What is a Nominal Variable? Definition, guide, characteristics, and examples
Correct Answer and Explanation
Definition of Nominal Variable
The purpose of a nominal variable, also known as a categorical variable, is to name, label, or otherwise classify a topic or item according to one or more of its characteristics. Nominal variables get their name, or “Nomen,” from the Latin word for “name.” Nominal variables only permit the categorization of data into several groups; they do not permit the ranking or ordering of the data.
Nominal variables include countries, religion, education level, fruit names, gender, and eye color. Keep in mind that even while some nominal variables (such as “unhappy,” “neutral,” and “happy”) may appear to have an order, they are still regarded as nominal rather than ordinal until there is a demonstrable difference between the categories.
The Purpose of Nominal Variable
Nominal variables play a crucial role in various fields due to their ability to categorize data into distinct groups. In market research, nominal variables help categorize consumer information, enabling researchers to analyze preferences for specific brands, advertising media types, and demographic data.
Similarly, in the realm of social sciences, including sociology, psychology, and political science, nominal variables prove instrumental in categorizing and studying data pertaining to social attitudes, beliefs, and behaviors.
Medical researchers utilize nominal variables to organize data related to the diagnosis and treatment of diseases, including categorizing types of medication, disease stages, and patient demographics.
In education research, nominal variables are used to classify data on student demographics, academic performance, and other determinants of student success. Utilizing nominal variables in these various fields enhances our ability to analyze and interpret complex datasets.
Nominal Variable Guide
Guide to understanding how to find nominal variable:
How do you find the normal variable?
1. Calculate the Categories and Variables
The initial step in analyzing nominal variable data involves identifying the categories and the variables that exist within these categories. For instance, if you’re looking at nominal data regarding the types of pets owned by office staff, the categories would be the different pet types, while the variables would represent the count of each pet type. Distinguishing between categories and variables is crucial as it enhances your understanding of the nominal data collected and aids in its application for different objectives.
2. Observe the Data Collected
Collecting nominal variable data through open-ended queries on surveys, questionnaires, or forms typically necessitates careful examination before categorizing the information. For instance, when analyzing responses from college students about their satisfaction with a course, you can review the answers and classify them into categories such as suggestions and comments for the professor’s consideration. This observation process reveals students’ sentiments and enables the professor to make appropriate adjustments to the course.
3. Determine the Data Purpose
Whether your nominal variable data is categorized or open-ended, it is crucial to determine the objective behind collecting and analyzing the data. Understanding the data’s purpose allows you to formulate guiding questions and seek answers through analysis, helping you discover new insights or validate hypotheses. It also aids in deciding the most appropriate way to measure the nominal data and how to apply it effectively in your professional pursuits.
4. Measure the Collected Data
After categorizing your nominal variable data, various statistical metrics can be utilized to uncover insights about the occurrence of specific variables within each data group category. Common measures for nominal data include frequencies, proportions, percentages, and modes.
5. Visualize the Collected Data
By employing nominal variable data metrics, pie charts or bar graphs can serve as effective tools for visualizing variable patterns and distributions, thereby facilitating more straightforward analysis. Typically, pie charts use percentages or proportions for data organization, while bar graphs represent each variable’s quantity with a bar on a numbered graph. By analyzing these completed charts and graphs, you can discern which categories of your nominal data exhibit higher or lower frequencies. This understanding can then be leveraged in your career to enhance your workplace or project.
6. Test the Data Collected
Nonparametric statistical tests are invaluable tools for hypothesis testing with your data. The chi-square goodness-of-fit test, in particular, enables you to evaluate whether your single-variable sample data is representative of the broader population. This is achieved by comparing your observations to what would be expected by mere chance. When dealing with two variables, the chi-square test of independence is typically the best option.
Nominal Variable Examples
Gender is a classic example of a nominal variable, commonly categorized into male and female groups.
Religion serves as another example of a nominal variable, often classified into distinct groups such as Muslims, Hindus, Christians, and so on.
Countries represent another type of nominal variable, as they can’t be ranked or ordered numerically. For example, India, the USA, and Australia are all examples of countries that fall under this category.
Type of Fruits
When classifying fruits by their type, nominal variables come into play. Examples of these fruit categories include apples, bananas, oranges, and strawberries, each representing a distinct category with no numerical ranking or order.
Level of Education
When it comes to categorizing education levels, nominal variables are typically employed. Examples of these categories encompass high school diplomas, bachelor’s degrees, master’s degrees, and PhDs, each representing a distinct classification with no inherent numerical order or ranking.
Color of the Eye
Eye color serves as an ideal example of a nominal variable, as it lacks any intrinsic numerical ranking or order. Different categories of eye color include shades such as blue, green, brown, and black, each distinct and separate from the others.