Nominal level[ edit ] The nominal type differentiates between items or subjects based only on their names or meta- categories and other qualitative classifications they belong to; thus dichotomous data involves the construction of classifications as well as the classification of items.
Define and distinguish among nominal, ordinal, interval, and ratio scales Identify a scale type Discuss the type of scale used in psychological measurement Give examples of errors that can be made by failing to understand the proper use of measurement scales Types of Scales Before we can conduct a statistical analysis, we need to measure our dependent variable.
Exactly how the measurement is carried out depends on the type of variable involved in the analysis. Different types are measured differently. To measure the time taken to respond to a stimulus, you might use a stop watch. Stop watches are of no use, of course, when it comes to measuring someone's attitude towards a political candidate.
A rating scale is more appropriate in this case with labels like "very favorable," "somewhat favorable," etc. For a dependent variable such as "favorite color," you can simply note the color-word like "red" that the subject offers.
Although procedures for measurement differ in many ways, they can be classified using a few fundamental categories. In a given category, all of the procedures share some properties that are important for you to know about. The categories are called "scale types," or just "scales," and are described in this section.
Nominal scales When measuring using a nominal scale, one simply names or categorizes responses. Gender, handedness, favorite color, and religion are examples of variables measured on a nominal scale.
The essential point about nominal scales is that they do not imply any ordering among the responses. For example, when classifying people according to their favorite color, there is no sense in which green is placed "ahead of" blue.
Responses are merely categorized. Nominal scales embody the lowest level of measurement. Ordinal scales A researcher wishing to measure consumers' satisfaction with their microwave ovens might ask them to specify their feelings as either "very dissatisfied," "somewhat dissatisfied," "somewhat satisfied," or "very satisfied.
This is what distinguishes ordinal from nominal scales. Unlike nominal scales, ordinal scales allow comparisons of the degree to which two subjects possess the dependent variable.
For example, our satisfaction ordering makes it meaningful to assert that one person is more satisfied than another with their microwave ovens. Such an assertion reflects the first person's use of a verbal label that comes later in the list than the label chosen by the second person.
On the other hand, ordinal scales fail to capture important information that will be present in the other scales we examine. In particular, the difference between two levels of an ordinal scale cannot be assumed to be the same as the difference between two other levels.
In our satisfaction scale, for example, the difference between the responses "very dissatisfied" and "somewhat dissatisfied" is probably not equivalent to the difference between "somewhat dissatisfied" and "somewhat satisfied.
Statisticians express this point by saying that the differences between adjacent scale values do not necessarily represent equal intervals on the underlying scale giving rise to the measurements. In our case, the underlying scale is the true feeling of satisfaction, which we are trying to measure.
What if the researcher had measured satisfaction by asking consumers to indicate their level of satisfaction by choosing a number from one to four? Would the difference between the responses of one and two necessarily reflect the same difference in satisfaction as the difference between the responses two and three?
The answer is No. Changing the response format to numbers does not change the meaning of the scale. We still are in no position to assert that the mental step from 1 to 2 for example is the same as the mental step from 3 to 4.
Interval scales Interval scales are numerical scales in which intervals have the same interpretation throughout. As an example, consider the Fahrenheit scale of temperature. The difference between 30 degrees and 40 degrees represents the same temperature difference as the difference between 80 degrees and 90 degrees.
This is because each degree interval has the same physical meaning in terms of the kinetic energy of molecules. Interval scales are not perfect, however. In particular, they do not have a true zero point even if one of the scaled values happens to carry the name "zero.
Zero degrees Fahrenheit does not represent the complete absence of temperature the absence of any molecular kinetic energy.There are four levels of measurement: nominal, ordinal, interval and ratio.
These constitute a hierarchy where the lowest scale of measurement, nominal, has far fewer mathematical properties than those further up this hierarchy of scales. Another way to separate data is to classify it into four levels of measurement: nominal, ordinal, interval and ratio.
Different levels of measurement call for different statistical techniques. We will look at each of these levels of measurement. Determine which of the four levels of measurement (nominal, ordinal, interval, ratio) is most appropriate.
Voltage measurements of batteries: V, 3 V, V, 6 V, and V. Ratio. If a measure is nominal, then you know that you would never average the data values or do a t-test on the data. There are typically four levels of measurement that are defined: Nominal ; Ordinal ; Interval ; Ratio ; In nominal measurement the numerical values just "name" the attribute uniquely.
No ordering of the cases is implied. For example, jersey . What are the 4 levels of measurement? 1. Nominal 2. Ordinal 3. Interval 4. Ratio. Nominal measurement. Used to classify variables or events into categories.
The variable either does or does not have the characteristic. It is the lowest level and allows for the least amount of statistical manipulation.
Used to describe the pattern of. Levels of Measurement. Author(s) Dan Osherson and David M. Lane. Prerequisites. Variables Learning Objectives. Define and distinguish among nominal, ordinal, interval, and ratio .