Ordinal Variable

Table of Contents

Definition:

Types of Ordinal Variables

There are two types of ordinal variables:

Continuous Ordinal Variable

This type of variable has an infinite number of possible values, and the difference between each value is not uniform. Examples of continuous ordinal variables include measures of pain intensity, such as mild, moderate, and severe.

Discrete Ordinal Variable

This type of variable has a finite number of possible values, and the difference between each value is usually uniform. Examples of discrete ordinal variables include letter grades in a course, such as A, B, C, D, and F, or levels of educational attainment, such as high school diploma, associate’s degree, bachelor’s degree, and master’s degree.

Applications of Ordinal Variable

Ordinal variables are widely used in many fields, including:

Social sciences: In social sciences, ordinal variables are commonly used to represent the level of satisfaction, agreement, or disagreement with a particular concept, such as a political candidate or a product. For example, a survey might ask participants to rate their satisfaction with a product on a scale of 1 to 5, where 1 is very dissatisfied and 5 is very satisfied. The resulting data would be ordinal, with each rating having a clear order.
Market research: In market research, ordinal variables are often used to measure preferences or attitudes towards a product or service. For example, a survey might ask participants to rank their favorite brands of shoes from most favorite to least favorite. The resulting data would be ordinal, with each brand having a clear rank.
Education: In education, ordinal variables are commonly used to represent the level of achievement or proficiency of students. For example, a school might use an ordinal grading system, where grades are assigned as A, B, C, D, or F, where each grade represents a higher or lower level of achievement.
Health care: In health care, ordinal variables are used to represent the severity or progression of a disease. For example, cancer staging systems use ordinal variables to represent the extent to which cancer has spread in a patient’s body.
Psychology: In psychology, ordinal variables are commonly used to represent the level of agreement or disagreement with a particular statement or concept, such as a personality trait or belief. For example, a questionnaire might ask participants to rank their agreement with statements like “I enjoy socializing with others” on a scale of 1 to 5, where 1 is strongly disagree and 5 is strongly agree. The resulting data would be ordinal, with each rating having a clear order.

Examples of Ordinal Variable

Here are some examples of ordinal variables:

Educational attainment: This variable represents the level of education achieved by a person, such as high school diploma, associate’s degree, bachelor’s degree, master’s degree, and doctorate degree. The categories have a clear order, but the difference in education level between each category is not uniform.
Income: Income can be considered an ordinal variable if it is divided into categories such as low, medium, and high. While the difference between the categories is not uniform, they still have a clear order.
Customer satisfaction: This variable represents the level of satisfaction a customer has with a product or service, usually measured on a scale of 1 to 5 or 1 to 10. The scale has a clear order, but the difference between each rating is not necessarily uniform.
Pain intensity: Pain intensity can be measured using a scale such as mild, moderate, or severe, where each category has a clear order, but the difference in pain intensity between each category may not be uniform.
Likert scale: A Likert scale is a type of survey question that measures attitudes or opinions, often using a scale of agreement or disagreement. The categories have a clear order, but the difference between each category is not necessarily uniform.

When to use Ordinal Variable

Ordinal variables are appropriate when the variable being measured can be categorized into a set of ordered levels or ranks. Here are some scenarios when ordinal variables can be used:

When the data being measured is subjective or cannot be measured using a numerical scale, such as opinions or attitudes.
When there is a natural order or hierarchy in the data being measured, such as education levels or job ranks.
When the data being measured has a natural order or progression, such as the stages of a disease or severity of pain.
When the data being measured is categorical, but the categories are not equally spaced or do not have a natural numerical meaning.

Purpose of Ordinal Variable

The purpose of an ordinal variable is to measure a categorical variable that has a clear order or ranking, but where the difference between the categories may not be equal or may not have a natural numerical meaning.

Ordinal variables allow us to categorize and analyze data that would otherwise be difficult to quantify using numerical variables. They provide a way to rank or order data in a meaningful way that can be used for statistical analysis, such as determining central tendencies or making comparisons between groups.

Ordinal variables are commonly used in research and data analysis in fields such as social sciences, market research, education, health care, and psychology. They allow researchers to measure and analyze data that would otherwise be difficult to quantify and provide valuable insights into attitudes, opinions, preferences, and other important factors.

Characteristics of Ordinal Variable

Here are some key characteristics of ordinal variables:

Ordered: The categories of an ordinal variable have a clear order or hierarchy. The categories can be arranged in a sequence from the lowest to the highest or from the least severe to the most severe.
Non-Numeric: Ordinal variables are usually non-numeric, which means that the categories cannot be measured on a numerical scale. For example, the categories of a Likert scale used to measure attitudes or opinions may be “strongly agree,” “agree,” “disagree,” and “strongly disagree,” which do not have a numerical meaning.
Unequal intervals: The difference between categories of an ordinal variable is not necessarily equal or uniform. For example, the difference between “low,” “medium,” and “high” income levels is not the same as the difference between “low,” “medium,” and “high” levels of education.
Qualitative: Ordinal variables are a type of qualitative variable, which means they are concerned with qualities or attributes rather than quantities or numbers.
Limited range: Ordinal variables usually have a limited number of categories or levels. For example, a pain intensity scale may have only three levels: mild, moderate, and severe.

Advantages of Ordinal Variable

Some advantages of using ordinal variables:

Easy to collect and analyze: Ordinal variables are easy to collect through surveys, questionnaires, or other types of data collection methods. They are also relatively easy to analyze using descriptive statistics and non-parametric tests.
Allow for ranking and comparison: Ordinal variables allow for ranking and comparison of data that may not have a natural numerical meaning or where the difference between categories is not equal. This can provide valuable insights into attitudes, opinions, preferences, and other important factors.
Maintain confidentiality: Ordinal variables can be used to measure sensitive or personal data without revealing specific numerical values. For example, a Likert scale used to measure attitudes or opinions may provide valuable insights without revealing individual responses.
Useful in exploratory research: Ordinal variables can be useful in exploratory research, where the focus is on identifying patterns or relationships in the data rather than making specific predictions or generalizations.
Flexibility: Ordinal variables can be used in a wide range of research and data analysis applications, including social sciences, market research, education, health care, and psychology.

Limitation of Ordinal Variable

Here are some limitations of using ordinal variables:

Limited statistical analysis: While ordinal variables are useful for descriptive analysis and non-parametric tests, they are limited in their ability to perform more complex statistical analyses, such as correlation coefficients or ANOVA. This is because the difference between categories may not be uniform or have a natural numerical meaning.
Arbitrary scale: The scale used to measure ordinal variables is often arbitrary and subject to interpretation. This can lead to inconsistencies in the way data is collected and analyzed, making it difficult to compare results across studies.
Risk of loss of information: Ordinal variables are less informative than numerical variables, as they only provide information about the ranking or order of data and not the magnitude of the differences between categories. This can lead to a loss of information and limit the insights that can be gained from the data.
Limited categories: Ordinal variables usually have a limited number of categories or levels, which may not fully capture the complexity of the data being measured. This can lead to oversimplification of the data and limit the accuracy of the analysis.
Subjectivity: Ordinal variables may be subject to bias or subjectivity in the way they are interpreted or categorized. This can lead to inconsistencies in the data and limit the reliability of the results.