Classifying Data
Quantitative data can be classified according to the different levels or scales of measurement. The purpose of describing quantitative data according to levels of measurement is to determine the appropriate mathematical treatments that can be applied to data as well as the proper way of analyzing, interpreting, and stating the relationship among sets of quantitative data.
Levels of Measurement
There are four levels or scales of measurement in increasing order of precision: nominal, ordinal, interval, and ratio.
Nominal Level
In the nominal level, quantitative data are distinguished by the use of categories, names, or labels for the values of the data. In this level of measurement, data are not arranged in a particular order.
Nominal data obtained from different groups or categories are interpreted as either the same (equal) or not the same (not equal). Therefore, only a statement of comparison or contrast can be made. You can say or write “A is equal to B” or “A is the same as B.” You can say or write “A is not equal to B,” or “A is not the same as B.”
Ordinal Level
For measurements made in the ordinal level, the quantitative data or values are arranged in a certain order. Data in this level are distinguished not just in name but also in terms of rank or direction. For example, sugar solutions described ( in the nominal level ) as sweet, sweeter, and sweetest can be ranked according to increasing sweetness as 1, 2, and 3, respectively.
Statements of observation for values obtained under ordinal scales may overlap with the nominal scale as can be seen in questionnaires where the qualitative categories have corresponding numerical equivalents.
Example:
| Strongly agree: | 5 |
| Agree: | 4 |
| Slightly disagree: | 3 |
| Disagree: | 2 |
| Strongly disagree: | 1 |
