Levels of Measurement
Nominal Level of Measurement
The nominal level of measurement is the lowest of the four ways to characterize data. Nominal means "in name only" and that should help to remember what this level is all about. Nominal data deals with names, categories, or labels.
Data at the nominal level is qualitative. Colors of eyes, yes or no responses to a survey, and favorite breakfast cereal all deal with the nominal level of measurement. Even some things with numbers associated with them, such as a number on the back of a football jersey, are nominal since it is used to "name" an individual player on the field.
Data at this level can't be ordered in a meaningful way, and it makes no sense to calculate things such as means and standard deviations.
Ordinal Level of Measurement
The next level is called the ordinal level of measurement. Data at this level can be ordered, but no differences between the data can be taken that are meaningful.
Here you should think of things like a list of the top ten cities to live. The data, here ten cities, are ranked from one to ten, but differences between the cities don't make much sense. There's no way from looking at just the rankings to know how much better life is in city number 1 than city number 2.
Another example of this are letter grades. You can order things so that A is higher than a B, but without any other information, there is no way of knowing how much better an A is from a B.
As with the nominal level, data at the ordinal level should not be used in calculations.
Interval Level of Measurement
The interval level of measurement deals with data that can be ordered, and in which differences between the data does make sense. Data at this level does not have a starting point.
The Fahrenheit and Celsius scales of temperatures are both examples of data at the interval level of measurement. You can talk about 30 degrees being 60 degrees less than 90 degrees, so differences do make sense. However 0 degrees
The nominal level of measurement is the lowest of the four ways to characterize data. Nominal means "in name only" and that should help to remember what this level is all about. Nominal data deals with names, categories, or labels.
Data at the nominal level is qualitative. Colors of eyes, yes or no responses to a survey, and favorite breakfast cereal all deal with the nominal level of measurement. Even some things with numbers associated with them, such as a number on the back of a football jersey, are nominal since it is used to "name" an individual player on the field.
Data at this level can't be ordered in a meaningful way, and it makes no sense to calculate things such as means and standard deviations.
Ordinal Level of Measurement
The next level is called the ordinal level of measurement. Data at this level can be ordered, but no differences between the data can be taken that are meaningful.
Here you should think of things like a list of the top ten cities to live. The data, here ten cities, are ranked from one to ten, but differences between the cities don't make much sense. There's no way from looking at just the rankings to know how much better life is in city number 1 than city number 2.
Another example of this are letter grades. You can order things so that A is higher than a B, but without any other information, there is no way of knowing how much better an A is from a B.
As with the nominal level, data at the ordinal level should not be used in calculations.
Interval Level of Measurement
The interval level of measurement deals with data that can be ordered, and in which differences between the data does make sense. Data at this level does not have a starting point.
The Fahrenheit and Celsius scales of temperatures are both examples of data at the interval level of measurement. You can talk about 30 degrees being 60 degrees less than 90 degrees, so differences do make sense. However 0 degrees