Good answer mam. In addition to what Hadeel has said I want to state as under:

In research we use several type of constructs Mapping rules have 4 categories Classification (number are used to group the responses), Order (one number is greater then less than or equal to some other number), Distance (difference between any pair of number is greater than,less than or equal to the difference between any other pair of numbers) and Origin (number series has a unique origin through number zero- absolute and meaningful zero point)

For your info, Mutually exclusive means two or more events which can not occur simultaneously-each excludes or precludes e.g war and peace, tossing a coin either we get head or tail but never both.

Collectively exhaustive means jointly exhaustive - in a set of event at least one of the event must occur.Rolling 6 sided coin the outcome could be 1, 2,3,4,5 and 6.

A mutually exclusive event occurs when there can be only one outcome like rolling a single die. A collective exhaustive event includes all possible outcomes like 1, 2, 3, 4, 5, and 6 on the die.

Now the characteristics of these measurement scales can be discussed briefly

Ratio scale is numbers used as measurements have numeric value i.e say weight of a given object. This is a scale with properties of categorization, order and equal intervals

Interval scale is data that incorporate equality of interval- the distance between one measure to the next measure i.e. temperature scale. This is a scale with properties of order and equal distance between points and with mutually exclusive and exhaustive categories

Ordinal scale is data determining greater than, equal to, less than status of a properties or an object. This is scale with mutually exclusive and collectively exhaustive categories as well as property of order but not distance or unique origin i.e a>b>c

Nominal Scale is a scale with mutually exclusive and collectively exhaustive categories but without properties of order, distance or unique origin i.e Mode-measure of nominal scale

Nominal scales are used for labeling variables, without any quantitative value. “Nominal” scales could simply be called “labels.” For example, labeling the gender type by two categories (male, female), and labeling the eyes color by many nominal scales (black, brown, blue, green, etc).

With ordinal scales, it is the order of the values is what’s important and significant, but the differences between each one is not really known. Ordinal scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort, etc. For example, ordering the level of satisfaction about a service (Very unsatisfied, Somewhat unsatisfied, Neutral, Somewhat satisfied, Very satisfied).

Interval scales are numeric scales in which we know not only the order, but also the exact differences between the values. The classic example of an interval scale is Celsius temperature because the difference between each value is the same. For example, the difference between 60 and 50 degrees is a measurable 10 degrees, as is the difference between 80 and 70 degrees. The problem with interval scales is that they don’t have a “true zero.” For example, there is no such thing as “no temperature.” With interval data, we can add and subtract, but cannot multiply or divide.

Ratio scales are the ultimate nirvana when it comes to measurement scales because they tell us about the order, they tell us the exact value between units, and they also have an absolute zero–which allows for a wide range of both descriptive and inferential statistics to be applied. Ratio variables can be meaningfully added, subtracted, multiplied, divided. Good examples of ratio variables include height and weight. Central tendency can be measured by mode, median, or mean; measures of dispersion, such as standard deviation and coefficient of variation can also be calculated from ratio scales.

Sharing link with examples;

http://www.statisticshowto.com/nominal-ordinal-interval-ratio/

Regards!

Good question I am following answer

Good answer mam. In addition to what Hadeel has said I want to state as under:

In research we use several type of constructs Mapping rules have 4 categories Classification (number are used to group the responses), Order (one number is greater then less than or equal to some other number), Distance (difference between any pair of number is greater than,less than or equal to the difference between any other pair of numbers) and Origin (number series has a unique origin through number zero- absolute and meaningful zero point)

For your info, Mutually exclusive means two or more events which can not occur simultaneously-each excludes or precludes e.g war and peace, tossing a coin either we get head or tail but never both.

Collectively exhaustive means jointly exhaustive - in a set of event at least one of the event must occur.Rolling 6 sided coin the outcome could be 1, 2,3,4,5 and 6.

A mutually exclusive event occurs when there can be only one outcome like rolling a single die. A collective exhaustive event includes all possible outcomes like 1, 2, 3, 4, 5, and 6 on the die.

Now the characteristics of these measurement scales can be discussed briefly

Ratio scale is numbers used as measurements have numeric value i.e say weight of a given object. This is a scale with properties of categorization, order and equal intervals

Interval scale is data that incorporate equality of interval- the distance between one measure to the next measure i.e. temperature scale. This is a scale with properties of order and equal distance between points and with mutually exclusive and exhaustive categories

Ordinal scale is data determining greater than, equal to, less than status of a properties or an object. This is scale with mutually exclusive and collectively exhaustive categories as well as property of order but not distance or unique origin i.e a>b>c

Nominal Scale is a scale with mutually exclusive and collectively exhaustive categories but without properties of order, distance or unique origin i.e Mode-measure of nominal scale

Thanks for your insightful view .Am grateful to you all

No challenge.

• A categorical variable, also called a nominal variable, is for mutual exclusive, but not ordered, categories. For example, your study might compare five different genotypes. You can code the five genotypes with numbers if you want, but the order is arbitrary and any calculations (for example, computing an average) would be meaningless.
• A ordinal variable, is one where the order matters but not the difference between values. For example, you might ask patients to express the amount of pain they are feeling on a scale of 1 to 10. A score of 7 means more pain that a score of 5, and that is more than a score of 3. But the difference between the 7 and the 5 may not be the same as that between 5 and 3. The values simply express an order. Another example would be movie ratings, from * to *****.
• A interval variable is a measurement where the difference between two values is meaningful. The difference between a temperature of 100 degrees and 90 degrees is the same difference as between 90 degrees and 80 degrees.
• https://www.graphpad.com/support/faq/what-is-the-difference-between-ordinal-interval-and-ratio-variables-why-should-i-care/

Wao! Thanks for all these insight

Good point. Following

Thanks

Agreed with Hadeel's answer

In a 30-year-old adult patient with a bacterial infection, I will exemplify it:

• If you have a fever, with a temperature greater than 37.5 oC. Fever is still considered if the temperature ranges from 37.5 to 40 or 42 ° C. This is an interval variable.
• If it is also diabetic, diabetes mellitus is usually uncontrolled by infection, often finding blood glucose values ​​above 126 mg / dl. The values ​​of blood glucose are an example of a ratio variable (they have absolute zero) and are also considered true variables.
• Assuming that he is a graduate student in medicine and that he is in the second year of the specialty of 4 to be a specialist in internal medicine. The second year, or second grade of his specialty, is an example of the ordinal type variable.
• Finally, if the type of infectious agent is found, the scientific or common name of the agent is a nominal variable; such as Escherichia coli, Staphylococcus aureus, etc ...

regards

Jose Luis

Good question and all answers are well articulated.

Thanks

Following

ok

Nominal Scales are to do with "what" and "where" ie. what feature is it and where is it? (location)