Nominal and ordinal data are part of the four data measurement scales in research and statistics, with the other two being interval and ratio data. These four data measurement scales are subcategories of categorical and numerical data.
The Nominal and Ordinal data types are classified under categorical, while interval and ratio data are classified under numerical. This classification is based on the quantitativeness of a data sample.
Categorical data is a data type that is not quantitative i.e. does not have a number. Therefore, both nominal and ordinal data are non-quantitative, which may mean a string of text or date.
Nominal data is defined as data that is used for naming or labeling variables, without any quantitative value. It is sometimes called “named” data – a meaning coined from the word nominal.
There is usually no intrinsic ordering to nominal data. For example, Race is a nominal variable having a number of categories, but there is no specific way to order from highest to lowest and vice versa.
Ordinal data is a type of categorical data with an order. The variables in ordinal data are listed in an ordered manner. The ordinal variables are usually numbered, so as to indicate the order of the list. However, the numbers are not mathematically measured or determined but are merely assigned as labels for opinions.
Nominal data is a group of non-parametric variables, while Ordinal data is a group of non-parametric ordered variables. Although, they are both non-parametric variables, what differentiates them is the fact that ordinal data is placed into some kind of order by their position.
For example, very hot, hot, cold, very cold, and warm are all nominal data when considered individually. But when placed on a scale and arranged in a given order (very hot, hot, warm, cold, very cold), they are regarded as ordinal data.
The major character difference between ordinal and nominal data is that ordinal data has a set order to it. This set order is the bedrock of all other character differences between these two data types.
For instance, both ordinal and nominal data are evaluated using nonparametric statistics due to their categorical nature. Therefore, the mean and standard deviation cannot be evaluated for these data types.
However, the use of parametric statistics for ordinal data may be permissible in some cases. This is done with methods that are a close substitute to mean and standard deviation.
Examples of nominal data include the country, gender, race, hair color, etc. of a group of people, while that of ordinal data includes having a position in class as “First” or “Second”. Note that the nominal data examples are nouns, with no order to them while ordinal data examples come with a level of order.
Consider the two examples below:
b. How was your customer service experience?
The data to be collected from Example I. is a nominal data, while that of II. is an ordinal data.
There are four types of tests carried out on nominal data, namely; the McNemar test, Cochran Q’s test, Fisher’s Exact test, and Chi-Square test. The ordinal data tests are also four, namely; Wilcoxon signed-rank test, Friedman 2-way ANOVA, Wilcoxon rank-sum test, and Kruskal-Wallis 1-way test.
Although they are all non-parametric, these tests differ from each other. This difference is partly influenced by the ordered nature of ordinal data.
Nominal data analysis is done by grouping input variables into categories and calculating the percentage or mode of the distribution, while ordinal data is analyzed by computing the mode, median, and other positional measures like quartiles, percentiles, etc. Although discouraged, ordinal data is sometimes analyzed using parametric statistics, with methods that are a close substitute to mean and standard deviation.
The different nominal data collection techniques we have include; open-ended questions, multiple response choice questions, and close-open-ended questions, while ordinal data is collected using Likert scale, interval scale, rating scale, etc. Even though these collection techniques differ from each other, a single questionnaire could use both nominal and ordinal data collection techniques.
Use a single questionnaire to collect both nominal and ordinal data occurs in the event that the researchers need to collect both nominal and ordinal data.
Nominal data are categorical in nature, while ordinal data are in between categorical and quantitative. This is because we sometimes assign quantitative values to ordinal data. Although we cannot perform any arithmetic operation with ordinal numbers, it is quite different from nominal data which does not have any quantitative value at all
Ordinal data is mainly used to carry out investigations that involve getting people’s views or opinion on some matter, while nominal data is used for research that involve getting personal data of a person (e.g. biodata), place or thing. Consider a restaurant who needs to collect customer’s data before and after service.
Nominal data of the customer’s name, phone number and order will be taken by the restaurant before service. After service, the restaurant will take ordinal data of the customer’s feedback about the service rendered.
Nominal data give the respondents the freedom to freely express themselves and give adequate information. Ordinal data, on the other hand, does not give respondents the freedom to express themselves.
Rather, they are restricted to particular options to choose from. However, this restriction gives researchers access to concise data, by eliminating any possibility of having irrelevant data.
The disadvantage to giving the respondents the freedom to express themselves is that researchers have to deal with a lot of irrelevant data. Although ordinal data ensures that researchers don’t have to deal with irrelevances, it doesn’t give enough information.
When collecting customer feedback, for instance, a business gets informed about customer’s satisfaction levels but is ignorant about what influenced their feelings. This information may not be enough to assist the company in improving its customer service.
Nominal data collection does not include rating scales, which is very common with ordinal data collection. This is mainly because it does not have an order. The rating scales in ordinal data have an order which is used to rate variables.
However, these rating scales do not have a specific or predefined difference for each member of the list.
Nominal data collection techniques are not as user-friendly as ordinal data collection techniques. For open-ended and closed-open-ended questions, respondents may have to type their inputs, something many respondents find tiring and time-consuming.
Also, smiley and other user-friendly features can be integrated into ordinal data collection forms, making it user-friendly. This may not be the same with nominal data.
Ordinal variables restrict responders to some predefined set of options, with nominal data doing the same in some cases depending on which data collection technique is used. The multiple-choice option questions restrict responders to predefined options, while the open-ended and closed-open-ended questions don’t.
For example, when asking respondents to choose a gender with a predefined option of male and female, the closed-open-ended questions allow other genders to identify themselves. This way, the questionnaire understands non-binary gender and is all-gender inclusive.
Nominal data and ordinal data are both groups of non-parametric variables used to store information. They are both classified under categorical data.
The characteristics of nominal and ordinal data are similar in some aspects. For instance, they are both qualitative, have an inconclusive mean value, and have a conclusive mode. These similarities are all based on the fact that they are both categorical data.
There are two main types of data which are categorical and numerical data. Nominal and ordinal data are two of the four sub-data types, and they both fall under categorical data.
Categorical data can be counted, grouped, and sometimes ranked in order of importance. With categorical data, information can be placed into groups to bring some sense of order or understanding.
Nominal and Ordinal data have 2 categories each, namely; the matched category and the unmatched category. The matched category groups variables with similarities, while the unmatched category or independent category does a random grouping of variables.
Nominal data and ordinal data are used in areas of research where categorical data are generated. Settings, where they are both used, include social sciences, behavioral sciences, government agencies, and business settings. Data is collected from people by testing, observation, surveys, or questionnaires.
They are both visualized or analyzed graphically through with pie charts and bar charts. Although ordinal data can also be visualized with grayscale, mosaic, etc., The pie chart and bar chart is the common visualization techniques used to analyze percentage and frequency.
Both nominal and ordinal data can be analyzed using percentage and frequency (i.e. mode). The modal value of these two data types is conclusive.
In addition, they both have an inconclusive mean and standard deviation. Hence, it can’t be used for data analysis.
Categorical variables store data with values from a finite set of discrete categories. This finite set of data is usually placed in categorical arrays. Since nominal and ordinal data are categorical, they can both be placed in a categorical array.
They can both be arranged into categorical arrays, which takes less time and space during analysis. The arithmetic operations performed on numerical data take time and space, making nominal and ordinal data better alternatives.
Compared to interval data, nominal and ordinal data are less informative. Interval data is measured along a scale, in which each point is placed at an equal distance from one another.
Although nominal and ordinal data gather relevant information, with ordinal data having a scale to it, the inequality of the scale leaves them at a disadvantage.
They can both take numerical values, but these values are not arithmetic. Consider the two examples below:
E.g. How old are you?
This is an example of a nominal data collection that takes a numerical value as an input. However, we cannot perform any arithmetic operation on this input.
E.g.2. Rate your customer service experience on a scale of 1-5 (Lowest-Highest)
This is an example of an ordinal data collection that takes a numerical value. This value is, however, not arithmetic.
There are different available choice options, which are peculiar to each of nominal and ordinal data collection. However, there are also some available choice options that can be used for both nominal and ordinal data collection.
For example, radio buttons feature on Formplus builder may be used for both multiple-choice questions and Likert scale, with each collecting nominal and ordinal data respectively. Images may also be used as a data collection tool for both data types.
The number of tests carried out on nominal data and ordinal data are the same. Four different types of tests are carried out on each of these data types, with the matched and unmatched categories taking two tests each.
Age can be both nominal and ordinal data depending on the question types. I.e “How old are you” is used to collect nominal data while “Are you the firstborn or What position are you in your family” is used to collect ordinal data.
Age becomes ordinal data when there’s some sort of order to it.
Formplus is a web-based data collection tool that helps users gather data, process them, and make data-driven decisions. This data collection tool is the best for collecting nominal and ordinal data.
It has exciting features that make data collection a seamless experience for both questionnaires and respondents.
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.
Formplus gives you the flexibility and freedom to choose how you want your form to look. With the easy-to-use customization options, you can create stylish forms tailored to your brand.
Migrate from boring old-school questionnaires to stylish forms that depict your brand identity. There is also a custom CSS feature that gives you extra flexibility.
You receive an automatic notification whenever a respondent fills your form. The notification feature can be customized such that, you can choose who on your team should receive these notifications.
Formplus also allows you to customize the content of the notification message sent to respondents. This includes email and SMS notifications.
Gather useful insights that inform great business decisions with Formplus. There is an Analytics dashboard that reveals information like the total form views, unique views, abandonment rate, conversion rate, the average time it takes to complete a form, top devices, and the countries your form views are from.
This information can be useful for both business and academic research purposes.
Whether it is an event registration form, reservation form, or a quick survey, you are in charge of what information you require and want to collect for building and generating your leads.
From the point of application to online assessment, to interviews, Formplus has got you covered. In the event that an applicant gains employment, you can easily add changes to their information and easily manage existing employee data.
This way, clients no longer have to face difficulty while making payments. This is a useful feature for online businesses.
When dealing with statistical data, it is important to know whether the data you are dealing with is nominal or ordinal, as this information helps you decide how to use the data. A statistician is able to make a proper decision on what statistical analysis to apply to a given data set based on whether it is nominal or ordinal.
The first step to proper identification of nominal and ordinal data is to know their respective definitions. After which, you need to identify their similarities and differences so as not to mix them up during analysis.
This knowledge is very essential, as it helps a researcher determine the type of data that needs to be collected.
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