This article will make you learn all the important concepts related to independent and dependent variables. These are important to learn because everything from Math to Science requires the application of variables. Also, we will look at some independent and dependent variables examples.

You must be well acquainted with the term variables. Variables are nothing but attributes that contain a lot of values. A single variable can have a real number value, an imaginary value, rational and irrational value, etc. Real-life examples of variables are weight, height, humidity, or exam results. Variables have a lot of applications in computer programing, mathematical and physics derivations, and statistics.

**Types of Variables**

Variables are of two types: ‘Independent’ and ‘Dependent’ variables. Both are very important for researchers. These are used in experiments to help examine the cause-and-effect links.

- Independent variables are always the cause. The value of an independent variable doesn’t change while researching.
- Dependent variables are considered as the ‘effect’. The magnitude of these depends on the value of the independent variable.

**For example:** Let us say that a group of scientists has devised a research plan. They determine how the changes in the temperature of your room affect your result.

**Solution: **In this case, the change in the room’s temperature is your independent variable. You change the ambient temperature for half of the participants and make it warmer for the other half. Your dependent variable is, let us say, math test scores. You measure the math skills of all participants using a standardized test and check whether they differ based on room temperature. Let us now study what independent and dependent variables with the explanation are.

**Independent Variable Definition**

In an experimental study, an independent variable is a variable that you control or modify to see what effects it has. It’s labeled “independent” because it’s unaffected by the study’s other factors.

You can also refer to independent variables as:

- Explanatory variables: They are used to explain the result of an event.
- Predictor variables: The value of the dependent variable is predicted using independent variables.
- Right-hand-side variables: In regression equations, they are always placed on the right side.

The terms mentioned above are often used in mathematics, especially in statistics.

**Types of Independent Variables**

Independent variables are further classified as follows:

- Experimental independent variables: Such types of variables can be manipulated or altered directly by anyone.
- Subject variables: You cannot manipulate or change Subject Variables. They are beneficial in a category-wise grouping of research subjects.

**Experimental Variables**

In studies, independent variables are actively manipulated to find how they affect the dependent variable. The independent variable is frequently applied at several levels to see the difference in the result.

Only two levels are required to determine whether an independent variable has any influence. Multiple levels can also be used to see how the independent variable impacts the dependent variable.

For example:

You’re researching the effects of a new medicine on Asthmatic patients with Covid variants. The therapy that you directly modify between groups is your independent variable.

You allocate your patients to one of three groups at random:

- Group 1: Low Dose
- Group 2: High Dose
- Group 3: Controlled Dose

For every experiment to be true, you must randomly assign the independent variables in different levels to your participants (patients in this case).

Random assignment allows you to keep track of participant characteristics so that they don’t impact the result of your investigation. This assures you that the results of your dependent variable are completely due to the modification of the independent variable.

**Subject Variables**

Researchers cannot manipulate subject variables. These are the traits that differ between individuals. Gender identity, race, color, property, and highest education, for example, are all key topic variables treated as independent variables by sociologists.

Because these are features of existing groups, they can’t be assigned to individuals randomly. Instead, you might design a study that compares the results of groups of participants based on their characteristics. Since there are no traces of random assignment, this is considered the quasi-experimental design.

An example of a Quasi-experimental design is you’re looking at how gender influences brain responses to infant screams.

Your independent variable here is the subject variable, which is the participants’ gender identification. There are three types of people: males, females, and others.

The brain activity reaction to hearing infant cries is your dependent variable. When people hear a child cry without realizing it, you use fMRI scans to monitor brain activity. You discover that females take care of the infant as soon as it cries while the male takes time. This concludes that gender identification impacts brain responses to infant screams.

**Dependent Variable Definition **

The dependent variable is the one that alters when there is a change in an independent variable. It’s the result you want to measure, and it “depends” on the independent variable specified.

You can find dependent variables as the following in statistical mathematics:

- Response variables: Since they change in response to another variable.
- Outcome variables: The result you want to find is represented by these variables.
- Left-hand-side variables: For any regression equation, dependent variables are present on the left side.

After you’ve altered the independent variable, you’ll record the dependent variable. By doing statistical analysis, you may determine the extent of the effect of the independent variable on the dependent variable.

You can predict how your independent variable changes your dependent variable by carefully examining your findings.

**Identifying Independent and Dependent Variables**

When organizing a complicated study or reviewing an academic journal, you may find yourself in a situation where the difference between independent and dependent variables is negligible.

It’s critical to pay close attention to research design since a dependent variable from one research could be the independent variable in another. To recognize independent and dependent variables use the questions mentioned below:

For independent variables:

- Is the researcher manipulating, controlling, or using the variable as a subject grouping method?
- Is there a time difference between this variable and the other variable?

For dependent variable:

- Is this variable evaluated as a result of the research?
- Is this variable in this experiment based on another factor?

**Difference Between Independent and Dependent Variables**

Independent Variables | Dependent Variables |

An independent variable is one whose value is never influenced by another variable. | A dependent variable is one whose value is influenced by another variable |

It is the postulated cause. | It is the presumed effect. |

Independent variables are known as predictors or regressors. | Dependent variables are known as predicted variables. |

There are no complicated mathematical processes or observations required for independent variables. | Dependent variables, on the other hand, are derived from longitudinal study or the solution of sophisticated mathematical equations. |

On the graph, independent variables are horizontally positioned. | Dependent variables are vertically positioned on the graph. |

The slightest change in independent variables directly affects the dependent variables. | Any amount of change doesn’t affect the independent variable. |

**Visualizing Independent and Dependent Variables**

Using charts or graphs to depict the outcomes of investigations is a good practice in quantitative research. The independent variable is usually plotted on the x-axis (horizontally), and the dependent variable is mostly plotted on the y-axis (vertically).

The type of visualization you employ is determined by the types of variables in your research questions:

- Researchers use a bar chart or pie chart to depict independent variables.
- A scatter plot or line graph is utilized to plot when your independent and dependent variables are quantitative.

Let us now look at some independent and dependent variables examples, to sum up this lesson.

**Independent and Dependent Variables Examples**

**Example 1: **Let us suppose that an employer wants to check his employee’s performance in the production section of his company. For this, he placed a performance boost award for those who complete their work on time.

The employer is unaware of what will happen. Therefore he predicts that the employees under this condition will work under two conditions:

1. Some of the employees will work overtime to increase their productivity.

2. Others will use tips and tricks to complete the same task on time.

Based on the result of the work done by each employee, the employer will award them with a suitable award (cash).

Therefore,

**Dependent Variable:**

- Work done in a month

**Independent Variables:**

- Overtime by the worker
- Intellectual smartness of the employee

**Example 2: **The income depends on the level of education in India (Higher the income, higher is the educational qualification). Find the independent and dependent variables for this problem.

**Solution:** According to the problem:

Income is directly linked with education, thus

Independent Variable: Education

Dependent Variable: Income

If the income is high, that means an individual’s education must be high and vice versa.