When conducting surveys, every researcher aims to get sincere responses that reveal answers to common questions. Researches utilize various scales of measurement comprising the nominal, ordinal, interval, and ratio scale. In light of this, we shall consider the best ordinal data examples to conduct surveys and carry out social experiments.
In essence, this article aims to introduce you to the concept of ordinal data, as we provide some examples of ordinal data worth considering. We have also highlighted some tips to hone your skills with this scale of measurement for various analysis methods. Read on to learn more.
What Is Ordinal Data?
Ordinal data is a form of data with categorical variables in natural rank order. These variables comprise ordinal values with an unknown degree of difference between each category. In essence, the order in which variables are listed is more important than the distance between categories.
For example, if you have a categorical variable like seniority level in an organization, the ordinal scale could be junior level, mid-level, and senior level. While the categories follow an ascending level of arrangement, the number of years it takes to move from a junior level to a mid-level does not have to be the same when moving from mid to senior level.
Where Is Ordinal Data Used?
- Social scientific research
- Aptitude and personality testing
- Behavioral science experiments
- Intelligence Quotient (IQ) testing
- Market research analysis
Why Is Ordinal Data Important?
Ordinal data is important because it eases the categorization and comparison of a response variable. It allows for identifying key characteristics of data after applying various statistical methods to examine responses. Generally, it plays a significant role in conducting surveys, filling questionnaires, and carrying out polls. It aids researchers in studying qualitative variables.
Real-World Examples of Ordinal Data
The following are common examples of ordinal data to give you a proper understanding of the application of this level of measurement. These samples apply in descriptive statistics and cover a wide range of discrete variables. Consider our list of ordinal data examples below.
- The Likert Scale
- Education Level
- Customer Level of Satisfaction
- Language Ability
- Performance of a Government Policy
- Time of the Day
- Levels of Agreement
- Military Ranks
- High School Class Ranking
- Socioeconomic Status
10 Great Examples of Ordinal Data
Ordinal Data Example 1: The Likert Scale
The Likert Scale is famous for its characteristic point feature. It could either be a four, five, or seven-point scale of extremes comprising broad categories. For example, a seven-point system could be very satisfied, satisfied, somewhat satisfied, neutral, somewhat dissatisfied, dissatisfied, and extremely dissatisfied.
Mount Wachusett Community College outlines a plethora of response options of the Likert Scale. These options range from action knowledge and level of consideration to the amount of use and likelihood of an event. It may be of interest to you that the Likert Scale, due to its numerous response options, facilitates the analysis of most ordinal variables.
Ordinal Data Example 2: Education Level
As the name implies, this ordinal variable helps identify people’s education levels. With this, organizations can evaluate the job application of candidates as it includes different levels like elementary school, high school, bachelor’s degree, master’s degree, and PhD. Analyzing independent samples of this variable facilitates inferential statistics.
Ordinal Data Example 3: Customer Level of Satisfaction
If you are interested in the level of satisfaction your customers have with your services, you may consider this variable. You can come up with a customer satisfaction survey to determine the quality of your customer service with categories such as very unpleasant, somewhat unpleasant, unpleasant, neutral, somewhat pleasant, very pleasant, and pleasant. Distance between categories is of no priority here.
Ordinal Data Example 4: Language Ability
The language ability test helps to determine your proficiency with a specific language. As such, the interval values are beginner, intermediate, and fluent. International students usually take proficiency tests like TOEFL, where the medium of instruction in their desired school is different from their mother tongue. Such tests help to reveal the student’s fluency in English.
Ordinal Data Example 5: Performance of a Government Policy
Conducting a survey to evaluate the performance of a government policy usually involves four ordinal values, which are superior, effective, minimal, and inadequate. You can draw inferences from the frequency of selections on a specific level of measurement. In a sense, it can enable you to understand the political orientation of a sample population.
Ordinal Data Example 6: Time of the Day
Interestingly, you can categorize the different times of the day into natural rank order. It begins with dawn, morning, having noon at its peak. Then it progresses to afternoon, evening, and night. Furthermore, you can use a bar chart and learn other data visualization methods to develop inferential statistics of this variable.
Ordinal Data Example 7: Levels of Agreement
This type of variable is instrumental when proposing a motion or making a claim during a survey. Ordinal values of this variable include strongly disagree, disagree, neither agree nor disagree, agree, strongly agree. The distinction between categories has a subtle influence on the quality of the response because responses are discrete and independent of one another.
Ordinal Data Example 8: Military Ranks
The broad categories of this ordinal variable follow a progressive rank up the military ladder. They also include colonel, brigadier general, major general, lieutenant general. Of course, this is only a portion of the possible sample size. However, it depicts the natural rank order of ordinal data.
Ordinal Data Example 9: High School Class Ranking
The ordinal values here are quantitative variables since they include numerical values. Although the data is not a random variable, its values may be random if they follow an order. For example, we can identify a natural progression from first to 34th to 97th to 100th. However, the numeric value selection at each instant is random.
Ordinal Data Example 10: Socioeconomic Status
In conducting surveys to determine the socioeconomic status of individuals or families, the prevalent ordinal variables include low income, middle income, and high income. They give an idea of the income level and thus, create a basis for evaluating respective socioeconomic statuses.
Pro Tips to Boost Your Ordinal Data Skills
- Be clear and objective with your variables. When selecting the variables to include in your survey, ensure that they are clear and objective. Avoid any form of ambiguity and objectively describe responses to prevent any bias. This will ultimately improve your ordinal data skills.
- Stick with close-ended questions. Using close-ended questions enables you to obtain specific responses to questions. It is easier to analyze responses with “yes” and “no” than responses with open-ended questions where responders have to answer in their own words.
- Be selective about your target audience. Ensure that your sample population perfectly represents your target audience. This will help you extract the correct information and also eliminate bias. If the sample population is incorrect, the inference you draw from your ordinal data will be low fidelity.
What Should Be the Next Step in My Ordinal Data Learning Journey?
To advance and gain mastery of ordinal data, you should understand classifications as a function of statistical learning and predictive modeling. Being a researcher demands specific knowledge of some concepts, alongside an understanding of various levels of measurement and how they relate with each other.
Ordinal Data Examples FAQ
There are four levels of measurement. They include nominal, ordinal, interval, and ratio data. These levels also represent the different types of measurement scales. Each measurement scale has its characteristic feature that reveals specific information.
Range plays a special role in indicating variability of responses. It caters to all possible outcomes and bolsters the result from your descriptive statistical analysis. Moreover, it helps you capture the opinion of responders, even to the extremes.
Yes, you can represent various types of variables in numeric or quantitative form. Sometimes it facilitates inferential statistics since you can quantify responses. However, the distance between the responses remains unknown, and it is also impossible to conduct simple arithmetic operations on them.
It depends on the nature of the response variable. The scale responses on a Likert scale are discrete and relatively independent. However, the subject variable, such as the level of agreement, is continuous.
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