![]() 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. Especially in Probability Topics, the chapter on probability, it is more helpful to leave an answer as an unreduced fraction. ![]() It is not necessary to reduce most fractions in this course. Most answers will be rounded off in this manner. For example, the average of the three quiz scores four, six, and nine is 6.3, rounded off to the nearest tenth, because the data are whole numbers. If it becomes necessary to round off intermediate results, carry them to at least twice as many decimal places as the final answer. Do not round off any intermediate results, if possible. Answers and Rounding OffĪ simple way to round off answers is to carry your final answer one more decimal place than was present in the original data. 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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