The dependent variable is the variable that is being measured or tested in an experiment. The dependent variable is dubbed dependent because it is thought to depend in some way on the variations of the independent variable.

A variable is any factor, trait, or condition that can exist in differing amounts or types. An experiment usually has three kinds of variables: independent, dependent, and controlled.

Time is a common independent variable, as it will not be affeced by any dependent environemental inputs. Time can be treated as a controllable constant against which changes in a system can be measured.

control variable

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.

A variable is said to be Binary or Dichotomous, when there are only two possible levels. These variables can usually be phrased in a “yes/no” question. Whether nor not someone is a smoker is an example of a binary variable.

categorical variable

Is Age Nominal or Ordinal Data? 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.

3 Answers. You could say that some variables are categorical or treat them as categorical by the length of their unique values. For instance if a variable has only unique values [-2,4,56] you could treat this variable as categorical. Every unique value in every variable treated as categorical will create a new column.

In short, countries don’t have an undisputable order and therefore “country” is a nominal variable. Now, countries may be represented by numbers (1 = Netherlands, 2 = Belgium and so on) in SPSS or some other data format. These numbers do have an undisputable order.

Nominal variables represent categories with no intrinsic order (e.g., job category or company division). Nominal variables could also consist of numeric values that represent distinct categories (e.g., 1=Male, 2=Female). The sex is a nominal variable, the blood pressure is numeric, and the date is ordinal.

Ordinal Data are inherently orderable categorical data like shirt sizes (s / m / l / xl), flood risk (low risk / medium risk / high risk) or age (young / middle aged / old).

Month should be considered qualitative nominal data. With years, saying an event took place before or after a given year has meaning on its own.

Shoes are assigned a number to represent the size, larger numbers mean bigger shoes so unlike the nominal scale that just reflects a category or class, the numbers of an ordinal scale show an ordered relationship between numbered items – we know that a shoe size of 8 is bigger than a shoe size of 4.

What is the difference between nominal, ordinal and scale? 6″), their house number is nominal. Ordinal numbers are words that represent rank and order in a set. In addition, numerical data can be further subdivided into interval and ratio data.

A nominal variable is a type of variable that is used to name, label or categorize particular attributes that are being measured. Some examples of nominal variables include gender, Name, phone, etc.

Height is a ratio variable, because the intervals between numbers are comparable and there is an absolute zero for height. it makes sense to say that a person 6 feet tall is twice as tall as a person who is 3 feet tall.

An 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. Variables like height, weight, enzyme activity are ratio variables.

Quantitative data is numerical. It’s used to define information that can be counted. Some examples of quantitative data include distance, speed, height, length and weight. It’s easy to remember the difference between qualitative and quantitative data, as one refers to qualities, and the other refers to quantities.

An ordinal variable is a categorical variable for which the possible values are ordered. Ordinal variables can be considered “in between” categorical and quantitative variables.

There are two types of quantitative data, which is also referred to as numeric data: continuous and discrete. As a general rule, counts are discrete and measurements are continuous. Discrete data is a count that can’t be made more precise. Typically it involves integers.

Examples of qualitative data include sex (male or female), name, state of origin, citizenship, etc. A more practical example is a case whereby a teacher gives the whole class an essay that was assessed by giving comments on spelling, grammar, and punctuation rather than score.

An example of quantitative research is the survey conducted to understand the amount of time a doctor takes to tend to a patient when the patient walks into the hospital.