In this video, we are going to explain nominal, ordinal, interval and ratio levels of data.
Together these terms are known as the four levels of measurement in statistics. Here we
will unpack each of them in simple terms along with loads of examples so that you can approach
your analysis with confidence. Now if you are new to the oftentimes intimidating world of
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fast-track your research. You can find the link to those in the description. Right,
with that out of the way let us jump into it.
Okay so let us start with the basics. When you are collecting survey data or really
any kind of quantitative data you are going to land up with one or both of two types of data
categorical and numerical. These two types reflect different levels of measurement to be specific.
Let us unpack them. Categorical data as the name suggests means data that reflect characteristics
or categories no big surprise there. For example, categorical data could include variables such
as gender, hair colour, ethnicity or even coffee preference. Simply put utilising categorical data
is essentially a way of assigning number values to inherently qualitative data. For example, you
could assign number one for female number two for male and so on. Numerical data on the other hand
reflect data that are inherently numbers based and quantitative in nature. For example, age height or
weight are all things that are naturally measured as numbers. This contrasts against categorical
data which as I mentioned involves assigning numbers to qualitative characteristics. All right
so we have got categorical and numerical data as a foundation now so let us drill down further.
Within each of these main data types, there are two levels of measurement. Within categorical,
there is nominal and ordinal level data and with a numerical, there is interval and ratio level data.
But why do we need all these levels you ask? Well, the reason it is important to define types of data
is because the level of measurement for any set of data will directly impact which statistical
tests you can and cannot use on it. For example, you can only calculate the mean, the average,
for certain levels of data. If you try to run a statistical test on an unsuitable data set data
that is at the wrong level of measurement you will end up with meaningless results. So you
need to understand what you are dealing with before you can really work with the numbers.
All right so let us unpack each of the four levels of measurement along with some practical examples.
Nominal data, a categorical data type describe qualitative characteristics or groups with no
inherent numerical value, order or rank between categories. For example, gender ethnicity or
blood type, the brand of appliance someone owns, for example, a refrigerator, microwave
etcetera or someone's personal preferences for example their favourite meal or favourite colour
etcetera. In all of these examples, the data options are categorical and there is no ranking
or natural order. In other words, they all have the same intrinsic value. One colour,
for example, is not ranked above another. Simply put you can view nominal data as the
most basic level of measurement reflecting categories with no rank or order involved.
Nominal data-generating questions are often used in surveys to collect sample
demographic information such as gender and ethnicity. They are particularly useful for
comparing how responses vary among these basic dimensions. For example,
if you wanted to assess statistically significant differences in responses between men and women.
So nominal level data the most basic of the four can still be very useful depending on
your research aims. All right so now let us take a look at nominal's big brother ordinal.
Ordinal level data kick things up a notch. This type of data is the same as nominal data insofar
as it describes categories but unlike nominal data, there is also a meaningful order or rank
difference between the options. Some examples of ordinal level data include income levels,
for example, low-income, medium income or high-income. Levels of agreement,
for example, disagree, neutral or agree, levels of satisfaction, for example, poor,
average, good or excellent. As you can see in these examples all the options are still
categories but there is a natural ordering or ranking difference between the options.
You cannot numerically measure the differences between the options because they are categories
after all but you can order or logically rank them. Simply put ordinal level data reflect
categories with a natural rank order. Right, now that we have covered the two categorical
levels of measurement let us take a look at the two numerical levels of measurement.
Interval level data are numerical data in other words this level of measurement
involves naturally quantitative data, data that are by default measured in numbers. Specifically,
interval data have an order like ordinal data but in addition to this,
the spaces between measurement points are equal, unlike ordinal data. Sounds a bit fluffy and
conceptual well let us make it a little more tangible. Some examples of interval level data
include credit scores which range from 300 to 850. GMAT scores which range from 200 to 800,
the temperature in Fahrenheit, the Fahrenheit bit is important though and I will explain why
a little later. Notably in all of these examples the data points are naturally numerical they are
always expressed in numbers and the spaces between measurements are equal. For example,
the difference between a credit score of 610 and 620 is the same as the difference between 650 and
660, 10 points. However, if you look closely at all those examples you will notice that they all
have something else in common too. That something is that the zero point is arbitrary. For example,
a temperature of zero degrees Fahrenheit does not mean that there is no temperature or no heat
at all it just means that the temperature is 10 degrees less than 10. Similarly, you cannot
achieve a zero credit score or GMAT score well, let us hope not. Simply put interval is a level
of measurement that is numerical. You can measure the distance between points but that does not have
a meaningful zero point. The zero is arbitrary so interval data offer a more sophisticated level of
measurement than nominal and ordinal but it is still not perfect. So enter ratio data.
As you have probably guessed ratio is the most sophisticated level of measurement. Just like
interval level data ratio data are ordered or ranked and the numerical distance between points
is consistent and can be measured. So what makes ratio the king of measurement levels is that the
zero point reflects an absolute meaningful zero, unlike interval level data. For example, weight,
height or length you can see how a zero value is meaningful here. The temperature in Kelvin since
zero Kelvin means zero heat, a length of time, for example, seconds or minutes or hours. In all
of these examples, the zero point is absolute. Zero seconds quite literally means zero duration
similarly zero weight means weightless. In other words, it is not some arbitrary number this is
what makes ratio the most sophisticated level of measurement. With ratio data not only can you
meaningfully measure distances between data points i.e. add and subtract you can also meaningfully
multiply and divide. For example, twenty minutes is indeed twice as much time as ten minutes. You
could not do that with credit scores, interval level data as there is no such thing as a zero
credit score. So as you can imagine you can do a lot more in terms of statistical testing with
ratio level data that you can with lower levels of measurement. Okay so let us do a quick recap.
At the highest level, we have got categorical data which are more qualitative in nature and
numerical data which are quantitative by default. Within categorical, the two levels of measurement
are nominal, categories with no inherent rank and ordinal, categories with a natural order or
rank. Kicking things up a notch numerical includes the two measurement levels of interval and ratio.
Interval level data are measurable in that the spaces between measurements are equal but it does
not have a meaningful zero point whereas ratio level data the king of measurement levels does.
It is also worth mentioning that numerical level data, interval and ratio are continuous data types
as opposed to discrete. Also if you are planning to use SPSS it is worth noting that numerical data
are referred to as scale data and this covers both interval and ratio level data. If that all sounds
like gibberish, do not stress we cover all of it in more detail on the Grad Coach blog, link below.
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