University of Maryland University College
HMGT 400 Research and Data Analysis in Health- FINAL EXAM
Dataset: HMGTFINALEXAM.csv (To be provided when you complete Assignment #3)
Required program: EXCEL
Author, Hossein Zare, PhD
Citation: Zare, H. (2019). HMGT 400 Research and Data Analysis in Health Care. FINAL EXAM. UMUC.EDU
Question #1 (15 credits):
The FINAL EXAM dataset provides some information about hospitals in 2011 and 2012. Analyze the FINAL EXAM dataset. You may calculate the “Hospital Beds per Population (Per Capita)” variable by dividing “total_hospital_beds by tot_population. Use the analysis results to complete Table 1 below.
Table 1. Descriptive statistics about hospitals in 2011 & 2012
2011 | 2012 | t Value (Pr<|t|) | |||||
N | Mean | St. Dev | N | Mean | St. Dev | ||
Hospital Characteristics | |||||||
1. Hospital beds | |||||||
2. Number of paid Employee | |||||||
3. Number of non-paid Employee | |||||||
4. Internes and Residents | |||||||
5. System Membership | |||||||
6. Total hospital cost | |||||||
7. Total hospital revenues | |||||||
8. Hospital net benefit | |||||||
9. Available Medicare days | |||||||
10. Available Medicaid days | |||||||
11. Total Hospital Discharge | |||||||
12. Medicare discharge | |||||||
13. Medicaid discharge | |||||||
Socio-Economic Variables | |||||||
14. Hospital Beds per Population (Per Capita) | |||||||
15. Percent of population in poverty | |||||||
16. Percent of Female population in poverty | |||||||
17.Percent of Male population in poverty | |||||||
18. Median Household Income |
Then please answer the following questions.
Question #2 (15 credits):
In the dataset, create a new variable called hospital net benefits. Do this by subtracting hospital costs from hospital revenues.
Analyze the dataset and then complete Table 2. In the last column report the T-test results, to compare hospital characteristics and the nationwide socioeconomic variables for for-profit and non-profit hospitals.
Table 2. Descriptive statistics between teaching and non-teaching hospitals, 2011 & 2012
For Profit | Non-For-Profit | p-value | |||||
N | Mean | St. Dev | N | Mean | St. Dev | ||
Hospital Characteristics | |||||||
1. Hospital beds | |||||||
2. Number of paid Employee | |||||||
3. Number of non-paid Employee | |||||||
4. Internes and Residents | |||||||
5. System Membership | |||||||
6. Total hospital cost | |||||||
7. Total hospital revenues | |||||||
8. Hospital net benefit | |||||||
9. Available Medicare days | |||||||
10. Available Medicaid days | |||||||
11. Total Hospital Discharge | |||||||
12. Medicare discharge | |||||||
13. Medicaid discharge | |||||||
Socio-Economic Variables | |||||||
14. Hospital Beds per Population (Per Capita) | |||||||
15. Percent of population in poverty | |||||||
16. Percent of Female population in poverty | |||||||
17. Percent of Male population in poverty | |||||||
18. Median Household Income |
Then answer the following questions:
Question #3 (15 credits):
The dataset provides the variable herf_ins called the Herfindahl–Hirschman Index which measures market concentration for the health insurance market. Please note that unlike the class exercise in which you used herf_cat, which measured market concentration for the hospital market, in this assignment you are using herf_ins which measures market concentration for the health insurance market.
Analyze the data to complete Table 3 (below):
Table 3. Comparing hospital characteristics and market, 2011 and 2012
High Competitive Market | Moderate Competitive Market | Low Competitive
Market |
ANOVA/Chi-Sq (results) | |||||||
N | Mean | STD | N | Mean | STD | N | Mean | STD | ||
Hospital Characteristics | ||||||||||
1. Hospital beds | ||||||||||
2. Number of paid Employee | ||||||||||
3. Number of non-paid Employee | ||||||||||
4. Internes and Residents | ||||||||||
5. System Membership | ||||||||||
6. Total hospital cost | ||||||||||
7. Total hospital revenues | ||||||||||
8. Hospital net benefit | ||||||||||
9. Available Medicare days | ||||||||||
10. Available Medicaid days | ||||||||||
11. Total Hospital Discharge | ||||||||||
12. Medicare discharge-ratio | ||||||||||
13. Medicaid discharge-ratio | ||||||||||
Socio-Economic Variables | ||||||||||
14. Hospital Beds per Population (Per Capita) | ||||||||||
15. Median Household Income |
Then answer the following questions:
(Note: to answer the last question, please compute Medicare-discharge ratios and Medicaid-discharge ratios first and then run two t-Tests (high competitive vs. moderate competitive, and high vs. low competitive market). Please support your findings with a box-plot).
Question #4 (Credits 20)- Excel version
If you have chosen to work with Excel, please run the models and complete the following tables.
Regression Model 1:
Analyze the data by running a linear regression model as depicted in Table 4 below.
Table 4 – Regression Model 1
Coefficient | ST. ERR | T Stat | P-values | Lower 95% | Upper 95% | |
Intercept/Constant | ||||||
Total Hospital beds | ||||||
Teaching Hospital Dummy | ||||||
N = | ||||||
R Square = |
Regression Model 2:
Analyze the data by running a linear regression model as depicted in Table 5 below.
Table 5 – Regression Model 2
Coefficient | ST. ERR | T Stat | P-values | Lower 95% | Upper 95% | |
Intercept/Constant | ||||||
Total Hospital beds | ||||||
Teaching Hospital Dummy | ||||||
N = | ||||||
R Square = |
Regression Model 3:
Analyze the data by running a linear regression model as depicted in Table 5 below.
Table 6 – Regression Model 3
Coefficient | ST. ERR | T Stat | P-values | Lower 95% | Upper 95% | |
Intercept/Constant | ||||||
Total Hospital beds | ||||||
Teaching Hosp. Dummy | ||||||
Medicare discharge ratio | ||||||
Medicaid discharge ratio | ||||||
N = | ||||||
R Square = |
Regression Model 4:
Analyze the data by running a linear regression model as depicted in Table 7 below.
Table 7 – Regression Model 4
Coefficient | ST. ERR | T Stat | P-values | Lower 95% | Upper 95% | |
Intercept/Constant | ||||||
Total Hospital beds | ||||||
Non-Teaching Hosp. Dummy | ||||||
Medicare discharge ratio | ||||||
Medicaid discharge ratio | ||||||
N = | ||||||
R Square = |
Question #5 (Credits 20)- Excel version
If you have chosen to work with Excel, please run three models and complete the following tables.
Logistic Regression Models
If you have chosen to work with RStudio, please run the following model and complete the following tables.
Logistic Model 1:
Analyze the data by running a logistic regression model as depicted in Table 8 below. Use “being a member of a hospital network” (system_member) as the dependent variable.
Table 8 – Logistic Model 1
Coefficient | ST. ERR | P-Value | Exp (coeff) | Exp (z SE) | Exp (Std. Coeff.) | |
Intercept/Constant | ||||||
Total Hospital costs | ||||||
N = | ||||||
R Square = |
Logistic Model 2:
Analyze the data by running a logistic regression model as depicted in Table 9 below.
Table 9 – Logistic Model 2
Coefficient | ST. ERR | P-Value | Exp (coeff) | Exp (z SE) | Exp (Std. Coeff.) | |
Intercept/Constant | ||||||
Total Hospital Costs | ||||||
Total Hospital Revenue | ||||||
N = | ||||||
R Square = |
Logistic Model 3:
Analyze the data by running a logistic regression model as depicted in Table 10 below.
Table 10 – Logistic Regression Model 3
Coefficient | ST. ERR | P-Value | Exp (coeff) | Exp (z SE) | Exp (Std. Coeff.) | |
Intercept/Constant | ||||||
Total Hospital Costs | ||||||
Total Hospital Revenue | ||||||
Medicare discharge ratio | ||||||
Medicaid discharge ratio | ||||||
N = | ||||||
R Square = |
Question 6 (15 credits)
1. Please offer a research question for the study using human subject research.
2. Explain the difference between the research process involving human subjects and the research process not involving human subjects.
3. Discuss ethical implications surrounding human subject research studies.
4. Explain the governance of the human subject research studies over the data and the process.
5. Provide examples of the consequences for not meeting IRB (Institutional Review Board) protocol requirements.