Levels of Measurement – nominal, ordinal, interval scale

Today, i will teach you the four levels of measurement – nominal, ordinal, interval scale

The first level of measurement is called the nominal level of measurement. A sample of college instructors classified according to subject taught (e.g., English, history, psychology, or mathematics) is an example of nominal-level measurement. Classifying survey subjects as male or female is another example of nominal-level measurement. No ranking or order can be placed on the data. Classifying residents according to zip codes is also an example of the nominal level of measurement. Even though numbers are assigned as zip codes, there is no meaningful order or ranking. Other examples of nominal-level data are political party (Democratic, Republican, Independent, etc.), religion (Christianity, Judaism, Islam, etc.), and marital status (single, married, divorced, widowed, separated).

The nominal level of measurement classifies data into mutually exclusive (nonover-lapping) categories in which no order or ranking can be imposed on the data.

The next level of measurement is called the ordinal level. Data measured at this level can be placed into categories, and these categories can be ordered, or ranked. For example, from student evaluations, guest speakers might be ranked as superior, average, or poor. Floats in a homecoming parade might be ranked as first place, second place, etc. Note that precise measurement of differences in the ordinal level of measurement does not exist. For instance, when people are classified according to their build (small, medium, or large), a large variation exists among the individuals in each class.

Other examples of ordinal data are letter grades (A, B, C, D, F).

The ordinal level of measurement classifies data into categories that can be ranked; however, precise differences between the ranks do not exist.

The third level of measurement is called the interval level. This level differs from the ordinal level in that precise differences do exist between units. For example, many standardized psychological tests yield values measured on an interval scale. IQ is an example of such a variable. There is a meaningful difference of 1 point between an IQ of 109 and an IQ of 110. Temperature is another example of interval measurement, since there is a meaningful difference of 1°F between each unit, such as 72 and 73°F. One property is lacking in the interval scale: There is no true zero. For example, IQ tests do not measure people who have no intelligence. For temperature, 0°F does not mean no heat at all.

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The interval level of measurement ranks data, and precise differences between units of measure do exist; however, there is no meaningful zero.



The final level of measurement is called the ratio level. Examples of ratio scales are those used to measure height, weight, area, and number of phone calls received. Ratio scales have differences between units (1 inch, 1 pound, etc.) and a true zero. In addition, the ratio scale contains a true ratio between values. For example, if one person can lift 200 pounds and another can lift 100 pounds, then the ratio between them is 2 to 1. Put another way, the first person can lift twice as much as the second person.



The ratio level of measurement possesses all the characteristics of interval measurement, and there exists a true zero. In addition, true ratios exist when the same variable is measured on two different members of the population.Page 9



There is not complete agreement among statisticians about the classification of data into one of the four categories. For example, some researchers classify IQ data as ratio data rather than interval. Also, data can be altered so that they fit into a different category. For instance, if the incomes of all professors of a college are classified into the three categories of low, average, and high, then a ratio variable becomes an ordinal variable. Table 1–2 gives some examples of each type of data. See Figure 1–2.TABLE 1–2Examples of Measurement Scales



Nominal-level data Ordinal-level data Interval-level data Ratio-level data
Zip codeGender (male, female) Eye color (blue, brown, green, hazel)Political affiliation Religious affiliation Major field (mathematics, computers, etc.)Nationality Grade (A, B, C, D, F)Judging (first place, second place, etc.)Rating scale (poor, good, excellent)Ranking of tennis players SAT scoreIQTemperature HeightWeightTimeSalaryAge
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