Task 1: Ethics In Research and Healthcare Statistics
WGU C813 TASK 1: HEALTHCARE STATISTICS
AND RESEARCH, |PASSED ON FIRST
ATTEMPT |LATEST UPDATE WITH COMPLETE
SOLUTION
AT WESTERN GOVERNORS UNIVERSITY
C813 Healthcare Statistics and Research
DATE……
Task 1: Ethics In Research and Healthcare Statistics
B.S. Health Information Management
Instructor: Ashley Killingsworth
A1. Discrete and Continuous Data Differences
There are different data types; however, this paper will discuss the difference between
, Task 1: Ethics In Research and Healthcare Statistics
Discrete and Continuous data. According to (Koch, 2014), discrete data is expressed as a whole
number or integer. An integer is a number without a fraction or decimal subdivision. The way
discrete data is obtained is by counting. Continuous data is measurable, not restricted to whole
numbers, and includes decimals and fractions. Even though the data can be measured in decimals
or fractions, that is not how they are reported. The number is reported to the nearest whole
number. An example of this is height. A patient is considered 5 feet 6 ¾, the height would be
reported as 5 feet 6 inches.
A2. Scales of Measurement
There are four different measurement scales: nominal, ordinal, interval, and ratio.
Nominal data are qualitative data. Nominal data is qualitative data in which numbers are
assigned to elements within a category. Arithmetic operations should not be performed on
nominal data (Koch, 2014). An example of this is race #1. Native American, #2. Hispanic, #3
White, #4 Asian, #5 African American, #6 Pacific Islander. Ordinal data are values or
observations that are ordered or ranked. Ordinal data represents a position in a series. This
number is scored and grouped into low, middle, and high rankings (Koch, 2014). An example of
ordinal data is surveys, intelligence quotient, and the number one leading cause of death is heart
disease.
Interval data is similar to ordinal data except that the values or observations are
measured on an evenly distributed scale, which does not begin at zero, and the values are equal.
An example of interval data is time, which comes down to the minutes. One hour equals sixty
minutes, and two hours equals 120 minutes—fun fact when patient access representatives use
2
WGU C813 TASK 1: HEALTHCARE STATISTICS
AND RESEARCH, |PASSED ON FIRST
ATTEMPT |LATEST UPDATE WITH COMPLETE
SOLUTION
AT WESTERN GOVERNORS UNIVERSITY
C813 Healthcare Statistics and Research
DATE……
Task 1: Ethics In Research and Healthcare Statistics
B.S. Health Information Management
Instructor: Ashley Killingsworth
A1. Discrete and Continuous Data Differences
There are different data types; however, this paper will discuss the difference between
, Task 1: Ethics In Research and Healthcare Statistics
Discrete and Continuous data. According to (Koch, 2014), discrete data is expressed as a whole
number or integer. An integer is a number without a fraction or decimal subdivision. The way
discrete data is obtained is by counting. Continuous data is measurable, not restricted to whole
numbers, and includes decimals and fractions. Even though the data can be measured in decimals
or fractions, that is not how they are reported. The number is reported to the nearest whole
number. An example of this is height. A patient is considered 5 feet 6 ¾, the height would be
reported as 5 feet 6 inches.
A2. Scales of Measurement
There are four different measurement scales: nominal, ordinal, interval, and ratio.
Nominal data are qualitative data. Nominal data is qualitative data in which numbers are
assigned to elements within a category. Arithmetic operations should not be performed on
nominal data (Koch, 2014). An example of this is race #1. Native American, #2. Hispanic, #3
White, #4 Asian, #5 African American, #6 Pacific Islander. Ordinal data are values or
observations that are ordered or ranked. Ordinal data represents a position in a series. This
number is scored and grouped into low, middle, and high rankings (Koch, 2014). An example of
ordinal data is surveys, intelligence quotient, and the number one leading cause of death is heart
disease.
Interval data is similar to ordinal data except that the values or observations are
measured on an evenly distributed scale, which does not begin at zero, and the values are equal.
An example of interval data is time, which comes down to the minutes. One hour equals sixty
minutes, and two hours equals 120 minutes—fun fact when patient access representatives use
2
