Ordinal data classification is an integral step towards proper collection and analysis of data. Therefore, in order to classify data correctly, we need to first understand what data itself is.

Data is a collection of facts or information from which conclusions may be drawn. They can exist in various forms - as numbers or text on pieces of paper, as bits and bytes stored in electronic memory, or as facts stored in a person's mind.

When dealing with data, they are sometimes classified as being nominal or ordinal. Data is classified as either nominal or ordinal when dealing with categorical variables - non-numerical data variable, which can be a string of text or date.

Ordinal data is a kind of categorical data with a set order or scale to it. For example, ordinal data is said to have been collected when a responder inputs his/her financial happiness level on a scale of 1-10.

In ordinal data, there is no standard scale on which the difference in each score is measured. Considering the example highlighted above, let us assume that 50 people earning between $1000 to $10000 monthly were asked to rate their level of financial happiness.

An undergraduate earning $2000 monthly may be on an 8/10 scale, while a father of 3 earning $5000 rates 3/10. This is to show that the scale is usually influenced by personal factors and not due to a set rule.

Read Also: What is Nominal Data? Examples, Category Variables & Analysis

**Characteristics of Ordinal Data**

**Extension of nominal data:**Ordinal data is built on the existing nominal data. Nominal data is known as “named” data, while ordinal data is “named” data with a specific order or rank to it. Let us consider the ordinal data example given below:

Which of the following best describes your current level of financial happiness?

- Very happy
- Happy
- Neutral
- Unhappy
- Very unhappy

The options in this question are qualitative, with a rank or order to it. The rank, in this case, is a sign of ordinal data.

**No standardised interval scale**

The difference in variation between “Very happy” and “happy” does not necessarily have to be the same as the one between “happy” and “neutral”. There is no standardised interval scale of measurement for each variable.

In fact, the difference in variation can't be concluded using the ordinal scale. This is the scale is dependent on factors which are unique for each respondent.

**Establish a relative rank**

In the example mentioned above, ”very happy” is definitely better than “unhappy” and “neutral” is worse than “happy”. Unlike the interval scale, there is an established rank of order on this case.

This rank is used to group respondents into different levels of happiness.

**Measure qualitative traits**

The ordinal scale has the ability to measure qualitative traits. The measurement scale, in this case, is not necessarily numbers, but adverbs of degree like very, highly, etc.

In the given example, all the answer options are qualitative with “very” being the adverb of degree used as a scale of measurement.

**Measure numeric values**

Ordinal data can also be quantitative or numeric. When asked to rate your level of financial happiness, for example, the values are numeric.

However, numerical operations (addition, subtraction, multiplication etc.) cannot be performed on them.

**Has a median**

Unlike in nominal data where only the mode can be calculated, ordinal data has a median. Median is the value in the middle but not the middle value of a scale and can be calculated with data which has an innate order. Consider the ordinal variable example below.

****

Rate your knowledge of Excel according to the following scale.

- Advanced
- Intermediate
- Basic
- Novice
- Zero

In this example, the middle value is “Basic” while the value in the middle is “intermediate”.

**Has an order: Ordinal data has a specific rank or order, which may either be ascending or descending.**

There are two major use cases for ordinal data examples namely; Likert Scale and Interval Scale.

**Likert Scale:**

A Likert scale is a point scale used by researchers to take surveys and get people's opinion on a subject matter. It is usually a 5 or 7-point scale with options that range from one extreme to another. Consider this example:

How satisfied are you with our meal tonight?

- Very satisfied
- Satisfied
- Indifferent
- Dissatisfied
- Very dissatisfied

This is a 5 point Likert scale. Like in this example, each response in a 5-point Likert scale is assigned to a numeric value from 1-5.

**Read Also:**** 4, 5 & 7 Point Likert Scale + [Questionnaire Examples]**

**Interval Scale**

An interval scale is a type of ordinal scale whereby each response is an interval on its own. Examples of interval scale includes; the classification of people into a teenager, youth, middle age etc. is done according to their age group.

In which category do you fall?

- Child - 0 to 12 years
- Teenager - 13 to 19 years
- Youth - 20 to 35 years
- Middle age - 36 to 58 years
- Old - 59 years and above

Example 2: In a school, students are graded as either A, B, C, D, E, or F according to their score. Students that score 70 and above are graded A, 60-69 are graded B and so on.

- 70 and above
- 60-69
- 50-59
- 40-49
- 35-40
- 34 and below.

Ordinal variables can be classified into 2 main categories, namely; the matched and unmatched category. This ordinal variable classification is based on the concept of matching - pairing up data variables with similar characteristics.

According to Wikipedia, matching is a statistical technique which is used to evaluate the effect of a treatment by comparing the treated and non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned).

In the matched category, each member of a data sample is paired with similar members of every other sample with respect to all other variables, aside from the one under consideration. This is done in order to obtain a better estimation of differences.

By eliminating other variables, we are able to prevent them from influencing the results of our current investigation. For example, when investigating the cause of skin cancer, it is better to match people of the same race together because of melanin deficiency (a condition common to white people) is a known cause.

There are 2 different types of tests done on the Matched category, depending on the number of sample groups that are being investigated. Namely; the Wilcoxon signed-rank test and Friedman 2-way Anova

**Wilcoxon signed-rank test: This is a qualitative statistical test used to compare the 2 groups of matched samples to assess their differences.****Friedman 2-way ANOVA: This is a non-parametric way of finding differences in matched sets of 3 or more groups. Developed by Milton Friedman, this test procedure involves ranking rows together, then considering the values of each rank by columns.**

Unmatched samples, also known as independent samples are randomly selected samples with variables that do not depend on the values of other ordinal variables. Most researchers base their analysis on the assumption that the samples are independent, except in a few cases.

For example, suppose examiners want to compare the efficiency of 2 test marking software. They take random samples of 10 student's answer scripts and send to the 2 software for marking. It doesn't matter whether the answers ticked by these students are similar or not.

**Wilcoxon rank-sum test**

The Wicoxon rank-sum test is also known as the Mann-Whitney U test. It is a non-parametric test used to investigate 2 groups of independent samples. This test is usually used to test whether the samples belong to the same population. A similar qualitative test used on matched samples is the Wilcoxon signed-rank test.

**Kruskal-Wallis 1-way test**

This is a non-parametric test for investigating whether 3 or more samples belong to the same population. Named after William Kruskal and W. Allen Wallis, this test concludes whether the median of two or more groups is varied.

Ordinal data analysis is quite different from nominal data analysis, even though they are both qualitative variables. It incorporates the natural ordering of the variables in order to avoid loss of power.

Although Ordinal variables differs from other qualitative variables, parametric analysis like finding the mean and standard deviation is often discouraged for ordinal data. Rather, other analysis like median and mode are done instead.

This is due to the assumption that equal distance between categories does not hold for ordinal data. Therefore, positional measures like the median and percentiles, in addition to descriptive statistics appropriate for nominal data should be used instead.

The use of parametric statistics for ordinal data variables may be permissible in some cases, with methods that are a close substitute to mean and standard deviation. Here are some of the parametric statistical methods used for ordinal analysis.

**Univariate statistics: Used in place of mean and standard deviation, the appropriate univariate statistics for ordinal data include the median, quartiles, percentiles and quartile deviation.****Bivariate statistics: Mann-Whitney, Smirnov, runs and signed-rank tests are used in lieu of testing differences in mean with t-test.****Regression applications: Outcomes are predicted using a variant of ordinal regression, such as ordered probit or ordered logit.****Linear trends: It is used to find similarities between ordinal data and other variables in contingency tables.****Classification methods: This method uses matching to categorise data, after which dispersion is measured and minimized in each category to maximize classification results.**

Ordinal data can also be analysed graphically with the following techniques.

- Bar chart
- Pie chart
- Tables
- Mosaic plots
- Bump chart
- Colour or grayscale gradation.

**Surveys/Questionnaires**

Ordinal data is used to carry out surveys or questionnaires due to its “ordered” nature. Statistical analysis is applied to collected responses in order to place respondents into different categories, according to their responses. The result of this analysis is used to draw inferences and conclusions about the respondents with regard to specific variables. Ordinal data is mostly used for this because of its easy categorization and collation process.

**Research**

Researchers use ordinal data to gather useful information about the subject of their research. For example, when medical researchers are investigating the side effects of a medication administered to 30 patients, they will need to collect ordinal data.

After using the medication, each patient may be asked to fill a form, indicating the degree at which they feel some potential side effects. A sample ordinal data collection scale is illustrated below.

How often do you feel the following?

Very often often not often

Nausea ¤ ¤ ¤

Headache ¤ ¤ ¤

Dizzy ¤ ¤ ¤

Hungry ¤ ¤ ¤

**Customer service**

Companies use ordinal data to improve their overall customer service. After using their service or buying their product, many companies are known to ask customers to fill an after service form, describing their experience.

This will help companies improve their customer service. Consider the example below:

How will you rate our service?

Good Okay Bad

Food ¤ ¤ ¤

Waiter ¤ ¤ ¤

Waiting time ¤ ¤ ¤

Environment ¤ ¤ ¤

**Job applications**

During job applications, employers sometimes use a Likert scale to collect information about the level of applicant's skill in a field. When an applicant is applying for a social media manager position, for instance, a Likert scale may be used to know how familiar an applicant is with Facebook, Twitter, LinkedIn etc.

E.g. How familiar are you with the following social networks?

1 2 3 4 5

Facebook ¤ ¤ ¤ ¤ ¤

Instagram ¤ ¤ ¤ ¤ ¤

Twitter ¤ ¤ ¤ ¤ ¤

LinkedIn ¤ ¤ ¤ ¤ ¤

**Personality tests**

This is a common test that is usually administered by employers to their potential employees. This is done so that the employer will know whether the applicant is a good fit for the organisation.

Some psychologists also use this to get more information about their patient before treatment. That way, they are able to know which questions to ask, what to say and what not to say.

- The options do not have a standardised interval scale. Therefore, respondents are not able to effectively gauge their options before responding.
- The responses are often so narrow in relation to the question that they create or magnify bias that is not factored into the survey. For example, in the customer service example cited above, a customer might be satisfied with the taste of the meal, but the meat was too tough or the water too cold. In the end, the restaurant will have a report on customer experience, but not be able to differentiate the reason why they chose the response they did.
- It does not allow respondents the opportunity to fully express themselves. They are usually restricted to some predefined options.

**30+ Field Types****With a wide range of field types, you can easily collect ordinal data.****Fields like matrix and scales make it easy to collect any set of ordinal data you need from your respondents.****Do you need your respondents to give you repeatable data where they specify how many times they want to fill a field?****You can also use tables if you need to collect ordinal data that is repeatable.**

Collect data in remote locations or places without reliable internet connection with Formplus. Offline forms can also act as a backup to the standard online forms especially in cases where you have unreliable WiFi, such as large conferences and field surveys.

When responders fill a form in the offline mode, responses are synced once there is an internet connection. Using conversational SMS, you can also collect data on any mobile device without an internet connection.

**Share & Export Data in Different Formats**

You can store collected data in tabular format or even export as PDF/CSV. Respondents can also submit their responses as PDF, Doc attachment or as images. These responses can also be shared as links through other applications like Gmail, WhatsApp, LinkedIn etc.

**Get Submission Notifications**

You can send notifications to your respondents and your team whenever your form is completed.

The notification could be set such that, you can choose who on your team should receive these email if you need to route them directly to the responsible people.

Formplus also allows you to customise the content of the notification message sent to respondents based on what they have filled out in the form.

**Ability to Customise Forms**

With Formplus, you can choose how you want your forms to look like. You can create an attractive and interactive form that makes your respondents feel encouraged to respond. There are also different choice options for you to choose from.

**Email Notifications**

You have the ability to choose how and when you receive notifications. There is also a customisable feature on the notifications sent to respondents upon completion of the form.

In the event that you are working with a team, you can also add team members to your list of notification recipients.

**Different Storage Options**

Formplus allows you to choose how you want to store data. After exporting data in tabular, csv or pdf format, you can either save them on your device or upload to the cloud.

Although Formplus has a cloud platform, you can also upload your data on Dropbox, Google Drive or Microsoft OneDrive. There are no limitations to the amount of files, images or videos that can be uploaded.

Ordinal data is designed to infer conclusions, while nominal data is used to describe conclusions. Descriptive conclusions organise measurable facts in a way that they can be summarised.

If a restaurant carries out a customer satisfaction survey by measuring some variables over a scale of 1-5, then satisfaction level can be stated quantitatively. However, no inference can be drawn about why some customers are satisfied and some are not.

The only inference that can be made is something like, “Most customers are (dis)satisfied”. This is, however, not the case for descriptive conclusion, where one can get enough information on why customers are (dis)satisfied.

Reference

- https://www.slideshare.net/mssridhar/types-of-data-42010881?
- https://www.slideshare.net/Intellspot/nominal-data-vs-ordinal-data-comparison-chart
- https://www.slideshare.net/rosesrred90/inferential-statistics-nominal-data?
- https://www.slideshare.net/SAssignment/graphical-descriptive-techniques-nominal-data-assignment-help
- https://www.slideshare.net/plummer48/scaled-v-ordinal-v-nominal-data3

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