Nominal scales
Nominal scales are used for naming and categorizing data in a variable
- usually in the form of identifying groups into which people fall. Membership in such groups may occur either naturally (as in the sex or nationality groupings just discussed) or artificially (as in a study that assigns students to different experimental and control groups). On a particular nominal scale, each observation usually falls into only one category. The next observation may fall into a different category, but it, too, will fall only into one such category. Examples of variables that group people naturally include sex, nationality, native language, social and economic status, level of study in a language and a specific like or dislike (e.g., whether the subjects like studying grammar rules). Artificially occurring variables might include membership in an experimental or control group and membership in a particular class or language pro-gram. So the essence of the nominal scale is that it names categories into which people fall, naturally or artificially. One source of confusion with this type of scale is that it is variously labeled a discrete, discontinuous, categorical, or even dichotomous scale (in the special case of only two categories). In this book, I will always call this type of scale nominal.


Ordinal scales
Unlike a nominal scale, an ordinal scale is used to order, or rank, data.
For instance, if you were interested in ranking your students from best to worst on the basis of their final examination scores (with 1 representing the best; 2, the next best; and 30 being the worst), you would be dealing with an ordinal scale. In our field, numerous rankings may be of concern, including ranking students in a class, ranking teachers in a center, or even ranking grammatical structures in a language (e.g., in French tenses, subjonctif is more difficult than the passé composé, which is more difficult than the present). In an ordinal scale, then, each point on the scale is ranked as "more than" and "less than" the other points on the scale. The points are then lined up and numbered first, second, third, and so on.

Interval scales
Interval scales also represent the ordering of things. In addition, they reflect the interval, or distance, between points in the ranking. When you look at the final examination scores in one of your courses, you are dealing with an interval scale. For instance, if Jim scored 90 out of 100, Jack scored 80, and Jill scored 78, you could rank these three students first, second, and third; that would be an ordinal scale. But the scores themselves give you more information than that. They also tell you the interval, or distance, between the performances on the examination: Jim scored 10 points higher than Jack, but Jack only scored 2 points higher than Jill. Thus, an interval scale gives more information than does an ordinal scale.
An interval scale gives you the ordering and the distances between points on that ranking. Examples of interval scales include most of the variables measured with tests like language placement, language apti-tude, language proficiency, and so on. But some interval scales are measured in different ways, such as age, number of years of schooling, and years of language study.
It is important to note that an interval scale assumes that the intervals between points are equal. Hence, on a 100-point test, the distance between the scores 12 and 14 (2 points) is assumed to be equal to the distance between 98 and 100 (also 2 points). The problem is that some items on a language test may be much more difficult (particularly those that make a difference on high scores like 98 and 100) than are others, so the distances between intervals do not seem equal. Nevertheless, this assumption is one with which most researchers can live - at least until knowledge in the field of language teaching becomes exact enough to provide ratio scales.


Ratio scales
The discussion of ratio scales will be short because such scales are not generally applied to the behavioral sciences, for two reasons: (1) a ratio scale has a zero value and (2) it can be said that points on the scale are precise multiples of other points on the scale. For example, you can have zero electricity in your house. But if you have a 50-watt bulb burning and then you turn on an additional 100-watt bulb, you can say that you are now using three times as much electricity. Can you, however, say that a person knows no (zero) Spanish? I think not. Even a person who has never studied Spanish will bring certain lexical, phonological, and syntactic information from the native language to bear on the task.



Read the text above and create a table. 

Columns:
Name of scale
Property
Operations
Characteristic
Example