Independent Variables and Dependent Variables Quantile Regression

Introduction to Independent Variables and Dependent Variables

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A variable in the field of research is an object, idea, or any other characteristic which can take any value that you are trying to measure. A variable can be age, blood pressure, height, exam score, sea level, time, etc. 

There are primarily two types of variables used in an experiment – Independent Variables and Dependent Variables.

Independent variable definition

As per the name, an independent variable (IV) stands alone. The value does not change due to the impact of any other variable. The researcher manipulates or changes the independent variable to measure its impact on other variables. 

Independent variables in some cases can already exist like age, but it is not dependent on any other variable.

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Dependent variable definition

Similarly, a dependent variable (DV) as the name suggests depends on other variables. It is the variable that is being tested in the experiment. A researcher measures the outcome of the experiment to see how other variables cause changes in the value of a dependent variable. 

Let’s take the example of “Test Scores”. 

You want to see the effect of studying or sleeping on a test score. In the example, “test score” is the dependent variable. “Studying” or “sleeping” is the independent variable because these factors impact how much a student scores on the test. 

So, in the experiment, you are trying to determine if and how one variable affects the other. Here you can manipulate the independent variable (time of studying) to see if the dependent variable (test score) changes or not.

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Difference between Independent and Dependent Variable

The easiest way to identify which variable in your experiment is the Independent Variable (IV) and which one is the Dependent Variable (DV) is by putting both the variables in the sentence below in a way that makes sense. 

“The IV causes a change in the DV. It is not possible that DV could cause any change in IV.”

In an experiment when you make changes in the Independent Variables your aim is to measure the changes it causes to the Dependent Variables. 

Remember that the dependent variable is affected by the changes you make in the independent variable.

Examples of Independent and Dependent Variables

Let’s explore some examples of dependent and independent variables to understand their properties better.

How does the amount of sleep impact test scores?

  1. Independent Variable: Time spent sleeping before the exam
  2. Dependent Variable: Test Score

What is the effect of fast food on blood pressure?

  1. Independent Variable: Consumption of fast food
  2. Dependent Variable: Blood Pressure

What is the effect of caffeine on sleep?

  1. Independent Variable: the amount of caffeine consumed
  2. Dependent Variable: Sleep

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In Experiment – Independent and Dependent Variables

Independent Variables and Dependent Variables Quantile Regression

In an experiment, the independent variable is the characteristic that is manipulated by the researcher to gauge the effect of the changes on the dependent variable. 

Note that, it is always the resulting change on the dependent variable that is measured by altering the independent variable. 

For example, you want to determine the effect of exposure to classical music on the test scores in math. 

To see the changes in the test score you divide students into two groups. Students in Group A listened to classical music for an hour every day for two months. Students in Group B were not instructed to listen to classical music. 

After two months students of both groups were given a math test. It was seen that Group A performed better than Group B. 

In the experiment, test score in math exam was the dependent variable and the exposure or lack thereof to classical music was the independent variable. 

In an Experiment, while the most common study has one independent variable and one dependent variable, it is also possible to have a different level of each variable. 

    • As a researcher, you may want to learn how a single Independent Variable can impact two different dependent variables. 

For example, you run an experiment to learn how playing video games impact a teenager’s memory as well as their mood. In the experiment, while playing video games is your independent variable, the teenager’s memory, and mood are your two dependent variables. 

    • Similarly, independent variables can have different levels. In some experiments, you may need to use multiple independent variables to see the various effects they may have on the single dependent variable.

For example, you want to see how a healthy diet can help with weight loss. So, you will look for several types of a healthy diet and their impact on weight. 

In this case, different types of diet would be your different levels of the independent variable, while weight loss is the outcome that makes it the dependent variable. 

    • Applying two levels of IV can tell you if it has any effect on the DV.
    • Applying multiple levels of IV can show you how it influences the outcome of DV. 
    • In some cases of experimental research, it is not possible to change the independent variable. 

For example, you want to see whether age impacts weight gain. 

    • Age is your Independent Variable
    • Weight gain is your Dependent Variable

You cannot control the age of the people you are studying to understand its impact on weight gain. So, you compare the factors that had an effect on weight and the factors that did not. 

Comparing the difference in the other factors you can learn the changes in the weight of a person caused by their age.

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Pitfalls to Be Aware Of

Aside from dependent and independent variables, you must be aware of other variables that may influence the result of your experiment. 

Extraneous Variables

Extraneous variables may influence the relationships between the Independent and the Dependent variables. Researchers try to identify these variables in order to control them. 

Confounding Variables

Confounding variables are those variables that cannot be controlled in research. In non-experimental research, there may be other variables that you have not identified. These variables may be influencing changes in the outcome.

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FAQs

The main difference between dependent and independent variables is in the definition.

  • The dependent variable is dependent on other variables. It is the variable that is measured or tested by a researcher.
  • Independent variables in research can be manipulated or altered to see their impact on other variables. 

The best way to identify dependent and independent variables in research is by putting the variables in the sentence “The Independent variable causes a change in Dependent variable.”

Variables are characteristics that take on different values. In experimental research, there are three types of Variables: Independent, Dependent, and Controlled Variables.

When you try to boil a potato, as the temperature of water rises the potato boils faster. Here, the temperature of the water is an Independent Variable and Potato is the Dependent Variable.

An independent variable is often denoted by “x.” It is a variable whose value does not depend on another variable. 

 

An independent variable is the factors or conditions that you can manipulate in an experiment. This variable can directly affect the value of a dependent variable.

A Dependent variable is denoted by “y.” It is a variable whose value depends on that of an independent variable.

In an experiment, a dependent variable is a factor that is observed or measured. When you vary an independent variable, you observe the change in your dependent variable.

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