Nominal or categorical data identify classifications only. No particular quantities are implied. Examples include sex (male/female), departments (international/marketing/personnel)
A nominal category or a nominal group is a group of objects or ideas that can be collectively grouped on the basis of a particular characteristic—a qualitative property. A variable that codes whether each one in a set of observations is in a particular nominal category is called a categorical variable.
Valid data operations
A nominal group only has members and non-members. That is, nothing more can be said about the members of the group other than they are part of the group.[1] Nominal categories cannot be numerically organized or ranked. The members of a nominal group cannot be placed in ordinal (sequential) or ratio form.
Nominal categories of data are often compared to ordinal and ratio data, to see if nominal categories play a role in determining these other factors. For example, the effect of race (nominal) on income (ratio) could be investigated by regressing the level of income upon one or more dummy variables that specify race. When nominal variables are to be explained, logistic regression or probit regression is commonly used.
Examples
For example, citizenship is a nominal group. A person can either be a citizen of a country or not. One citizen of Canada does not have "more citizenship" than another citizen of Canada; therefore it is impossible to order citizenship according to any sort of mathematical logic.
Another example would be "words that start with the letter 'a'". There are thousands of words that start with the letter 'a' but none have "more" of this nominal quality than others.
Correlating two nominal categories is thus very difficult, because some relationships that occur are actually spurious, and thus unimportant. For example, trying to figure out whether proportionally more Canadians have first names starting with the letter 'a' than non-Canadians would be a fairly arbitrary, random exercise.
regards
A nominal category or a nominal group is a group of objects or ideas that can be collectively grouped on the basis of a particular characteristic—a qualitative property. A variable that codes whether each one in a set of observations is in a particular nominal category is called a categorical variable.
Valid data operations
A nominal group only has members and non-members. That is, nothing more can be said about the members of the group other than they are part of the group.[1] Nominal categories cannot be numerically organized or ranked. The members of a nominal group cannot be placed in ordinal (sequential) or ratio form.
Nominal categories of data are often compared to ordinal and ratio data, to see if nominal categories play a role in determining these other factors. For example, the effect of race (nominal) on income (ratio) could be investigated by regressing the level of income upon one or more dummy variables that specify race. When nominal variables are to be explained, logistic regression or probit regression is commonly used.
Examples
For example, citizenship is a nominal group. A person can either be a citizen of a country or not. One citizen of Canada does not have "more citizenship" than another citizen of Canada; therefore it is impossible to order citizenship according to any sort of mathematical logic.
Another example would be "words that start with the letter 'a'". There are thousands of words that start with the letter 'a' but none have "more" of this nominal quality than others.
Correlating two nominal categories is thus very difficult, because some relationships that occur are actually spurious, and thus unimportant. For example, trying to figure out whether proportionally more Canadians have first names starting with the letter 'a' than non-Canadians would be a fairly arbitrary, random exercise.
regards
Dear Ahmad Battal
The answer is in his introduction, and I agree with the one expressed by Hassan Nima.
Another way to explain what nominal or categorical information means is in the field of biostatistics; where it is considered that the information that only allows to locate characteristics of the phenomena or population under study in a specific group, to label them with some denomination and that in essence can not be measured or graduated, only to name or classify, corresponds to the nominal and categorical variables .
In this situation, when they correspond to the results of a research, if the variables can be expressed with qualitative type statistics; measures of central tendency (Fashion, frequency) and has no dispersion measures.
regards
Jose Luis
Nominal or categorical data is generally qualitative in that it cannot graduate or be measured like a ratio or interval data. However nominal data is used with non parametric testing like the Chi Square. Nominal data is using a name for data, where one is either a member of the group or not ex male/female, pregnant/not pregnant, or categorical data classifies items into groups and stratifies them. ex. parti socialiste/Républicain/Vert/parti communiste other ex; in my dissertation the categories were types of STEM class categories ex hard sciences/soft sciences/math/IT, computing/engineering. When comparing means in categories an ANOVA (analysis of variance) is used
Dr Réagan
ISTANDWITHELI
Nominal data are non-numerical data that can not be differentiated and are made up of cross- exclusive groups such as those whose answers are yes or no. In statistics, observations are recorded and analyzed using variables. Variables are classified into categories according to the attributes used to measure them. They use the specific characteristics of each variable such as color, gender, blood type, etc. These variables often have a limited number of possibilities and assume only one of the possible outcomes, and the value is one of the specific categories. Therefore, these are usually known as categorical data.
TYPES OF STATISTICAL DATA:
1) NUMERICAL, 2)CATEGORICAL, AND 3) ORDINAL
Numerical data. These data have meaning as a measurement, such as a person’s height, weight, IQ, or blood pressure; or they’re a count, such as the number of stock shares a person owns, how many teeth a dog has, or how many pages you can read of your favorite book before you fall asleep. (Statisticians also call numerical data quantitative data.)
Numerical data can be further broken into two types: discrete and continuous.
Categorical data: Categorical data represent characteristics such as a person’s gender, marital status, hometown, or the types of movies they like. Categorical data can take on numerical values (such as “1” indicating male and “2” indicating female), but those numbers don’t have mathematical meaning. You couldn’t add them together, for example. (Other names for categorical data are qualitative data, or Yes/No data.)
Ordinal data mixes numerical and categorical data. The data fall into categories, but the numbers placed on the categories have meaning. For example, rating a restaurant on a scale from 0 (lowest) to 4 (highest) stars gives ordinal data. Ordinal data are often treated as categorical, where the groups are ordered when graphs and charts are made. However, unlike categorical data, the numbers do have mathematical meaning. For example, if you survey 100 people and ask them to rate a restaurant on a scale from 0 to 4, taking the average of the 100 responses will have meaning. This would not be the case with categorical data.
Nominal or categorical data, as the name implies, is grouped into some sort of category or multiple categories. Quantitative data is related to quantities. Things like height, weight, GPA, number of hours spent studying, and other types of information that are quantitative and are collected just as numbers. A good way to remember the difference between categorical and quantitative data is to examine the answer to the question. If the answer is a number, then the data is quantitative. If the answer is a preference, a characteristic, or anything other than a number, then the data is categorical. For example, if you ask someone, How tall are you? they would answer with a number: 5 foot 6 inches. If someone asked, What's your favorite sport? then they would answer with a preference: Basketball. Therefore, the first question gives us quantitative data and the second gives us categorical data. Now that you understand the difference between categorical and quantitative data, let's look at how you can use categorical data in statistics.
Quote from
https://study.com/academy/lesson/what-is-categorical-data.html
Best wishes
Huda
Nominal or categorical data is grouped into single category or multiple categories. Quantitative data is related to numerical.
Towards the understanding and data analysis it would be always preferred to work with numbers either of the questions for any statistical inference.
Any null or alternate hypothesis is inferred with relevant data and structured analysis.
It all depends on how accurate your outcome is near to reality....
A categorical variable (sometimes called a nominal variable) is one that has two or more categories, but there is no intrinsic ordering to the categories. For example, gender is a categorical variable having two categories (male and female) and there is no intrinsic ordering to the categories.
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