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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 5-6 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:
Introduce the problem or topic being investigated. Including the relevant background information, for example;
Note: Your introduction should not be more than one or two paragraphs.
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 …?
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.
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.
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]):
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]):
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).
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
Full – Complete Label
Type of Data
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
Total facility beds set up and staffed
at the end of the reporting period
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).
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.
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.
Table 2. Descriptive analysis to compare % of BL in Medicare beneficiary, MD vs. VA- 2013
t Value (Pr<|t|)
Per of Lipid in MD
Per of Lipid in VA
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.
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.
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