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Foundations of Measurement, The Building Blocks of Research
Unit 1, Foundations of Measurement, The Building Blocks of Research
In this unit you will start by examining the foundations of measurement – what we measure, why we measure, and how do we measure when conducting scientific research. Let us start by thinking about why measurements matter.
Slide 2: Why Do Measurements Matter?
So why do we measure things? In general, measurements make our lives easier and we measure things all the time. For example, when we go shopping, we identifying shoes that may be suitable based on our shoe size. When we cook, we often use a recipe with designated amounts of spices, oil, and the temperature used for baking. When we are building a new house, we need to know the dimensions of the rooms. Before you go outside in winter, we check the temperature to see how many layers of clothing to wear.
So why do we measure things? For two reasons: we want to duplicate results, such as in cooking from a recipe, and to accurately make comparisons, such as which shoes fit better.
These two reasons for measuring are also the same for conducting experiments. Because we need to make accurate comparisons and hopefully be able to duplicate our results. So let us turn our attention to what we measure and how we measure.
Slide 3: Levels of Measurement
In essence, in our research we measure two things: objects, and properties. Object are tangible items, like people, automobiles, buildings, or soft drinks. Properties are the characteristics of the object. So for an automobile, a property might be color. For people, properties would include things such as intelligence, attitude, or leadership ability.
Because it is difficult to measure properties themselves, we actually measure indicants of properties, and we do this through creating measurement scales that collect specific types of data. These scales provide different levels of measurement, and they can be discussed from weakest to strongest. Those levels are provided here. From the bottom, or the weakest, we start with a nominal level of measurement, which is simply categorical data. In other words the attributes are only named, not ordered in any way. Second is ordinal data, whereby the names of categories can also be ordered. Next is interval data, in which subtraction can be performed and the distances make sense between the data points. Finally, ratio data also has distances that make sense between data points, but in addition includes a ratio measurement. Let us look at some examples.
Slide 4: Example: University Student Data
So in this example of University Student Data, gender is a nominal variable, since “male” and “female” are just names of categories. There is no intrinsic ordering between them. A student’s level of standing in their class, however, for example, being a freshman or sophomore is ordinal. These are also names of categories, but unlike gender, they are rank-ordered. However, subtraction cannot be done and distances do not make sense.
Grade Point Average is an interval measurement; subtraction can be done and distances make sense. For example, the distance from 2.5 to 2.6 is the same distance as 3.7 to 3.8. However, ratios do not make sense: is 4.0 twice as high as 2.0? No. You could make a grading system where A, B, and C grades equal 4.0, 3.0, 2.0 or 6.0, 5.0, 4.0. Both would work equally well.
Finally, number of credit hours is a ratio measurement. A student who has completed 90 credit hours has twice as many as 45 credit hours, and 3 times as many as 30 credit hours.
Note that a measurement level contains an amount of information greater than or equal to the level below it. At lower levels of measurement, data analyses tend to be less sensitive or sophisticated. A statistical study should always aim for the highest levels of measurement possible.
Course Subject Matter Expert:
William McKibben, Adrienne Isakovic
Course Instructional Design: