Statistical data, whether qualitative or quantitative, is generated or obtained through measurement or observational processes called levels of measurement. Each measurement or observation made on any object or variable can be attributed to one of the four scales of measurement.
4 scales of measurement or levels of measurement are;
Each character has unique characteristics and implications for the statistical procedures used.
Nominal Level of Measurement
All qualitative measurements are nominal, regardless of whether the categories are designated by names (red, white, male) or numerals (June 20, Room no. 10, bank account no. student ID, etc.).
In the nominal level of measurement, the categories differ from one another only in names. In other words, one category of a characteristic is not necessarily higher or lower, greater or smaller than the other category.
Sex (male and female) and religion (Muslim, Hindu, Christian, Jewish) are examples of nominal measurements. The categories are homogeneous and mutually exclusive, with no assumption about the ordered relationships between the categories.
To work with such non-numerical data with statistical tools, we need to impose a numerical scheme on the data.
For example, with gender, 0 might be assigned to males and 1 to females. With religion, the scheme might be to use 1 for Muslim, 2 for Hindu, 3 for Christian, etc.
In each of these cases, the numerical data have been artificially created, but none of the numbers have any numerical meaning. We call such data nominal data because they are numerical in name only.
In the measurement scale, the nominal level of measurement is the lowest or weakest level of measurement, and the resulting data are nominal.
Ordinal Level of Measurement
When there is an ordered relationship between the categories, we achieve what we refer to as the ordinal level of measurement.
Unlike the nominal level, here we have the typical relations “higher,” “more than,” “less difficult,” “more prejudiced,” “more feminine,” “less favorable,” “more profitable,” “less costly,” and the like.
More specifically, the relationships are expressed in terms of the algebra of inequalities: a is less than b (a < b) or a is greater than b (a > b).
examples of the ordinal level of measurement are;
- university degree (for example, Master’s, Bachelor’s, etc.),
- job title (for example, manager, deputy manager, accountant),
- socio-economic status (high, medium-low),
- academic performance (outstanding, very good, good, poor),
- monthly frequency of visits of a physician in a clinic (frequently, occasionally, rarely, never),
- level of agreement on the issue of imposing VAT on food items (strongly agree, agree, disagree, strongly disagree).
Note that an ordinal scale is distinguished from a nominal scale by the additional property of order among the categories included on the scale.
You can rate, for example, the level of agreement on the issue of VAT on a 4-point scale of 1 (strongly agree) to 4 (strongly disagree).
Still, such ratings have no real significance in the sense of usual arithmetic operations, but they represent a way to introduce an ordering relation.
The chief properties of the ordinal level of measurement are
- The categories are distinct, mutually exclusive, and exhaustive;
- The categories are possible be ranked or ordered;
- The distance or differences from one category to another is not necessarily constant.
Interval Level of Measurement
The interval level of measurement includes all the properties of the nominal and the ordinal level but an additional property that the difference (interval) between values is known and of constant size.
A thermometer, for example, measures temperature in degrees, which are the same size at any point in the scale.
The difference between 20° C and 21° C is the same as the difference between 12° C and 13° C. The temperatures 12° C, 13° C, 20° C and 21° C can be ranked, and the differences between the temperatures can easily be determined.
It is also important to note that 0 is an arbitrary point on the scale. It does not necessarily represent the absence of heat, just that it is cold. 0 degrees Celsius is 32 degrees on the Fahrenheit scale.
Therefore, we cannot say that a temperature of 64°F is twice as warm as a temperature of 32°F.
Note that the Celsius equivalence of 32°F (the freezing point of water) is 0°C, while the equivalence of 64°F is 17.8°C. 17.8°C is not twice as warm as 0°C.
The Gregorian calendar is another example of an interval scale: 0 is used to separate BC and AD. It does not mean that there was no time before 0. We refer to the years before 0 as BC and to those after 0 as AD.
Incidentally, 0 is a hypothetical date in the Gregorian calendar because there never was a year 0. The other examples are IQ and calendar time (6 AM, 10 AM, etc.). The interval levels of data have the following properties:
- The data classification is mutually exclusive and exhaustive;
- The data can be meaningfully ranked or ordered;
- The difference between one data classification to the next is known and constant.
Ratio Level of Measurement
In practice, almost all quantitative data fall under the ratio level of measurement. It has all the ordering and distance properties of the interval level.
Also, a ‘zero-point’ can be meaningfully designated; thus, the ratio between two numbers is also meaningful.
Examples of ratio levels of measurement include wages, stock prices, sales values, age, weight, and height.
Thus it makes sense to speak of 0 sales when there are no sales in the store. It is also quite meaningful to say a 4-feet tallboy is twice as tall as a 2-feet tallboy. A family with 6 members is twice as large as a family with 3 members.
In comparing the four levels of measurement, we can conclude that an ordinal measure is a nominal measure and has the ordinality property, an interval measure is an ordinal measure and a unit of measurement.
The ratio measure has all the properties of nominal, ordinal, and interval measures and has an absolute or true zero.
The characteristic properties of the four levels of measurement and the way of deciding whether a particular level of measurement qualifies as nominal, ordinal, interval, or ratio; the following flowchart may be used:
- Do the numbers express a quantitative value or order?
If no, then -> nominal level. If yes, then ask: - Do the differences between the numbers represent equal units of measurement (e.g., 3-2=4-3)?
If no, then -> ordinal level. If yes, then ask: - Does the measurement have an absolute zero?
If no, then -> interval level. If yes, then -> ratio level.
The accompanying table attempts to compare the various levels of measurement.
Measurement Scales and Their Comparison
| Scale | Characteristics | Examples |
|---|---|---|
| Nominal | Unordered category | Sex: Male, female Religion: Muslim, Hindu, Buddhist, etc Color: Red, blue, green, etc. |
| Ordinal | Ordered category | Farm size: Large, Medium, Small The severity of disease: Severe, Moderate, Mild, Normal |
| Interval | Ordered, equal intervals and arbitrary zero point | Temperature: 10°C, 45°C, 65°C, etc. SAT score: 650, 810, 789 etc. IQ test score: 40, 47, 76 etc. |
| Ratio | Ordered, equal intervals and true zero point | Investment: $5000, $10,000 etc. Contraceptive use rate: 50%,.55% etc. Age: 5 years, 12 years, 65 years, etc. |
What are the levels of measurement in statistics?
The four levels of measurement in statistics are Nominal, Ordinal, Interval, and Ratio.
How is a nominal scale defined, and what is its primary characteristic?
A nominal scale is the 1st level of measurement scale where numbers serve as “tags” or “labels” to classify or identify objects. It deals with non-numeric variables or numbers that don’t have any value. The primary characteristic of a nominal scale is that the numbers don’t define the object characteristics; they are used primarily for identification.
How is the nominal level of measurement characterized?
The nominal level of measurement involves unordered categories. The categories differ only in names and are not ranked. Examples include sex (male, female) and religion (Muslim, Hindu, Buddhist).
Why is the interval scale preferred in statistics?
The interval scale is preferred in statistics because it helps assign numerical values to arbitrary assessments, such as feelings or calendar types, making it versatile for various types of data analysis.
What is the key feature of the interval level of measurement?
The interval level of measurement has ordered categories with known and constant-sized intervals between them. However, it does not have a true zero point.
What kind of operations can be performed on interval scale data?
The interval scale allows for the calculation of the mean and median of the variables. It also allows for subtraction of values between variables to understand the difference.
What distinguishes the ordinal level of measurement from the nominal level?
The ordinal level of measurement has an ordered relationship between the categories, unlike the nominal level. It can express relations like “higher,” “more than,” or “less than.”
How is the ratio level of measurement different from the other levels?
The ratio level of measurement includes all properties of the other levels and additionally has a true zero point, making ratios between numbers meaningful.
Can you provide an example of data that falls under the ratio level of measurement?
Examples of ratio levels of measurement include wages, stock prices, age, weight, and height.
How can one determine the appropriate level of measurement for a set of data?
To determine the level of measurement, one can ask a series of questions: Do the numbers express a quantitative value or order? Do the differences between numbers represent equal units? Does the measurement have an absolute zero? Based on the answers, one can classify the data as nominal, ordinal, interval, or ratio.
What is the primary difference between ordinal and interval scales?
The ordinal scale reports the ordering and ranking of data without establishing the degree of variation between them. In contrast, the interval scale is quantitative and measures the exact difference between two variables in a meaningful way.
What is unique about the ratio scale in comparison to other scales?
The ratio scale is the only scale that has a true zero point, meaning it doesn’t have negative numbers. This allows for unique statistical analyses, including addition, subtraction, multiplication, and division of variables.
Can you provide an example of a question that uses the ordinal scale?
Yes, an example of an ordinal scale question is: “How often do you exercise?” with options like “Very often,” “Often,” “Not often,” and “Not at all.”