.
References
Include a reference page listing the bibliographic information for all sources cited in this report. This information should be consistent with the requirements specified in the American Psychological Association (APA) format and style guide.

 

 

You will develop an analysis report, in five main sections, including (1) introd

You will develop an analysis report, in five main sections, including (1) introduction, (2) research method (which includes your research questions/research objective, description of your data set, and your method of analysis), (3) research results, (4) conclusions, and (5) health policy recommendations. This should be a 4 page analysis report.
Here are the main steps for this assignment.
Step 1: Develop your analysis based on your approved research topic for Assignment #2
Step 2: Develop your formal research question or research objective – derived from the topic
Step 3: Run the analysis using an analytical software package (Analysis ToolPak or R)
Step 4: Create your analysis report based on the following instructions for this assignment.
The Report Structure:
Start with a
1. Cover page (1 page, including running head).
Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.
In the title page include:
Title – this is your approved topic
Student name
Class name
Instructor name
Date
2. Introduction
Introduce the problem or topic being investigated. Including the relevant background information, for example;
Indicate why this is an issue or topic worth researching;
Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and
Specify how others have operationalized this concept and measured these phenomena
Note: Your introduction should not be more than one or two paragraphs.
Literature Review
There is no need for a literature review in this assignment
3. Research Question or Research Hypothesis
What is your Research Question or Research Hypothesis?
***Just in time information: Here are a few points for Research Question or Research Hypothesis
There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.
Examples of non-testable questions are:
How do managers feel about the reorganization?
What do residents feel are the most important problems facing the community?
Respondents’ answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.
In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:
Is there a significant difference between …?
Is there a significant relationship between …?
For example:
Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?
A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words “Is there” with the words “There is,” and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:
There is a significant relationship between the age of managers and their attitudes towards the reorganization.
It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words “no” or “not” to the statement. For example, the null hypotheses for the two examples would be:
There is no significant relationship between the age of managers
and their attitudes towards the reorganization.
There is no significant difference between white and minority residents
with respect to what they feel are the most important problems facing the community.
All statistical testing is done on the null hypothesis…never the hypothesis. The result of a statistical test will enable you to either:
1) reject the null hypothesis, or
2) fail to reject the null hypothesis. Never use the words “accept the null hypothesis.”
*Source: StatPac for Windows Tutorial. (2017). User’s Guide; Formulating Hypotheses from Research Questions. Retrieved May 17, 2019, from https://statpac.com/manual/index.htm?turl=formulatinghypothesesfromresearchquestions.htm
What does significance really mean?
“Significance is a statistical term that tells how sure you are that a difference or relationship exists. To say that a significant difference or relationship exists only tells half the story. We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship? After finding a significant relationship, it is important to evaluate its strength. Significant relationships can be strong or weak. Significant differences can be large or small. It just depends on your sample size.
To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:
P-value: The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.
Example of Welch Two Sample T-test from Exercise 1
The p-value from the above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).
Note: This is an example from the week1 exercise.
An example from Exercise 1
The p-value from the above example, 0.0001, indicates that we’d expect to see a meaningless (random) “number of the employees on payer” difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1).
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):
Confidence Interval Example
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):
Confidence Interval Example
The boundaries of this confidence interval around the difference also provide a way to see the upper [40.44] and lower bounds [-40.82].
As a summary:
“Statistically significant means a result is unlikely due to chance.
The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn’t a difference for all users.
Statistical significance doesn’t mean practical significance. Only by considering context can we determine whether a difference is practically significant; that is, whether it requires action.
The confidence interval around the difference also indicates statistical significance if the interval does not cross zero. It also provides likely boundaries for any improvement to aid in determining if a difference really is noteworthy.
With large sample sizes, you’re virtually certain to see statistically significant results, in such situations, it’s important to interpret the size of the difference”(“Measuring U”, 2019).
*Resource
Measuring U. (2019). Statistically significant. Retrieved May 17, 2019, from: https://measuringu.com/statistically-significant/
Small sample sizes often do not yield statistical significance; when they do, the differences themselves tend also to be practically significant; that is, meaningful enough to warrant action.
4. Research Method
Discuss your research method (in general). Describe the variable or variables that are being analyzed. Identify the statistical test(s) you propose to use to analyze these data and explain why you chose this test(s). Summarize your statistical alternative hypothesis. This section should include the following sub-sections:
a) Describe the Dataset
Example: The primary source of data will be HOSPITAL COMPARE MEDICARE DATA (APA formatted in-text citation). This dataset provides information on hospital characteristics, such as “Number of staffed beds, ownership, system membership, staffing by nurses and non-clinical staff, teaching status, percentage of discharge for Medicare and Medicaid patients, and information regarding the availability of specialty and high-tech services, as well as Electronic Medical Record (EMR) use”. (Describe dataset in 2-3 lines, Google the dataset and find the related website to find more information about the data).
Also, describe the sample size; for example, “The writer is using Medicare data-2013, this data includes 3000 obs. for all of the hospitals in the US.”
b) Describe Variables
Next, review the data you selected and select the variable(s) that may be used to quantitatively measure the concept(s) articulated in your research question or hypothesis.
Return to your Research Question or Hypothesis (as stated above) and evaluate it considering the variables you have selected. (See the sample Table 1).
Table 1. Listing and Definition of variables used for the analysis
Variable
Full – Complete Label
Type of Data
Source
Year
….
….
…..
Source: UMUC, 2019
***Just in time information:
To cite a dataset, you can go with two approaches:
First, look at the note in the dataset for example;
Medicare National Data by County. (2012). Dartmouth Atlas of Health Care, A
Second, use the online citation, for example:
Zare, H., (2019, May). MN Hospital Report Data. Data posted in University of Maryland University College HMGT 400 online classroom, archived at http://campus.umuc.edu
See two examples describing the variables from Minnesota Data below:
Sample Table 1. Listing and Definition of variables used in the analysis
Variable
Definition
Description
of code
Source
Year
hospital_beds
Total facility beds set up and staffed
at the end of the reporting period
Numeric
MN data
2013
year
FY
Categorical
MN data
2013
Source: UMUC, 2019
c) Describe the Research Method for Analysis
First, describe the research method in general (e.g., this is a quantitative method and then explain this method in about one paragraph).
Then, explain the statistical method you plan to use for your analysis (Refer to content in Week 3 on Biostatistics for information on various statistical methods you can choose from).
Example:
Hypothesis: AZ hospitals are more likely to have lower readmission rates for PN compared to CA.
Research Method: To determine whether Arizona hospitals are more likely to have lower readmission rate than California, we will use a t-test, to determine whether differences across hospital types are statistically significant (You can change the test depends on your analysis).
▪ Add one or two sentences on why you need to see the distribution of data before any analysis (e.g., check for outliers, finding the best-fit test; for example, if the data does not have a normal distribution, you can’t use the parametric test, etc.).
▪ Did you have to eliminate outliers? Indicate what you did.
d) Describe the statistical software package
Add one paragraph for the statistical package, e.g., Excel Analysis ToolPak or RStudio. If you use RStudio, please specify the label or name of the exact R script you used.
5. Results
Discuss your analysis findings considering the following:
▪ Upon analysis, how many observations did you find in your dataset and how many observations did you find for your selected variables, report the % of variable(s) that were missing.
Create tables and other summary tabulations, diagrams, and graphs to present the results of your statistical analysis. You may start with just statistical summaries of your data (summary tables, including N, mean, std. dev.). Then create additional tables to present results that relate directly to your research question(s). Make sure to completely and correctly label all the columns, rows, and titles in any of your tables. Label the tables in sequence (Table 1, Table 2, etc.) so that you can refer to them appropriately in your discussions and other subsequent sections of your analysis report. Then create appropriate graphs to depict your analysis results. The data in your results tables may be graphed to emphasize a point or relate directly to your research question(s). Once again, make sure to completely and correctly label all the axes, legends, and titles in any of your graphs. Label the graphs in sequence (Figure 1, Figure 2, etc.) so that you can refer to them appropriately in your discussions and other subsequent sections of your analysis report.
For example
Table 2. Descriptive analysis to compare % of BL in Medicare beneficiary, MD vs. VA- 2013
Variable
Obs.
Mean
SD
P-value
Per of Lipid in MD
24
83.20
2.32
0.4064
Per of Lipid in VA
124
82.69
4.41
Source: UMUC, 2019
When you have tables and graphs ready, discuss your findings as presented in those tables and graphs and state your statistical conclusion(s). That is, do the results present evidence in favor of the null hypothesis or evidence that contradicts the null hypothesis?
6. Conclusion and Discussion
Review your research question(s) or hypothesis.
How has your analysis informed your research question(s) or hypothesis? Present your conclusion(s) from the results (as presented above) and discuss the meaning of your conclusion(s) considering the research question(s) or hypothesis as presented in your introduction.
At the end of this section, add one or two sentences to discuss the limitations (including biases) associated with your present analysis and any other statements you think are important in understanding the results of this analysis.
References
Include a reference page listing the bibliographic information for all sources cited in this report. This information should be consistent with the requirements specified in the American Psychological Association (APA) format and style guide.

 

 

You will develop an analysis report, in five main sections, including (1) introd

You will develop an analysis report, in five main sections, including (1) introduction, (2) research method (which includes your research questions/research objective, description of your data set, and your method of analysis), (3) research results, (4) conclusions, and (5) health policy recommendations. This should be a 4 page analysis report.
Here are the main steps for this assignment.
Step 1: Develop your analysis based on your approved research topic for Assignment #2
Step 2: Develop your formal research question or research objective – derived from the topic
Step 3: Run the analysis using an analytical software package (Analysis ToolPak or R)
Step 4: Create your analysis report based on the following instructions for this assignment.
The Report Structure:
Start with a
1. Cover page (1 page, including running head).
Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.
In the title page include:
Title – this is your approved topic
Student name
Class name
Instructor name
Date
2. Introduction
Introduce the problem or topic being investigated. Including the relevant background information, for example;
Indicate why this is an issue or topic worth researching;
Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and
Specify how others have operationalized this concept and measured these phenomena
Note: Your introduction should not be more than one or two paragraphs.
Literature Review
There is no need for a literature review in this assignment
3. Research Question or Research Hypothesis
What is your Research Question or Research Hypothesis?
***Just in time information: Here are a few points for Research Question or Research Hypothesis
There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.
Examples of non-testable questions are:
How do managers feel about the reorganization?
What do residents feel are the most important problems facing the community?
Respondents’ answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.
In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:
Is there a significant difference between …?
Is there a significant relationship between …?
For example:
Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?
A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words “Is there” with the words “There is,” and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:
There is a significant relationship between the age of managers and their attitudes towards the reorganization.
It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words “no” or “not” to the statement. For example, the null hypotheses for the two examples would be:
There is no significant relationship between the age of managers
and their attitudes towards the reorganization.
There is no significant difference between white and minority residents
with respect to what they feel are the most important problems facing the community.
All statistical testing is done on the null hypothesis…never the hypothesis. The result of a statistical test will enable you to either:
1) reject the null hypothesis, or
2) fail to reject the null hypothesis. Never use the words “accept the null hypothesis.”
*Source: StatPac for Windows Tutorial. (2017). User’s Guide; Formulating Hypotheses from Research Questions. Retrieved May 17, 2019, from https://statpac.com/manual/index.htm?turl=formulatinghypothesesfromresearchquestions.htm
What does significance really mean?
“Significance is a statistical term that tells how sure you are that a difference or relationship exists. To say that a significant difference or relationship exists only tells half the story. We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship? After finding a significant relationship, it is important to evaluate its strength. Significant relationships can be strong or weak. Significant differences can be large or small. It just depends on your sample size.
To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:
P-value: The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.
Example of Welch Two Sample T-test from Exercise 1
The p-value from the above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).
Note: This is an example from the week1 exercise.
An example from Exercise 1
The p-value from the above example, 0.0001, indicates that we’d expect to see a meaningless (random) “number of the employees on payer” difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1).
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):
Confidence Interval Example
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):
Confidence Interval Example
The boundaries of this confidence interval around the difference also provide a way to see the upper [40.44] and lower bounds [-40.82].
As a summary:
“Statistically significant means a result is unlikely due to chance.
The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn’t a difference for all users.
Statistical significance doesn’t mean practical significance. Only by considering context can we determine whether a difference is practically significant; that is, whether it requires action.
The confidence interval around the difference also indicates statistical significance if the interval does not cross zero. It also provides likely boundaries for any improvement to aid in determining if a difference really is noteworthy.
With large sample sizes, you’re virtually certain to see statistically significant results, in such situations, it’s important to interpret the size of the difference”(“Measuring U”, 2019).
*Resource
Measuring U. (2019). Statistically significant. Retrieved May 17, 2019, from: https://measuringu.com/statistically-significant/
Small sample sizes often do not yield statistical significance; when they do, the differences themselves tend also to be practically significant; that is, meaningful enough to warrant action.
4. Research Method
Discuss your research method (in general). Describe the variable or variables that are being analyzed. Identify the statistical test(s) you propose to use to analyze these data and explain why you chose this test(s). Summarize your statistical alternative hypothesis. This section should include the following sub-sections:
a) Describe the Dataset
Example: The primary source of data will be HOSPITAL COMPARE MEDICARE DATA (APA formatted in-text citation). This dataset provides information on hospital characteristics, such as “Number of staffed beds, ownership, system membership, staffing by nurses and non-clinical staff, teaching status, percentage of discharge for Medicare and Medicaid patients, and information regarding the availability of specialty and high-tech services, as well as Electronic Medical Record (EMR) use”. (Describe dataset in 2-3 lines, Google the dataset and find the related website to find more information about the data).
Also, describe the sample size; for example, “The writer is using Medicare data-2013, this data includes 3000 obs. for all of the hospitals in the US.”
b) Describe Variables
Next, review the data you selected and select the variable(s) that may be used to quantitatively measure the concept(s) articulated in your research question or hypothesis.
Return to your Research Question or Hypothesis (as stated above) and evaluate it considering the variables you have selected. (See the sample Table 1).
Table 1. Listing and Definition of variables used for the analysis
Variable
Full – Complete Label
Type of Data
Source
Year
….
….
…..
Source: UMUC, 2019
***Just in time information:
To cite a dataset, you can go with two approaches:
First, look at the note in the dataset for example;
Medicare National Data by County. (2012). Dartmouth Atlas of Health Care, A
Second, use the online citation, for example:
Zare, H., (2019, May). MN Hospital Report Data. Data posted in University of Maryland University College HMGT 400 online classroom, archived at http://campus.umuc.edu
See two examples describing the variables from Minnesota Data below:
Sample Table 1. Listing and Definition of variables used in the analysis
Variable
Definition
Description
of code
Source
Year
hospital_beds
Total facility beds set up and staffed
at the end of the reporting period
Numeric
MN data
2013
year
FY
Categorical
MN data
2013
Source: UMUC, 2019
c) Describe the Research Method for Analysis
First, describe the research method in general (e.g., this is a quantitative method and then explain this method in about one paragraph).
Then, explain the statistical method you plan to use for your analysis (Refer to content in Week 3 on Biostatistics for information on various statistical methods you can choose from).
Example:
Hypothesis: AZ hospitals are more likely to have lower readmission rates for PN compared to CA.
Research Method: To determine whether Arizona hospitals are more likely to have lower readmission rate than California, we will use a t-test, to determine whether differences across hospital types are statistically significant (You can change the test depends on your analysis).
▪ Add one or two sentences on why you need to see the distribution of data before any analysis (e.g., check for outliers, finding the best-fit test; for example, if the data does not have a normal distribution, you can’t use the parametric test, etc.).
▪ Did you have to eliminate outliers? Indicate what you did.
d) Describe the statistical software package
Add one paragraph for the statistical package, e.g., Excel Analysis ToolPak or RStudio. If you use RStudio, please specify the label or name of the exact R script you used.
5. Results
Discuss your analysis findings considering the following:
▪ Upon analysis, how many observations did you find in your dataset and how many observations did you find for your selected variables, report the % of variable(s) that were missing.
Create tables and other summary tabulations, diagrams, and graphs to present the results of your statistical analysis. You may start with just statistical summaries of your data (summary tables, including N, mean, std. dev.). Then create additional tables to present results that relate directly to your research question(s). Make sure to completely and correctly label all the columns, rows, and titles in any of your tables. Label the tables in sequence (Table 1, Table 2, etc.) so that you can refer to them appropriately in your discussions and other subsequent sections of your analysis report. Then create appropriate graphs to depict your analysis results. The data in your results tables may be graphed to emphasize a point or relate directly to your research question(s). Once again, make sure to completely and correctly label all the axes, legends, and titles in any of your graphs. Label the graphs in sequence (Figure 1, Figure 2, etc.) so that you can refer to them appropriately in your discussions and other subsequent sections of your analysis report.
For example
Table 2. Descriptive analysis to compare % of BL in Medicare beneficiary, MD vs. VA- 2013
Variable
Obs.
Mean
SD
P-value
Per of Lipid in MD
24
83.20
2.32
0.4064
Per of Lipid in VA
124
82.69
4.41
Source: UMUC, 2019
When you have tables and graphs ready, discuss your findings as presented in those tables and graphs and state your statistical conclusion(s). That is, do the results present evidence in favor of the null hypothesis or evidence that contradicts the null hypothesis?
6. Conclusion and Discussion
Review your research question(s) or hypothesis.
How has your analysis informed your research question(s) or hypothesis? Present your conclusion(s) from the results (as presented above) and discuss the meaning of your conclusion(s) considering the research question(s) or hypothesis as presented in your introduction.
At the end of this section, add one or two sentences to discuss the limitations (including biases) associated with your present analysis and any other statements you think are important in understanding the results of this analysis.
References
Include a reference page listing the bibliographic information for all sources cited in this report. This information should be consistent with the requirements specified in the American Psychological Association (APA) format and style guide.

 

 

Note: “The Herfindahl–Hirschman Index is a commonly accepted measure of market c

Note: “The Herfindahl–Hirschman Index is a commonly accepted measure of market concentration used by antitrust enforcement agencies and scholars in the field. The HHI is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers” (NASI, 2015; pp: 14-16). Read more from here:
https://www.urban.org/sites/default/files/publication/50116/2000212-Addressing-Pricing-Power-in-Health-Care-Markets.pdf
For this exercise, you do not need to compute the HHI. However, you will need to understand completely what the HHI is, in order to correctly interpret the results of your analysis in this exercise and write your analysis report well. In your data, herf_cat of 0 = ‘High’, 1 = ‘Moderate’, 2 = ‘Low’
Present your method of analysis under the header “METHOD”. Under that header, present your step-by-step procedure or specify the R-script that you used for your data analysis.
Use the dataset from Exercise #1. Analyze the data to obtain results to complete Table 3 below. Use those results in Table 3 to answer the following questions:
Compare the following information between hospitals located in high, moderate, and low competitive markets? (Table 1)
What are the main significant differences between hospitals in different markets? (use the ANOVA test)
Use density curve graphs and compare hospital costs and revenues between the three markets.
(a) What is the impact of being in a high-competitive market on hospital revenues and costs? (b) Do you think being in a high-competitive market has a positive impact on net hospital benefits? (c) What about the number of Medicare and Medicaid discharges? (d) Do you think hospitals in a higher competitive market are more likely to accept Medicare and Medicaid patients? (e) What is the impact of any other variables? (f) Please report your findings. Then (g) discuss your findings in 2 paragraphs.
Note: to answer questions 4(a) through 4(f), please compute the “Medicare-discharge ratio” and the “Medicaid-discharge ratio” first, and then run two t-tests, comparing high vs. moderate and comparing high vs. low competitive markets. Create a simple table (Table 3A) to report your findings. Please in your analysis report, support your findings by creating simple box-plot graphs.
Table 3. Comparing hospital characteristics and market competitiveness, 2011 and 2012

 

 

Note: “The Herfindahl–Hirschman Index is a commonly accepted measure of market c

Note: “The Herfindahl–Hirschman Index is a commonly accepted measure of market concentration used by antitrust enforcement agencies and scholars in the field. The HHI is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers” (NASI, 2015; pp: 14-16). Read more from here:
https://www.urban.org/sites/default/files/publication/50116/2000212-Addressing-Pricing-Power-in-Health-Care-Markets.pdf
For this exercise, you do not need to compute the HHI. However, you will need to understand completely what the HHI is, in order to correctly interpret the results of your analysis in this exercise and write your analysis report well. In your data, herf_cat of 0 = ‘High’, 1 = ‘Moderate’, 2 = ‘Low’
Present your method of analysis under the header “METHOD”. Under that header, present your step-by-step procedure or specify the R-script that you used for your data analysis.
Use the dataset from Exercise #1. Analyze the data to obtain results to complete Table 3 below. Use those results in Table 3 to answer the following questions:
Compare the following information between hospitals located in high, moderate, and low competitive markets? (Table 1)
What are the main significant differences between hospitals in different markets? (use the ANOVA test)
Use density curve graphs and compare hospital costs and revenues between the three markets.
(a) What is the impact of being in a high-competitive market on hospital revenues and costs? (b) Do you think being in a high-competitive market has a positive impact on net hospital benefits? (c) What about the number of Medicare and Medicaid discharges? (d) Do you think hospitals in a higher competitive market are more likely to accept Medicare and Medicaid patients? (e) What is the impact of any other variables? (f) Please report your findings. Then (g) discuss your findings in 2 paragraphs.
Note: to answer questions 4(a) through 4(f), please compute the “Medicare-discharge ratio” and the “Medicaid-discharge ratio” first, and then run two t-tests, comparing high vs. moderate and comparing high vs. low competitive markets. Create a simple table (Table 3A) to report your findings. Please in your analysis report, support your findings by creating simple box-plot graphs.
Table 3. Comparing hospital characteristics and market competitiveness, 2011 and 2012

 

 

Note: “The Herfindahl–Hirschman Index is a commonly accepted measure of market c

Note: “The Herfindahl–Hirschman Index is a commonly accepted measure of market concentration used by antitrust enforcement agencies and scholars in the field. The HHI is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers” (NASI, 2015; pp: 14-16). Read more from here:
https://www.urban.org/sites/default/files/publication/50116/2000212-Addressing-Pricing-Power-in-Health-Care-Markets.pdf
For this exercise, you do not need to compute the HHI. However, you will need to understand completely what the HHI is, in order to correctly interpret the results of your analysis in this exercise and write your analysis report well. In your data, herf_cat of 0 = ‘High’, 1 = ‘Moderate’, 2 = ‘Low’
Present your method of analysis under the header “METHOD”. Under that header, present your step-by-step procedure or specify the R-script that you used for your data analysis.
Use the dataset from Exercise #1. Analyze the data to obtain results to complete Table 3 below. Use those results in Table 3 to answer the following questions:
Compare the following information between hospitals located in high, moderate, and low competitive markets? (Table 1)
What are the main significant differences between hospitals in different markets? (use the ANOVA test)
Use density curve graphs and compare hospital costs and revenues between the three markets.
(a) What is the impact of being in a high-competitive market on hospital revenues and costs? (b) Do you think being in a high-competitive market has a positive impact on net hospital benefits? (c) What about the number of Medicare and Medicaid discharges? (d) Do you think hospitals in a higher competitive market are more likely to accept Medicare and Medicaid patients? (e) What is the impact of any other variables? (f) Please report your findings. Then (g) discuss your findings in 2 paragraphs.
Note: to answer questions 4(a) through 4(f), please compute the “Medicare-discharge ratio” and the “Medicaid-discharge ratio” first, and then run two t-tests, comparing high vs. moderate and comparing high vs. low competitive markets. Create a simple table (Table 3A) to report your findings. Please in your analysis report, support your findings by creating simple box-plot graphs.
Table 3. Comparing hospital characteristics and market competitiveness, 2011 and 2012

 

 

Note: “The Herfindahl–Hirschman Index is a commonly accepted measure of market c

Note: “The Herfindahl–Hirschman Index is a commonly accepted measure of market concentration used by antitrust enforcement agencies and scholars in the field. The HHI is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers” (NASI, 2015; pp: 14-16). Read more from here:
https://www.urban.org/sites/default/files/publication/50116/2000212-Addressing-Pricing-Power-in-Health-Care-Markets.pdf
For this exercise, you do not need to compute the HHI. However, you will need to understand completely what the HHI is, in order to correctly interpret the results of your analysis in this exercise and write your analysis report well. In your data, herf_cat of 0 = ‘High’, 1 = ‘Moderate’, 2 = ‘Low’
Present your method of analysis under the header “METHOD”. Under that header, present your step-by-step procedure or specify the R-script that you used for your data analysis.
Use the dataset from Exercise #1. Analyze the data to obtain results to complete Table 3 below. Use those results in Table 3 to answer the following questions:
Compare the following information between hospitals located in high, moderate, and low competitive markets? (Table 1)
What are the main significant differences between hospitals in different markets? (use the ANOVA test)
Use density curve graphs and compare hospital costs and revenues between the three markets.
(a) What is the impact of being in a high-competitive market on hospital revenues and costs? (b) Do you think being in a high-competitive market has a positive impact on net hospital benefits? (c) What about the number of Medicare and Medicaid discharges? (d) Do you think hospitals in a higher competitive market are more likely to accept Medicare and Medicaid patients? (e) What is the impact of any other variables? (f) Please report your findings. Then (g) discuss your findings in 2 paragraphs.
Note: to answer questions 4(a) through 4(f), please compute the “Medicare-discharge ratio” and the “Medicaid-discharge ratio” first, and then run two t-tests, comparing high vs. moderate and comparing high vs. low competitive markets. Create a simple table (Table 3A) to report your findings. Please in your analysis report, support your findings by creating simple box-plot graphs.
Table 3. Comparing hospital characteristics and market competitiveness, 2011 and 2012