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Section 1.2 Measurement Scales

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In creating statistical measures, you might want to consider one of the following general types.

  • Nominal measures - In this case, data falls into mutually exclusive and exhaustive categories for which the numerical value is only used for identification purposes. For example, assigning Male = 1, Female = -1.

  • Ordinal measures - In this case, data consists of discrete numerical values which can be ranked from lowest to highest or vice versa. For example, your grades in a number of classes are used to compute your GPA--which is a single number.

  • Interval measures - In this case, data possesses an order and where the distance between data values is of significance. For example, heights and weights.

  • Ratio measures - In this case, data can be expressed as a position in some interval and where ratios between observations have meaning. For example, percentile rankings

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In the subsequent sections of this chapter, you will see that a number of different measures are available for most data sets. Determining which "correct" measure to use for describing any given data set will depend the actual situation surrounding the collection of the data.

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Below are a couple of interactive exercises that you can continue to attempt until you get the correct answer. The first one is very simple and for you to get accustomed to entering and checking answers. The second relates to the material in this section. Interactive elements such as these will be scattered throughout the text so that you can test your knowledge right away and at no cost for trying as many times as you like. To get the best result from these, try to avoid clicking on the provided solution unless you are hopelessly stuck.

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Checkpoint 1.2.1.
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Checkpoint 1.2.2. WebWork - Types of Data.