# Scales that you can use for data analysis

Once you have collected and arranged the data, the same must be measured against some chosen scales. The need for such scales arises so as to get uniformity between different types of information. This also enables measurement and analysis, even for subjective information. As research scholars, you have to be familiar with various scales that you can use.

Nominal

This is the most basic form of measurement, wherein the variables assume absolute identity. For instance, if the participants are being categorized on the basis of gender, there can only be two cases, male or female. So, the values are mutually exclusive. Such demographic details are represented on a nominal scale by assigning values like 0 and 1. These values do not mean anything empirically, but are only meant to assist analysis. This method of assigning values can also be seen as labeling of information.

Ordinal

The term ordinal is probably obtained from the word order, because with the use of the ordinal scale, you can assign values to denote certain order or rank. For instance, if you are measuring the performance of students in a class, then they would be symbolized by assigning values as per the grades that they attain. The values here actually hold some meaning, unlike nominal scale. Such a scale is useful for studying descriptive information, like less hungry, more satisfied, etc.

Interval

This is the scale where the exact interval between values assigned to subjects or information is known. For instance, the gap between each minute in a stopwatch is 60 seconds. This is a fact that can’t be changed. Measurement of central tendency of such values is easy and provides accurate results.

Ratio

The most complex of all scales, ratios are used extensively in quantitative analysis, as they enable application of a wide range of statistical tests. Here, the ratio between consecutive values can be measured and is meaningful. For instance, in a weighing scale, 10kg is always double of 5kg.

The scale that you apply will depend upon the type of methodology and data being considered. Make use of all these scales suitably for getting accurate results.