Briefly describe the
four types of data and provide unique examples of each (not the examples from
the text). In your opinion, what is the most useful way to organize data for
What is the most useful way to present data for those with data management?
.low numeracy and literacy levels? Please explain your response
Briefly describe the four types of data and provide unique examples of each (not the examples
from the text). In your opinion, what is the most useful way to organize data for data
management? What is the most useful way to present data for those with low numeracy and
literacy levels? Please explain your response. You finding should be from reliable sources and
reference should be an APA format.
Running Head: DATA 1
DATA
Name
Course
University
DATA 2
Introduction
Data refers to unprocessed information, and its measurement scales are ordinal, nominal,
ratio, and interval. These measurement scales were invented by a psychologist researcher named
Stanley Stevens. They are used in recording and collecting both quantitative and qualitative data
for further analysis. Nominal scales only measure qualitative data, while interval and ratio scales
measure quantitative data. Ordinal scales can be used to collect both qualitative and quantitative
data (Zaman, 2016). This paper discusses the four types of data and identifies the best one to
organize data for data management and the best way to present data to audiences with low
literacy and numeracy levels.
Nominal data
These scales are used for labeling variables with qualitative data. Nominal scales are
mutually exclusive in labeling variables without any numerical significance. Some of the data
collected by these scales include an individual’s gender education level, hair color, area of
residence, temperature, and weather. Nominal scales are divided into several subtypes, with the
key ones being nominal with an order, nominal without order, and dichotomous scales.
Dichotomous scales are nominal scales with only two categories. People’s gender is
dichotomous since one can either be male or female. Nominal scales with order follow an
ascending or descending order such as temperature (cold, warm, hot). Nominal scales without
order include gender and area of residence (Dalati, 2018). For example, one can live in the USA,
Canada, China, Oman or another state which has no order.
Ordinal
Ordinal scales hinge on maintaining a certain order based on the value of the variables
when measuring datasets. The order of variables is significant is the most significant scale, but
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the differences between each variable are not known. These scales mostly record qualitative
variables such as the level of happiness, satisfaction, consumer experience, and discomfort.
Ordinal data can be used in further analysis using measures of central tendency. The ordinal
scales lack a definite mean but can be defined using its mode and median. For example, we can
measure an individual’s level of happiness through the ordinal scales of 1. Unhappy, 2. Happy,
and 3. Ecstatic. The numerical order of 1,2, and 3 makes the scale significant, but we don’t
define the differences between unhappy, happy, and ecstatic. Furthermore, the variables can’t be
analyzed without the 1,2 and 3 order.
Interval
These are numeric scales in which the order and exact differences between variable
values are known. Interval scales allow for further statistical analysis using the measures of
variation and central tendency. However, interval scales lack a true zero with both positive and
negative numbers having a meaning. The lack of a true zero doesn’t allow the researcher to
compute ratios of the data collected. Also, interval scales only allow us to add or subtract values
but don’t allow us to multiply or divide the values. An example of interval scales in measuring
temperature in degrees Celsius. Here the difference between the variables is known and the same
such as the difference between 60 and 70 degrees, which is the same as between 50 and 60
degrees. Also, temperature lacks a true zero since there can be no temperature and negative
temperatures such as -50 and -60 degrees have meaning (Dalati, 2018).
Ratio
These are the ultimate nirvana in data types since they combine all the other data
measurement scales. They tell us the value between variables, their order, and have a true zero
scale. The presence of a true zero makes it possible to apply both inferential and descriptive
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statistics to the data. The variables under this scale can be added, subtracted, multiplied, and
divided while still maintaining its meaning. Central tendency and variation can also be calculated
using mean, mode, median, standard deviation, range, variance, and coefficients. An example of
ratio data is BMI. A person’s BMI has a true zero and can be added, subtracted, and multiplied
while still maintaining its significance. It can be used in complex descriptive and inferential
analysis.
Best way to organize data
The most useful way of organizing data for data management is by using ratio scales. The
ratio scales combine both interval and ordinal scales, which make it possible for variables to be
subtracted, multiplied and divided while still maintaining its meaning. The central tendency and
variation of the data can also be calculated using mean, mode, median, standard deviation, range,
variance, and coefficients.
Best way to present data
The most suitable way of presenting data to audiences with low numerical and literacy
levels is through nominal scales. These scales are labels for the variables, which means there are
few quantitative and qualitative data on the scale. The audience only has to get one or two words
from the scale to get the gist of the data. Nominal scales are divided into several subtypes with
the key ones being nominal with an order, nominal without order, and dichotomous scales.
Dichotomous scales are nominal scales with only two categories, nominal scales with order
follow an ascending or descending order, while nominal scales without order lack general order.
These terms are easy for the audience to pick up on, even if they have low numeracy and literacy
skills (Zaman, 2016).
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Conclusion
Nominal scales are used in labeling a series of variables and values. They are divided into
nominal with an order, nominal without order, and dichotomous scales. Ordinal scales provide
information about the order of variables and can be used in surveys. Interval scales measure the
differences in values given a specific order; thus, they show us the order used and the difference
between variables. However, they lack a true zero which inhibits the use of inferential and
descriptive statistics. Ratio scales combine both interval and ordinal scales into one. The scales
can be added, subtracted, multiplied and divided. Both inferential and descriptive statistics can
be used in analyzing the scale since it has a true zero.
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References
Dalati, S. (2018). Measurement and Measurement Scales. In Modernizing the Academic
Teaching and Research Environment (pp. 79-96). Springer, Cham.
Zaman, T., & Raza, A. (2016). Data variables and measurement scales. Journal of Pakistan
Association of Dermatology, 15 (1), 60-65
