Different Scale of Measurements – nominal, ordinal, interval and ratio scales

The level or scale of measurement depends on the properties of the data. There are four
types of scales are used in business research. These include nominal, ordinal, interval and ratio scales. Variables can be classified as discrete or continuous.

What is a Nominal Scale?

A nominal scale is the simplest type of scale. The numbers or letters assigned to objects serve as labels for identification or classification. For example, names, player list, gender are categorical variables; and one can put the level ‘M’ for Male and ‘F’ for Female, or ‘1’ for male and ‘2’ for female, or ‘1’ for female and ‘2’ for male. Other examples include marital status, religion, race, colour and employment status, and so forth.

What is an Ordinal Scale?

When a nominal scale follows an order then it becomes an ordinal scale. In other words, an ordinal scale arranges objects or categorical variables according to an ordered relationship. Ordinal data are often referred to as ranked data because the data are ordered from highest to lowest, or biggest to smallest.  So, the ranking of nominal scales is an essential prior criterion for ordinal scales. A typical ordinal scale in business research asks respondents to rate career opportunities and company brands as ‘excellent, ‘good’, ‘fair’ or ‘poor’. Other examples would be (i) result of examination: first, second, third classes and fail; (ii) quality of products.

What is an Interval Scale?

The interval scale indicates the distance or difference in units between two events. In other words, such scales not only indicate order but also measure the order or distance in units of equal intervals. It is important to note that the location of the zero points is arbitrary. To take an example, in the price index, the number of the base year is set to be usually 100. Another classic example of an interval scale is the temperature where the initial point is always arbitrary.

What is a Ratio Scale?

Ratio scales have absolute rather than relative quantities. In other words, if an interval scale has an absolute zero then it can be classified as a ratio scale. The absolute zero represents a point on the scale where there is an absence of the given attribute. Ratio data have all four properties of measurement—identity, magnitude, equal unit size, and absolute zero. For examples, age, money and weights are ratio scales because they possess absolute zero and interval properties.

What is Discrete variables and Continuous variables?

Variables can be classified in terms of whether they are discrete or continuous in nature. Based on the level or scale of measurement the variables are classified as nominal, ordinal, interval and ratio scales.

Discrete variables

Discrete variables usually consist of whole number units or categories. They are made up of chunks or units that are detached and distinct from one another. A change in value occurs a whole unit at a time, and decimals do not make sense with discrete scales.

Most nominal and ordinal data are discrete. For example, gender, designation, and Location are discrete scales.

Some interval or ratio data can be discrete. For example, the number of Covid infections reported as a whole number (discrete data), yet it is also ratio data (you can have a true zero and form ratios).

Continuous variables

Continuous variables usually fall along a continuum and allow for fractional amounts. The term continuous means that it “continues” between the whole-number units.

Examples of continuous variables are Age (22.7 years), Height (64.5 inches), and Weight (113.25 pounds). Most interval and ratio data are continuous in nature.

Discrete and continuous data are more important in research design and data presentation.

How to Read and Understand a Journal Article / Research Article?

People new to research often find it hard to read and understand an article. Research articles have a very specific format. They usually have five main sections: Abstract, Introduction, Method, Results, and Discussion. Let’s briefly discuss each section below.


The Abstract is a brief description of the entire research paper. It should not exceed 250 words and ranges between 150- 250 words. The Abstract describes the problem under investigation and the purpose of the study; the participants / Sample and research methodology; the findings, including statistical significance levels; and the conclusions and implications or applications of the study.


The Introduction has three basic components: an introduction to the problem under study; a review of relevant previous research, which cites works that are important and significant for the study; and the purpose and rationale for the study.


The Method section describes exactly how the study was conducted, in sufficient detail that a person who reads the Method section could replicate the study. The Method section is generally divided into subsections. Although the subsections vary across papers, the most common subsections are Sample, Participants, Materials or Apparatus, and Procedure. The Participants subsection includes a description of the participants and how they were obtained. The Materials subsection usually describes any testing materials that were used, such as a particular test or inventory or a type of problem that participants were asked to solve. An Apparatus subsection describes any specific equipment that was used. The Procedure subsection summarizes each step in the execution of the research, including the groups used in the study, instructions given to the participants, the experimental manipulation, and specific control features in the design.


The Results section summarizes the data collected and the type of statistic used to analyze the data. In addition, the results of the statistical tests used are reported with respect to the variables measured and/or manipulated. This section should include a description of the results only, not an explanation of the results. In addition, the results are often depicted in tables and graphs or figures.


The results are evaluated and interpreted in the Discussion section. Typically, this section begins with a restatement of the predictions of the study and tells whether or not the predictions were supported. It also typically includes a discussion of the relationship between the results and past research and theories. Highlight the implications for future research are presented.