The differences between the data have meaning.
The data can be put in order from lowest to highest: 20, 68, 80, 92. For example, four multiple choice statistics final exam scores are 80, 68, 20 and 92 (out of a possible 100 points).
Ratio scale data is like interval scale data, but it has a 0 point and ratios can be calculated. There is no meaning to the ratio of 80 to 20 (or four to one).ĭata that is measured using the ratio scale takes care of the ratio problem and gives you the most information. 80° C is not four times as hot as 20° C (nor is 80° F four times as hot as 20° F). Interval level data can be used in calculations, but one type of comparison cannot be done.
Temperatures like -10° F and -15° C exist and are colder than 0. But 0 degrees does not because, in both scales, 0 is not the absolute lowest temperature. In both temperature measurements, 40° is equal to 100° minus 60°. Temperature scales like Celsius (C) and Fahrenheit (F) are measured by using the interval scale. The differences between interval scale data can be measured though the data does not have a starting point. Like the nominal scale data, ordinal scale data cannot be used in calculations.ĭata that is measured using the interval scale is similar to ordinal level data because it has a definite ordering but there is a difference between data. But the differences between two pieces of data cannot be measured. The top five national parks in the United States can be ranked from one to five but we cannot measure differences between the data.Īnother example of using the ordinal scale is a cruise survey where the responses to questions about the cruise are “excellent,” “good,” “satisfactory,” and “unsatisfactory.” These responses are ordered from the most desired response to the least desired. An example of ordinal scale data is a list of the top five national parks in the United States. Nominal scale data cannot be used in calculations.ĭata that is measured using an ordinal scale is similar to nominal scale data but there is a big difference. The data are the names of the companies that make smartphones, but there is no agreed upon order of these brands, even though people may have personal preferences. Smartphone companies are another example of nominal scale data. Putting pizza first and sushi second is not meaningful. For example, trying to classify people according to their favorite food does not make any sense. Categories, colors, names, labels and favorite foods along with yes or no responses are examples of nominal level data. They are (from lowest to highest level):ĭata that is measured using a nominal scale is qualitative (categorical). Data can be classified into four levels of measurement. Not every statistical operation can be used with every set of data. Correct statistical procedures depend on a researcher being familiar with levels of measurement. The way a set of data is measured is called its level of measurement. However, when calculating the frequency, you may need to round your answers so that they are as precise as possible. Once you have a set of data, you will need to organize it so that you can analyze how frequently each datum occurs in the set.
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