Types of data measurement scales are fundamental to understand well for researchers. Types of data measurement scales in statistics consist of nominal, ordinal, interval, and ratio scales. This article will discuss the different types of data measurement scales.

We can use the types of data measurement scale to determine the analytical tools used in research. The selection of analytical tools cannot be determined according to the researcher’s wishes but must follow statistical rules.

In determining the data processing method, we need to know in advance what analytical tools will be used. When we draft a research proposal, there is a research methods chapter. In the research methods chapter, one sub-chapter will convey the plan of analysis methods and data processing methods.

Determine the analytical tool to be used depends on the scale of measurement of the data in the study. Types of data measurement scales consisting of nominal and ordinal scales can be grouped into non-parametric statistics.

Meanwhile, for types of data measurement scales consisting of interval and ratio scales, we can group them in parametric statistics.

In general, researchers often see associative relationships between variables. The associative relationship consists of an effect test, correlation, and comparative test.

In selecting this associative test, the analytical tools will be different for variables with nominal, ordinal, interval, and ratio scales. Therefore, when choosing associative tests, we must understand the differences in the nominal, ordinal, interval, and ratio data scales.

**Nominal Scale Data**

Nominal scale data is the lowest data scale in Types of data measurement. On a nominal scale, data is measured by categorizing the data. There is no ranking or level of data in the categorization of data on a nominal scale.

We can say that the nominal scale is only used to distinguish the data. An example of a nominal data scale is gender. Gender consists of males and females.

Based on the categorization of this data, it is only to distinguish a person’s gender, whether male or female. There is no measure that men are better than women and vice versa.

Another example of a nominal data scale is the type of work. The types of work consist of government employees, private employees, entrepreneurs, farmers, etc. The size of this type of work only distinguishes between types of work. No job A measure is higher than job B, and vice versa.

**Ordinal Scale Data**

Ordinal scale data is a higher data measurement scale than nominal scale data. On the ordinal data scale, data categorization is not only to differentiate but also to rank.

An example of an ordinal data scale is the level of education. The education level consists of elementary school, junior high school, high school, and college.

Based on the difference in education level, there are differences and any levels. Respondents with elementary school education levels will be lower than high school, and vice versa.

In measuring the ordinal data scale, many researchers measure it using a Likert scale. The Likert scale is widely used to measure qualitative variables, for example, competence, behavior, performance, etc.

**Interval Scale Data**

The data scale interval is higher than the nominal and ordinal scales. The interval data scale has also been grouped in parametric statistics.

On the interval scale, the data measurement has obtained a numerical value. In contrast to the nominal and ordinal scales, the data are qualitative. A scoring technique is needed so that quantitative analysis can be carried out.

The numerical data obtained has a distance on the interval data scale, and this data does not yet have absolute zero. Interval data can be measured on a particular scale.

An example of an interval data scale is temperature. The temperature has a specific interval of 1-100 degrees Celsius. This temperature data does not have absolute zero.

**Ratio Scale Data**

The data scale ratio is at the top of the data measurement scale. The data scale ratio is almost the same as the data scale interval. The difference is that the data already has absolute zero in the scale ratio.

In addition to having absolute zero, another characteristic of the ratio data scale is that it has the same distance. Examples of ratio data scales are height, weight, income, consumption, etc.

Ratio data scale data has many data analysis options. The ratio data scale already has lower data scale characteristics (nominal, ordinal, interval).

**Selection of Analysis Tools**

The selection of analytical tools must be adjusted to the scale of data measurement. For example, variables with nominal and ordinal data scales, then use analytical tools included in the non-parametric statistical group.

If the variables analyzed use interval and ratio scales, then select an analytical tool that belongs to the parametric statistics group.

In parametric statistics, some assumptions must be met. The assumption test is more complex in the analytical tools included in parametric statistics than those using non-parametric statistics.

Today, we have learned about the types of data measurement scales. See you in the following article! I hope it will be beneficial for all of us.

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