### Levels of Measurement (Scale of Measure)

The levels of measurement (scale of measures) have been classified into four categories. It is important to understand these measurement levels since they play an important part in determining the arithmetic and different possible statistical tests carried on the data. The scale of measure is a classification that describes the nature of the information within the number assigned to a variable. In simple words, the level of measurement determines how data should be summarized and presented. It also indicates the type of statistical analysis that can be performed. The four-level of measurements are described below:

### 1) Nominal Level of Measurement (Nominal Scale)

At the nominal level of measurement, the numbers are used to classify the data (unordered group) into mutually exclusive categories. In other words, for the nominal level of measurement, observations of a qualitative variable are measured and recorded as labels or names.

### 2) Ordinal Level of Measurement (Ordinal Scale)

In the ordinal level of measurement, the numbers are used to classify the data (ordered group) into mutually exclusive categories. However, it does not allow for a relative degree of difference between them. In other words, for the ordinal level of measurement, observations of a qualitative variable are either ranked or rated on a relative scale and recorded as labels or names.

### 3) Interval Level of Measurement (Interval Scale)

For data recorded at the interval level of measurement, the interval or the distance between values is meaningful. The interval scale is based on a scale with a known unit of measurement.

### 4) Ratio Level of Measurement (Ratio Scale)

Data recorded at the ratio level of measurement are based on a scale with a known unit of measurement and a meaningful interpretation of zero on the scale. Almost all quantitative variables are recorded on the ratio level of measurement.

### Examples of levels of measurement

**Examples of Nominal Level of Measurement**

- Religion (Muslim, Hindu, Christian, Buddhist)
- Race (Hispanic, African, Asian)
- Language (Urdu, English, French, Punjabi, Arabic)
- Gender (Male, Female)
- Marital Status (Married, Single, Divorced)
- Number plates on Cars/ Models of Cars (Toyota, Mehran)
- Parts of Speech (Noun, Verb, Article, Pronoun)

**Examples of Ordinal Level of Measurement**

- Rankings (1st, 2nd, 3rd)
- Marks Grades (A, B, C, D)
- Evaluations such as High, Medium, and Low
- Educational level (Elementary School, High School, College, University)
- Movie Ratings (1 star, 2 stars, 3 stars, 4 stars, 5 stars)
- Pain Ratings (more, less, no)
- Cancer Stages (Stage 1, Stage 2, Stage 3)
- Hypertension Categories (Mild, Moderate, Severe)

**Examples of Interval Levels of Measurement**

- Temperature with Celsius scale/ Fahrenheit scale
- Level of happiness rated from 1 to 10
- Education (in years)
- Standardized tests of psychological, sociological, and educational discipline use interval scales.
- SAT scores

**Examples of Ratio Level of Measurement**

- Height
- Weight
- Age
- Length
- Volume
- Number of home computers
- Salary

In essence, levels of measurement act like a roadmap for statistical analysis. They guide us in selecting the most appropriate methods to extract valuable insights from the data under study. The level of measures are very important, because the help us in

**Choosing the right statistical tools:**Different levels of measurement are used for different statistical methods. For example, One can compute an measure of central tendency (such as mean and median) for data on income (which is interval level), but measure of central tendency (such as mean and median) cannot be computed for data on favorite color (which is nominal level, mode can be computed regarding measure of central tendency).**Drawing valid conclusions:**In case if wrong statistical test is used because of misunderstanding the level of measurement of the data, the conclusions might be misleading or even nonsensical. Therefore, levels of measurement help us to ensure that analysis reflects the actual characteristics of the data.**Making meaningful comparisons:**Levels of measurement also allow us to compare data points appropriately. For instance, one can say someone is 2 years older than another person (ordinal data), but one cannot say that their preference for chocolate ice cream is twice as strong (nominal data).

**For further details visit:** Levels of measurement