In statistics we know the scale of data measurement : nominal data, ordinal data, interval, and ratio. In general it can be said that the purpose of an observation is to obtain about the condition of an object in various circumstances. Among the various measurements for objects, which are numbers, rank, length, volume, time, weight, and physical-chemical measurements.

There are four measurement scales in Statistics:

## Nominal Data

Firstly nominal Scale is the simplest measurement scale. It groups objects into several groups, which have similar features will be in one group. Nominal scale measurement results not to sort but can distinguish. Common examples commonly used are gender variables. In this case the measurement results cannot be sorted (women are higher than men, or vice versa). Examples of nominal scale applications: trademarks, types of shops, sales territories.

## Ordinal Data

Secondly ordinal Data describes the position or rank but do not measure the distance between ranks. Size on an ordinal scale does not give an absolute value to an object, but only a relative sequence. Furthermore, the distance between rank 1 and 2 does not have to be the same as the distance between rank 2 and 3. On an ordinal scale, the rank does not have a unit of measurement. For example: social status (high, low, medium), measurement results that classify people into high, low or medium social status. In this case, we can know the level, but the difference between social statuses (high-low, low-medium, high-medium etc.) is not necessarily the same. Example application: preference level, management position, career path.

## Interval Data

Thirdly interval scale gives the numeric characteristic to objects that have nominal and ordinal scales. It has the same distance in the order of the object. Interval scale is the level of this scale above the ordinal and nominal scale. Therefore, an important feature of this scale: we can add, subtract, duplicate, and share without affecting the relative distance of the scores.

Furthermore, this scale does not have an absolute zero. We cannot interpret in full the value of a certain ratio. In interval measurement switches, the ratio between two arbitrary intervals does not depend on the value of zero and the unit of measurement, For example, measurement of temperature on a Celsius scale. If a water bath is full of 0 degrees C, 50 degrees C, and 100 degrees C, the difference between 0-50 and 50-100 degrees C is the same. We cannot say that water at 100 degrees C is twice as hot as water 50 degrees C. Example application: Employee performance appraisal (on a scale from 0-100)

## Ratio Data

Further ratio scale has all the properties of the interval scale plus one trait to give information about the absolute value of the object. It aims to distinguish, sort, certain distance, and we can compare (the most complete, including all of the scales above). Example: If we want to compare the weight of two people. A weight 40 kg and B 80 kg. We can know that A is twice as heavy as A. Because the value of the numerical variable weight expresses the ratio with zero as its default. Other examples: Age, value for money, height, etc.

The measurement scale above ranks from the low level (1 nominal scale) to the highest level (4 ratio scale). Measurement Scale with a higher level of measurement can change to a lower level, but the opposite.

Knowing this measurement scale will benefit a study. In the analysis, it needs measurement and what analysis tools are fit to answer the research objectives.

### Ayat HIdayat Huang

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