Interval measurement

There are two levels of quantitative measurement, depending on the nature of the zero point on which the numbers are based Interval measurement is based an artificial or arbitrary zero point invented by humans for our convenience. Measurement of temperature, for example, is based on an interval scale. Although a thermometer shows a zero point, it is artificial. Zero on either a Celsius or Fahrenheit thermometer does not mean the absence of heat. It can get a lot colder than zero as people in the far north know so well. Zero in either case is simply a point we have all agreed to use to measure what we call temperature.

Interval measurements are used widely in social research. The intelligence or IQ test is probably one of the best known of these. When psychologists developed these tests, they fixed the normal or average intelligence at 100 with scores below 100 indicating progressively less intelligence and those above 100 indicating progressively more intelligence. Using IQ scores, psychologists can estimate a person's chance of doing certain things. A score of 60 would not be a good basis for getting through college, while a score of 120 would be, but the two scores cannot be interpreted to mean that a person with a score of 120 is twice as intelligent as a person with a score of 60. The reason is that intelligence is not based on an absolute measure of zero intelligence. We can only say that the person with the higher score measured 60 points higher than the other person in terms of the way intelligence was measured.

But this statement is a further advance in providing information over that obtained with ordinal measurement. With interval measurement, a number can be used to describe differences, but the difference has to be described in terms of the intervals or numbers used in the measurement; hence the name interval for this level of measurement. Many measurements in the social sciences are at the interval level. Measurement of attitudes, values, preferences, prestige, and various abilities, for example, are frequently measured at the interval level.

Ratio measurement

In addition to the features of interval measurement, ratio measurement is based on the unique feature of starting from an absolute zero point. Many indicators we use have an absolute zero point. Examples include the size of any population, age, years of schooling, time spent somewhere, amount of money, and the frequency of events. For each of these and many other variables, we can compare the size of any measurement with some other measurement of the same variable and say how much they differ in absolute terms, such as one is twice or four times larger than the other. A student, for example, may be 20 years old and his or her father may be 40 years old. We can accurately say that the father is twice as old as the student. In this instance, we calculated the ratio of the ages (40 divided by 20) and came up with a ratio of 2. This is possible because we started with an absolute zero for measuring age, hence the name ratio for this level of measurement.

Level of measurement and research design

The level of measurement used can limit the way data are analyzed. Ratio or interval measurements can be analyzed by a number of powerful statistical tests. Analyses of nominal or ordinal data are far more limited. Researchers, therefore, seek to obtain measurement at the highest possible level for any variable. For indicators in nominal form, there is no choice. Gender, for example, can only be recorded as male or female. For many variables, researchers can choose a level of measurement. Data for schooling, for example, can be obtained at the ordinal or ratio level. If the question about schooling was stated as: "What is the highest level of schooling you completed" and the response categories were "none," "primary," "intermediate," "secondary," "beyond secondary," the resulting measurement would be at the ordinal level. Analysis of the data would be limited to what could be done with ordinal data. Instead of asking for the level of schooling completed, respondents could be asked for the last year completed, from 0 for no schooling to some number representing the total for years of schooling. This would produce ratio data, which could be analyzed with more precise and powerful statistical methods.

When a choice exists for levels of measurement and you believe you can get accurate responses, use interval or ratio over ordinal measurement. Doing so will give you more freedom in choosing methods of analyzing your data and allow you to use more powerful analysis techniques.