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Measurement of Health Outcomes

1. Select a health outcome of interest to your area of practice that is measured at the
   nominal level.
   Medical technologies transforming healthcare
   a. Briefly define the variable
   Like other sectors, the healthcare industry is today influenced by several new
   technologies, such as robotics, big data, electronic health records (EHRs), artificial intelligence
   (AI), health wearables, virtual reality, augmented reality, and telemedicine. For example, EHRs
   technology-driven solutions reduce medication errors and improve safety and quality of care (Le
   et al., 2018).
   b. Specify how the measurement is obtained
   The nominal variable can be collected by asking respondents either open-ended or close-
   ended questions. For example, in the open-ended format, respondents can be asked to name the
   most critical medical technologies transforming the healthcare practice today (Boslaugh, 2012).
   The nominal variable is the question: “Which are the most critical technologies transforming
   clinical practice today?”. In the close-ended format, respondents can be asked to identify two
   technologies an organization can add to improve its service delivery in a list of five items,
   including telemedicine, EHRs, CPOEs, AI, and wearables.
   c. Described how the measurement should be recorded
   The measurement can be recorded in a questionnaire by identifying the correct choice
   without much description or explanation. It is mostly about identifying, naming, and listing.
   d. Specify what descriptive statistics should be used to describe the variable

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Mode is the preferred measure of central tendency (descriptive statistics) for describing
the nominal variable. Mode is the value that appears more frequently in a dataset. A researcher
can tell the most preferred medical technology by examining the most frequent medical
technology. Counts and percentages can also rank responses from the most reappearing to the
least frequent (Boslaugh, 2012).
e. Select an appropriate graphical display to depict the variable
A bar graph is the most appropriate graphic technique to display the types of medical
technologies. A bar graph can be easily plotted using percentages. Pie charts can also be used in
place of bar graphs (Boslaugh, 2012).

2. Select a health outcome of interest to your area of practice that is measured at the
   ordinal level.
   Educational level
   a. Briefly define the variable
   Ordinal data classifies variables that have a natural rank or order. Educational level is an
   essential socioeconomic predictor of health outcomes, including healthcare access, quality of
   life, and mortality. Evidence suggests that people with higher educational attainment, usually
   Bachelor’s degree and above, perform better in life compared to those with a high school
   qualification and below (Zajacova & Lawrence, 2018). Therefore, the various ordinal variables
   (ranks) from educational level can include high school and below, Bachelor’s degree, Master’s
   degree, and Doctorate.
   b. Specify how the measurement is obtained
   The variable can be obtained by asking clients close-ended questions through interviews
   or questionnaires to identify their educational qualifications from a list containing the five

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options above. Close-ended questions give clients a broad range of possibilities and restrict their
responses to the specific alternatives stipulated.
c. Described how the measurement should be recorded
Interviews can be recorded using digital data recording tools like cameras and other
modern techniques. This data is then decrypted or decoded into a paper-based format. On the
other hand, questionnaire responses can be handwritten on paper or filled through online surveys
and other online platforms like email. As qualitative data, the answers are recorded in narrative
form (Boslaugh, 2012).
d. Specify what descriptive statistics should be used to describe the variable
Frequency distribution tables can present and describe the educational levels of
respondents and other related information. The frequency distribution table can be expressed in
numbers or percentages. Also, median and mode can be used to evaluate the variable’s central
tendency. Finally, the range can also be applied to indicate variability (Boslaugh, 2012).
e. Select an appropriate graphical display to depict the variable
A bar graph is the most appropriate graphical format for displaying educational
attainment in a specific population. The various academic categories can be plotted on the x-axis
and the frequencies (numbers or percentages) on the y-axis. Unlike ‘nominal’ bar graphs, it is
necessary to consider classes’ order when displaying ordinal bar graphs. Pie charts can also be
used in place of bar graphs.

3. Select a health outcome of interest to your area of practice measured at the interval
   or ratio level.
   Income
   a. Briefly define the variable

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Income is a typical example of a health outcome often measured at the ratio level.
Interval and ratio level data are similar except that the latter has a defined “zero” point. Besides
educational level, income is another fundamental socioeconomic variable that determines health
status and quality of life. Evidence has also linked income to other healthcare disparities,
including healthcare access, morbidity, and mortality (Chokshi, 2018).
b. Specify how the measurement is obtained
As a quantitative variable, a population’s income can be collected through face-to-face
interviews, telephone interviews, surveys, longitudinal studies, controlled observations, and
experiments. Secondary data can also be gathered from government databases, including national
census polls (Boslaugh, 2012).
c. Described how the measurement should be recorded
Unlike nominal and ordinal variables that are qualitative, income is a quantitative
variable that requires investigators to record numerical data. For example, the real median
household income for Black, Hispanic, and White households in 2017 were $40,258, $50,486,
and $68,145, respectively (U.S. Census Bureau, 2018).
d. Specify what descriptive statistics should be used to describe the variable
All descriptive statistics can describe the income levels of a population (Boslaugh, 2012).
This includes measures of frequency (frequency, percent, and count), measures of central
tendencies (mode, median, and mean), measures of variability (standard deviation, range, and
variance), measures of position (quartile and percentile ranks), and normality measures (kurtosis
and skewness).
e. Select an appropriate graphical display to depict the variable

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A histogram is the most appropriate visual display for income measurement. Histograms
are bar graphs containing equal class intervals. Besides histograms, pie charts, bar graphs, and
line graphs can also display income data.

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References
Boslaugh, S. (2012). Statistics in a nutshell. O’Reilly Media, Inc.
Chokshi, D. A. (2018). Income, poverty, and health inequality. The Journal of the American
Medical Association, 319(13), 1312-1313. doi:10.1001/jama.2018.2521
Le, D., et al. (2018). Emerging technologies for health and medicine: Virtual reality, augmented
reality, artificial intelligence, internet of things, robotics, industry 4.0. John Wiley &
Sons.
U.S. Census Bureau. (2018). Figure 1. Real median household income by race and Hispanic
origin: 1967 to 2017.
https://www.census.gov/content/dam/Census/library/visualizations/2018/demo/p60-
263/figure1.pdf
Zajacova, A., & Lawrence, E. M. (2018). The relationship between education and health.
Reducing disparities through a contextual approach. Annual Review of Public Health, 39,
273-289. doi: 10.1146/annurev-publhealth-031816-044628