There are four common levels of measurement, also known as scale measurement, that we see
in statistics.
These include nominal, ordinal, interval, and ratio scales.
Each level of measurement providers the user access to different types of data, some stronger
than others.
It also has an impact on the types of statistical tests that can be performed, and subsequently
the conclusions that can be drawn as well.
The most basic and least powerful level of measurement is the nominal scale.
With the nominal scale question, respondents are only able to select a descriptor as a
response.
As a result, data obtained through these types of questions is restricted to being classified
and counted.
Examples of nominal scales include asking respondents to indicate their marital status.
Available options often include married, single, separated, divorced, and widowed.
Since these are qualitative variables we know that we're fairly limited in terms of the
types of tests that can be performed.
For example, we can't average the results.
So we can really only tally the number of responses into mutually exclusive categories
and provide overall results.
Another limitation of nominal scales is we can't determine the level of intensity between
responses.
For example, a question asking respondents whether they like or dislike a certain type
of music fails to establish the intensity to which respondents like or dislike a particular
genre genre of music.
A more powerful level of measurement is the ordinal scale.
The ordinal scale gives respondents the opportunity to express some relative level of intensity
between responses.
Responses obtained through an ordinal scale can be ranked and allow the user to determine
both the median and mode in their analysis.
Restaurants and other businesses commonly ask patrons to rate the level of service that
they received during a recent visit.
Available options may include excellent, good, average, poor and inferior.
So respondents can select from one of the available options, which allows restaurants
the opportunity to understand customer perceptions with regards to service quality.
But although users can determine how many respondents rated the service quality as superior
as opposed to just good, they can't determine how much better a superior rating is over
a good rating.
We know that it's obviously better, but we can't determine the level of difference between
the two ratings.
The third level of measurement is the interval scale.
The interval scale allows the user to determine preference or order in responses just like
the ordinal scale, but it also establishes an absolute difference between each available
option.
Questions that use the interval scale allow the user to determine not only the mode and
median, but also the means and the standard deviation of the responses.
An example of an interval scale is the commonly used Likert scale where respondents may assess
a variable along a five, seven, and even ten point scale.
Take our previous example related to restaurant service quality.
Using an interval scale, we could ask respondents to rank the service they received on a seven
point scale ranging from 1, which represents not satisfied at all, to a 7, which represents
extremely satisfied.
By using this type of scale, we can not only determine which responses receive a higher
rating than others, but also the level of magnitude between those responses.
The fourth and final level of measurement is the ratio scale.
The ratio scale adds to the interval scale an absolute zero.
The fact that zero is an available option, and is more importantly that it is meaningful,
means that the ratio between two numbers is significant.
Although an interval scale can provide the absolute difference between each scale point,
like from extremely satisfied to somewhat satisfied, the ratio scale provides an absolute
comparison between the responses themselves.
For example, a question asking respondents how many times they have exercised during
the past seven days is a ratio scale.
In addition to being able to provide a numerical value, respondents can also list zero as their
response.
This zero has a great deal of significance, as it indicates they did not exercise at all
during a seven day period.
Furthermore, we know that someone who exercised twice in a seven day period, was twice as
active as someone you exercised once.
So in addition to being able to determine the mean, median, mode, and standard deviation,
users can also make comparisons between responses.
Thank you for watching this video on the levels of measurement.
For questions please leave them in the comment box below and I'll do my best to get back
to them in a timely fashion.
If you want more from Alanis Business Academy, you can subscribe to our channel or go to
alanisbusinessacademy.com where you'll find additional content, quizzes, and more.
Thanks for watching.