Statistical Concepts on the DRG Summary for Medicare Inpatient Prospective Payment
Using the link below, access and download the DRG Summary for Medicare Inpatient Prospective Payment Hospitals (data) provided by the U.S. Centers for Medicare & Medicaid Services:
https://www(dot)cms(dot)gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Downloads/Inpatient_Data_2015_XLSX.zip
This is a two-part assignment.
Part 1:
In 1 page, complete the following:
Define and explain the 4 main types of scales variables can be measured on: (1) nominal; (2) ordinal, (3) interval, and (4) ratio scales.
Using the chart below or creating your own, identify each of the 12 columns on the spreadsheet as nominal, ordinal, or interval/ratio data.
Part 2:
In 1-2 pages, complete the following:
Define and explain the differences among mean, median, mode, and standard deviation.
Using the excel data, sort Column A, DRG Definition, to only show “001 - HEART TRANSPLANT OR IMPLANT OF HEART ASSIST SYSTEM W MCC” records.
For this part of the assignment, you will only use data from Column I, Total Discharges, and Column L, Average Medicare Payments.
Using excel, calculate the mean, mode, median, and standard deviation for Column I, Total Discharges.
Using excel, calculate the mean for Column L, Average Medicare Payments.
Present your findings in your paper in the chart below, or one that you have created.
Explain what each calculation means in terms of the data presented. (Note: The data is DRG Summary data for Medicare Inpatient Prospective Payment Hospitals for 2015. You will need to explicitly explain the calculation findings based on the dependent variable, heart transplants and/or implant of heart assistance system. An example of such an explanation would consist of the following, “I found a mean of 37 for DRG 007 – Lung Transplant. This data shows that on average in 2015, 22 Medicare patients were discharged for lung transplants.”
Statistical Concepts
Student’s Name
Institutional Affiliation
Course Code: Course Name
Instructor’s Name
March 19, 2022
Statistical Concepts
Part 1
In statistics, data can be categorized into four types of scales of measure namely nominal, ordinal, interval, and ratio scale (Dhall, 2019). These levels of measure can be further grouped into categorical and continuous data (Watt and Collins, 2019). The nominal and ordinal scales are categorical data types, while interval and ratio scales are continuous (Dhall, 2019). Understanding these scales of measurement is essential in establishing the right methods to be used in analyzing particular data (Anderson et al., 2016). In this section, each of these scales of measure will be defined and their respective examples provided (Watt and Collins, 2019).
A nominal scale is a categorical type of measure that does not have any natural order (Albers, 2017). Nominal scale data is the data that consists of levels that cannot be ordered or simply labels or names (Watt and Collins, 2019). An example of a variable with a nominal scale is gender, which mainly has two levels: male and female (Anderson et al., 2016). On the other hand, an ordinal scale is a categorical type of measure that has a natural order (Dhall, 2019). An ordinary scale data is made up of levels that follow a particular meaningful order (Watt and Collins, 2019). An example of an ordinal scale variable is education level. A person in the secondary level has a lower education level compared to the one in college. Also, secondary level always comes before college. This implies that there is an order that has to be followed to understand the variable education level. Likert scale data are always ordinal (Anderson et al., 2016).
The interval scale is one of the measures in statistics that is continuous (Albers, 2017). An interval scale is a type of measure that has meaningful order and difference between values (Dhall, 2019). A good example of a variable with an interval scale is temperature. Higher values of temperature mean more heat and the difference between, say, 20 degrees and 40 degrees is similar to 30 degrees and 50 degrees, which is 20 degrees difference. The Interval scale does not have a true zero (Anderson et al., 2016). On the other side, the ratio scale is a continuous type of measure that has an order, meaningful difference between values, and has a true zero (Anderson et al., 2016). An example of a ratio scale is weight.
Column
Title
Scale
A
DRG Definition
Nominal
B
Provider ID
Nominal
C
Provider Name
Nominal
D
Provider Street Address
Nominal
E
Provider City
Nominal
F
Provider State
Nominal
G
Provider Zip Code
Nominal
H
HHR Description
Nominal
I
Total Discharges
Ratio
J
Average Cover Charges
Ratio
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