Types of Variables

---

Basic Terminology:« Previous | Techniques of Collecting Data: Next »

---

Outline:

• Types of Variables
• Quantitative Variable
• Example: Quantitative Variable
• Qualitative Variable
• Scale of Measurement
• *Nominal Scales
• *Ordinal Scales
• *Interval Scales
• *Ratio Scales
• Independent Variable
• Dependent Variable
• Intervening Variable
• Control Variable
• Extraneous Variable
• Confounding Variable
• References:

---

Types of Variables

A quality or phenomenon that changes or varies over time for different individuals or objects under investigation is referred to as variable (Mendenhall, et.al., 2012). In research, a variable is essentially a person, place, object, or phenomena that you are attempting to quantify in some way. A variable in statistical research is defined as an attribute of a study object.

You must understand the types of variables with which you are working in order to select appropriate statistical tests and interpret the results of your study.

The goal of all research is to describe and explain the world's variation. Variance is simply the difference; that is, variation that occurs naturally in the world versus change that we create as a result of manipulation. Variables are names given to the variation that we want to explain.

Variables are used by researchers and statisticians to describe and measure the items, places, people, or ideas they are studying. There are many different types of variables, and when designing studies, selecting tests, and interpreting results, you must choose the right variable to measure. A solid understanding of variables can lead to more precise statistical analyses and results. In this article, we will discuss the various types of variables and answer some frequently asked questions.

The following are the most common types of variables in research:

Quantitative Variable

Quantitative variables are numerical in nature. They are a quantifiable quantity. Any data set containing numbers or amounts.

Example: Quantitative Variable

Height, student enrolment, class size, family size, test scores, entrance test results, crime rate, salary, number of passengers, a volume of orange juice, etc.

There are two types of quantitative variables:

Discrete variable - This refers to variables that can only be obtained, have a finite number of values, or can be counted.

Example: Discrete Variable

Number of family members, number of new car sales, number of defective bulbs, faculty size, hospital staff size, number of students enrolled in Statistics course, number of bedrooms in a house

Continuous variable - Variables that can assume many values corresponding to the points on a line interval.

Example: Continuous Variable

Here are some examples: Crime rates, cell density, rainfall, temperature, air pressure, weight, height, study hours, time, salary, distance traveled

Qualitative Variable

Qualitative variables, also known as categorical variables, are non-numerical values or groupings. Eye or hair color are two examples. Qualitative variables can be further classified into three types by researchers:

Here are some examples: gender, taste ranking, religious affiliation, academic achievement, marital status, type of high school attended and many more.

Scale of Measurement

In statistical analysis, a measurement scale is the type of information provided by numbers. Each of the four scales (nominal, ordinal, interval, and ratio) provides a unique set of data.

Nominal Scales

A nominal scale is a measurement scale that is used to categorize events or objects. This scale does not necessitate the use of numeric values or categories ranked by class, but rather the use of unique identifiers to label each distinct category.

Example: Nominal Scale

Numbers on the back of a baseball jersey, driver’s license numbers, product serial numbers, and gender.

Gender is an example of a nominal measurement in which one gender, such as males, is labeled with a number (e.g., 1) and the other gender, females, is labeled with a different number (e.g., 2). Numbers do not imply that one gender is better or worse than the other; they are simply used to categorize people. In fact, because they do not represent an amount or a quality, any other number could be used. Certain statistical techniques make it impossible to use word names, but numerals can be used in coding systems.

Ordinal Scale

Numbers represent rank order and indicate the order of quality or quantity in ordinal scales, but they do not provide an amount of quantity or degree of quality. An ordinal scale of measurement represents a ranked series of relationships.

Example: Ordinal Scale

Movie Rating, satisfaction rating (“extremely dislike”, “dislike”, “neutral”, “like”, “extremely like”), Rank of Employee, socio economic status (“low income”,”middle income”,”high income”), and education level (“high school”,”BS”,”MS”,”PhD”).

Usually, the number 1 means that the person (or object or event) is better than the person labeled 2; person 2 is better than person 3, and so forth—for example, to rank order persons in terms of potential for promotion, with the person assigned the 1 rating having more potential than the person assigned a rating of 2. Such ordinal scaling does not, however, indicate how much more potential the leader has over the person assigned a rating of 2, and there may be very little difference between 1 and 2 here. When ordinal measurement is used (rather than interval measurement), certain statistical techniques are applicable (e.g., Spearman’s rank correlation).

Interval Scale

An interval scale is a scale that represents quantity and has equal units, but zero represents nothing more than an additional point of measurement. Furthermore, zero is not the absolute lowest value. Rather, it's a point on a scale with numbers above and below it. If the value zero is used, it simply serves as a reference point on the scale and does not indicate the complete absence of the characteristic being measured. Temperature scales such as Fahrenheit and Celsius are examples of interval measurement. 0 °F and 0 °C do not indicate a lack of temperature on those scales.

Examples of interval variables include: temperature (Farenheit), temperature (Celcius), pH, SAT score (200-800), credit score (300-850).

Ratio Scales

It has the property allows one to make statements of equality of intervals. This is the highest level of measurement which includes the inherent zero starting point. When measuring the length of a piece of wood in centimeters, there is a quantity, equal units, and that measure cannot be less than zero centimeters. It is not possible to have a negative length.

The most common examples of this scale are number of children in a family, height, money, age, and weight.

Types of Variables According to Functional Relationship

Independent Variables

An independent variable is a unique characteristic that cannot be changed by the other variables in your experiment. If the goal is to predict the value of one variable based on the other, this is referred to as a predictor variable.

Dependent Variables

This is sometimes referred to as the criterion variable, and its value is predicted. It is dependent on and can be altered by other components.

For example, academic achievement is dependent on Intelligent Quotient, study habits, interests, attitudes and many more. Hence, IQ, study habits, interests, attitudes are independent variables. On the other hand, academic achievement is the dependent variable.

For example, the time you spent studying (independent) can affect the grade on your test (dependent) but the grade on your test does not affect the time you spent studying.

Intervening variables

An intervening variable, also known as a mediator variable, is a theoretical variable used by a researcher to explain a cause or connection between other study variables—typically dependent and independent variables.

For example, The relationship between poverty (the independent variable) and life expectancy may be of interest to researchers (the dependent variable). The two variables were discovered to have a strong correlation. They discover, in particular, that more impoverished people have lower life expectancies.

However, the researchers have failed to notice the intervening variable healthcare without recognizing it. It turns out that those who are poorer have less dependable access to healthcare, which inevitably indicates that their life expectancies are lower.

Control variables

Characteristics that are stable and do not vary during a study are referred to as control or controlling variables. Other factors are unaffected by them. To avoid bias, researchers could keep a control variable constant throughout an experiment.

Extraneous variables

Extraneous variables are variables that affect the dependent variable but were not originally considered by the researcher when designing the experiment. These undesired variables have the potential to influence the outcomes of a study or how a researcher perceives those results accidentally.

Confounding variables

A confounding variable is one you didn't account for that can obscure the impact of another one. Confounding factors might invalidate your experiment results by biasing them or implying a relationship between variables when there isn't one.

---

"I'm sure I don't have all of the information about the different types of variables right here." I'm hoping and would love to hear your thoughts on this topic in the comments section." You can follow to this blog to receive notifications of new posts.

---

References:

https://libguides.usc.edu/writingguide/variables
     https://www.scribbr.com/methodology/types-of-variables/
     https://ori.hhs.gov/education/products/sdsu/variables.htm
     https://nces.ed.gov/nceskids/help/user_guide/graph/variables.asp
     https://conjointly.com/kb/understanding-variables/
     https://www.indeed.com/career-advice/career-development/types-of-variables
    

Is this article useful to you? If yes, please share this blog.

---

Basic Terminology:« Previous | | Techniques of Collecting Data: Next »

---

Share this blog: