Quick Answer: What Is An Example Of Ordinal Data?

What type of data is age?

Mondal[1] suggests that age can be viewed as a discrete variable because it is commonly expressed as an integer in units of years with no decimal to indicate days and presumably, hours, minutes, and seconds..

What are the two types of data?

We’ll talk about data in lots of places in the Knowledge Base, but here I just want to make a fundamental distinction between two types of data: qualitative and quantitative. The way we typically define them, we call data ‘quantitative’ if it is in numerical form and ‘qualitative’ if it is not.

Is ordinal data qualitative or quantitative?

Data at the ordinal level of measurement are quantitative or qualitative. They can be arranged in order (ranked), but differences between entries are not meaningful. Data at the interval level of measurement are quantitative. They can be ordered, and meaningful differences between data entries can be calculated.

Can ordinal data be continuous?

In some cases, the measurement scale for data is ordinal, but the variable is treated as continuous. … At the same time, some researchers would argue that a Likert scale, even with seven values, should never be treated as a continuous variable.

What is an example of ratio data?

An excellent example of ratio data is the measurement of heights. Height could be measured in centimeters, meters, inches, or feet. It is not possible to have a negative height. When comparing to interval data, for example, the temperature can be – 10-degree Celsius, but height cannot be negative, as stated above.

What are the 5 types of measurements?

Types of data measurement scales: nominal, ordinal, interval, and ratio.

What is an example of interval data?

Examples of interval data includes temperature (in Celsius or Fahrenheit), mark grading, IQ test and CGPA. These interval data examples are measured with equal intervals in their respective scales. Interval data are often used for statistical research, school grading, scientific studies and probability.

What are 4 types of attitude scales?

Four types of scales are generally used for Marketing Research.Nominal Scale. This is a very simple scale. … Ordinal Scale. Ordinal scales are the simplest attitude measuring scale used in Marketing Research. … Interval Scale. … Ratio Scale.

Is age nominal or ordinal?

Age can be both nominal and ordinal data depending on the question types. I.e “How old are you” is a used to collect nominal data while “Are you the first born 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.

Is gender ordinal or nominal?

There are two types of categorical variable, nominal and ordinal. A nominal variable has no intrinsic ordering to its categories. For example, gender is a categorical variable having two categories (male and female) with no intrinsic ordering to the categories. An ordinal variable has a clear ordering.

What are the 4 types of data?

No doubt you’ve noticed that quantitative data and qualitative data can be sub-divided into 4 further classes of statistical data types; Ratio Data, Interval Data, Ordinal Data and Nominal Data.

Is age an example of ordinal data?

Age is frequently collected as ratio data, but can also be collected as ordinal data. … Variables that are naturally ordinal can’t be captured as interval or ratio data, but can be captured as nominal.

Is name nominal or ordinal?

A nominal variable is one of the 2 types of categorical variables and is the simplest among all the measurement variables. Some examples of nominal variables include gender, Name, phone, etc.

What kind of data is test scores?

[Ratio] Test scores on course examinations are often recorded as percent correct. Such scores are at the ratio level of measurement because there is an absolute zero value (0% correct) and differences between values can be compared meaningfully.

What is difference between nominal and ordinal data?

Nominal and ordinal are two of the four levels of measurement. Nominal level data can only be classified, while ordinal level data can be classified and ordered.