When asked for input on a website, you are given a scale from 1 to 10, and you must select any number to convey your feelings about the platform. If it’s above 5, it implies the service was good, five means it was ordinary, and less than five means it wasn’t good enough. This is the purpose of ordinal data.
Data Analysis and Ordinal Data
Data analysis is essential for organisations to assess and analyse their performance and make smart business decisions that will significantly impact their performance. These data are often gathered from a variety of sources, and each data set is divided into qualitative and quantitative categories. Because each type serves a particular function, analysts should first understand the various forms of data, their functions, etc.
Quantitative data are gathered using numbers, whereas qualitative data are more descriptive and used for in-depth study. Each type differs from one another, and ordinal data is one of the most common types of data. This detailed guide on ordinal data and its applications in data analysis can be found here.
What is Ordinal Data?
Ordinal data is a sort of qualitative data that categorises variables. Ordinal data is distinguished by the fact that the categories it uses are ordered on some form of hierarchical scale. Ordinal data is the second most sophisticated measurement level, following nominal data.
Key Characteristics of Ordinal Data
To better understand the notion, you should know some features of ordinal data.
- Has a Median
A median value exists for ordinal data scales. This can be estimated using data with an inherent sense of order; however, it isn’t always the centre value on the scale.
- There Is No Uniformity in the Interval Scale
An ordinal scale cannot be used to establish a standard interval. The distinction between “strongly agree” and “agree” in the example mentioned above might not be the same as the distinction between “moderate” and “disapprove.”
- Extension of Nominal Data
The range of nominal data is thought to include ordinal data. This is thus because ordinal data, like nominal data, is “named” data but also has a rank or specified order.
- Can Characterise Qualitative or Non-numerical Traits
Ordinal data can be used to gauge emotions and other qualitative factors needed for market research, as shown in the example above, where the questions seek to grasp a respondent’s job happiness.
- Establishes a Relative Rank
According to the example mentioned above, selecting “Strongly agree” indicates that the respondent agrees with the statement more than they would have if they had selected “agree.” This shows the existence of relative ranks.
- Able to Quantify Numerical Values
Ordinal scales can sometimes be numerical. If the scale is ordinal, even non-numerical scales can have rank. These quantities cannot be subjected to numerical operations.
What Exactly Is the Distinction Between Ordinal, Nominal, Interval, and Ratio Variables?
Before we proceed, there are four different types of data, which are usually confused among beginners. They are,
- Nominal data
- Ordinal data
- Interval data
- Ratio data
- Nominal data is the most basic sort of data. It categorises data solely by labelling or naming values, such as marital status, hair colour, or eye colour. There is no hierarchy in it.
- Ordinal data groups information while establishing a hierarchy or rating. Consider categorising people’s economic condition as “privileged,” “middle income,” or “poor.”
- Interval data introduces measured intervals while simultaneously classifying and ranking data. Scales that measure temperature in Celsius or Fahrenheit are a wonderful example.
- Ratio data organises and ranks information similarly to interval data and makes use of measured intervals. Ratio data, however, also has a true zero, in contrast to interval data.
Types of Ordinal Data
Now that we now have an idea of what ordinal data is, the next step is on how to analyse them. To simplify things, many data analysis tools are available to perform analysis. Additionally, various tests can be run on the data depending on the needs and the kinds of ordinal data they contain.
The Matched Category
Each member of a data sample is paired with similar members of every other sample in terms of variables other than the one under consideration in the matched category. This is done to improve the assessment of differences.
We can prevent other variables from impacting the results of our current inquiry by removing them. When examining the cause of skin cancer, for example, it is preferable to group persons of the same race together because melanin deficiency is a recognised factor.
Depending on how many sample groups are being examined, two distinct sorts of tests are run on the Matched category. the Friedman 2-way Anova and the Wilcoxon signed-rank test, specifically
- Wilcoxon Signed-Rank Test:
This qualitative statistical test compares the two sets of matched samples to determine how they differ from one another.
- Friedman 2-Way ANOVA:
This method of identifying differences in matched sets of three or more groups is non-parametric. This test method, created by Milton Friedman, includes grouping rows together and analysing the values of each rank by columns.
The Unmatched Category
Unmatched samples, sometimes referred to as independent samples, are samples that were chosen at random and contained variables whose values are independent of those of other ordinal variables. Except in a few instances, the majority of researchers base their study on the premise that the samples are independent.
Consider the scenario when examiners want to contrast the effectiveness of two test marking programs. They randomly select ten students’ answer scripts and send them to two different software programs for marking. Whether or whether the responses checked by these pupils are comparable is unimportant.
- Wilcoxon Rank-Sum Test
The Mann-Whitney U test is another name for the Wilcoxon rank-sum test. It is a non-parametric test used for two independent sample groups. This test often aims to determine whether the samples come from the same population. The Wilcoxon signed-rank test is a comparable qualitative analysis applied to matched samples.
- Kruskal-Wallis 1-Way Test
This non-parametric test determines if three samples or more are representative of the same population. This test, which bears the names of William Kruskal and W. Allen Wallis, determines whether the median of two or more groups is variable.
Ordinal Data Examples
Here are some ordinal data examples for you to understand what they are like.
Researchers utilise a Likert scale, a point scale, to conduct surveys and get opinions from respondents. Typically, there are 5 or 7 possibilities on the scale, with the selections ranging from extreme to moderate. Consider this example:
How are you pleased with our service?
- Very satisfied
- Very dissatisfied
A 5-point Likert scale was used. Each response on a 5-point Likert scale is given a numerical value between 1 and 5, just like in this example.
A sort of ordinal scale called an interval scale treats each response as its interval. Examples of interval scales include categorising people into age groups based on their age, such as teenagers, young adults, persons in their middle years, etc.
Other widely used examples are as follows:
- financial standing (poor, middle income, wealthy)
- Income distribution in non-equal ranges ($10k–$35k, $35k–65k, $65k–100k)
- Course grades (A+, A-, B+, B-, C)
- academic level (Elementary, High School, College, Graduate, Post-graduate)
- military echelons (Colonel, Brigadier General, Major General, Lieutenant General)
- Age (child, teenager, young adult, middle-aged, retiree)
Ordinal data, as should be evident by now, is an inaccurate but helpful method of measuring and sorting data depending on its qualities. Next, we’ll look at how ordinal data is acquired and how it’s typically used.
Uses of Ordinal Data
Ordinal data are frequently used to gather demographic data and conduct in-depth polls to gauge public opinion on a certain law or service. This is ubiquitous in industries like finance, marketing, and insurance. Still, it’s also employed by the government in other contexts, including the census, and it’s frequently used when conducting customer satisfaction surveys.
Due to its “ordered” character, ordinal data is utilised to conduct surveys or questionnaires. In order to categorise respondents according to their responses, statistical analysis is done to gather responses. The outcome of this analysis is utilised to make deductions and judgments about the respondents concerning certain factors.
Ordinal data is typically utilised for this since it is simple to categorise and compile.
Study Researchers collect pertinent data on their research topics using ordinal data. For instance, ordinal data will be required when medical researchers examine the side effects of a medicine given to 30 people.
Each patient may be asked to complete a questionnaire after taking the drug to indicate the severity of any potential side effects. Below is an example ordinal data gathering scale.
How often do you feel the following?
Frequently, Often, or Not Often
Nausea ¤ ¤ ¤
Headache ¤ ¤ ¤
Dizzy ¤ ¤ ¤
Hungry ¤ ¤ ¤
Ordinal data is used by businesses to enhance their general customer service. Many businesses urge clients to complete an after-service form detailing their experience after utilising their service or purchasing their goods.
This will assist businesses in providing better customer service. Consider the following illustration:
What do you think of our service?
Good, OK, or Bad
Food ¤ ¤ ¤
Waiter ¤ ¤ ¤
Waiting time ¤ ¤ ¤
Environment ¤ ¤ ¤
Employers will occasionally utilise a Likert scale while collecting data from job applicants throughout the application process. A Likert scale may be utilised, for example, when a candidate is seeking a position as a social media manager to determine their familiarity with Facebook, Twitter, LinkedIn, etc.
How well-versed are you in the following social networks, for instance?
1/ 2/ 3/ 4/ 5
Facebook ¤ ¤ ¤ ¤ ¤
Instagram ¤ ¤ ¤ ¤ ¤
Twitter ¤ ¤ ¤ ¤ ¤
LinkedIn ¤ ¤ ¤ ¤ ¤
This is a typical test that employers frequently give prospective workers. This is done for the employer to determine whether the applicant is a suitable fit for the company.
Additionally, some psychologists use this to learn more about their patients before treatment. They can make informed decisions about what questions to ask, what to say, and what not to say.
Ordinal Data: Advantages and Drawbacks
Ordinal data has its own set of applications and constraints, much like every other type of data.
Advantages of Ordinal Data
Here are the advantages of using ordinal data,
- The simplicity of comparing different variables is the main benefit of adopting an ordinal scale.
- It is very practical to group the variables after they have been ordered.
- Due to the ease of analysis and categorisation, it is effectively employed in surveys, polls, and questionnaires. Comparing collected data makes it simple to reach meaningful conclusions about the target audience.
- The results are more illuminating than the nominal scale since the values are expressed relative to one another using a linear rating scale.
Drawbacks of Ordinal Data
The drawbacks of using ordinal data are as follows:
- There is no standardised interval scale for the choices. As a result, respondents are unable to evaluate their options efficiently before replying.
- A survey’s bias is rarely taken into account because the replies are frequently so limited in scope relative to the query.
For instance, in the last customer service scenario, the customer might have been pleased with the meal’s flavour but not with the meat’s texture or the water’s temperature. In the end, the restaurant will have a report on customer satisfaction but won’t be able to pinpoint why the client’s response was what it was.
- Respondents are not given a chance to express themselves completely. Typically, they are limited to a few predetermined selections.
The Bottom Line
Ordinal data reveals more about the underlying data. They are easier for businesses to understand the genuine thoughts of the majority thanks to this more descriptive element of them, and based on that information, they will be able to make more wise business judgments.
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