Independent Variable Definition
An independent variable is defines as the variable that is changed or controlled in a scientific experiment. It represents the cause or reason for an outcome.
Independent variables are the variables that the experimenter changes to test their dependent variable. A change in the independent variable directly causes a change in the dependent variable. The effect on the dependent variable is measured and recorded.
Independent Variable Examples
A scientist is testing the effect of light and dark on the behavior of moths by turning a light on and off. The independent variable is the amount of light and the moth's reaction is the dependent variable.
In a study to determine the effect of temperature on plant pigmentation, the independent variable (cause) is the temperature, while the amount of pigment or color is the dependent variable (the effect).
Graphing the Independent Variable
When graphing data for an experiment, the independent variable is plotted on the x-axis, while the dependent variable is recorded on the y-axis. An easy way to keep the two variables straight is to use the acronym DRY MIX, which stands for:
Dependent variable that Responds to change goes on the Y axis
Manipulated or Independent variable goes on the X axis
Practice Identifying the Independent Variable
Students are often asked to identify the independent and dependent variable in an experiment. The difficulty is that the value of both of these variables can change. It's even possible for the dependent variable to remain unchanged in response to controlling the independent variable.
Example: You're asked to identify the independent and dependent variable in an experiment looking to see if there is a relationship between hours of sleep and student test scores.
There are two ways to identify the independent variable. The first is to write the hypothesis and see if it makes sense:
Student test scores have no effect on the number of hours the students sleeps.
The number of hours students sleep have no effect on their test scores.
Only one of these statements makes sense. This type of hypothesis is constructed to state the independent variable followed by the predicted impact on the dependent variable. So, the number of hours of sleep is the independent variable.
The other way to identify the independent variable is more intuitive. Remember, the independent variable is the one the experimenter controls to measures its effect on the dependent variable. A researcher can control the number of hours a student sleeps. On the other hand, the scientist has no control on the students' test scores.
The independent variable always changes in an experiment, even if there is just a control and an experimental group. The dependent variable may or may not change in response to the independent variable. In the example regarding sleep and student test scores, it's possible the data might show no change in test scores, no matter how much sleep students get (although this outcome seems unlikely). The point is that a researcher knows the values of the independent variable. The value of the dependent variable is measured.
The independent variable (IV) is the characteristic of a psychology experiment that is manipulated or changed by researchers, not by other variables in the experiment. For example, in an experiment looking at the effects of studying on test scores, studying would be the independent variable. Researchers are trying to determine if changes to the independent variable (studying) result in significant changes to the dependent variable (the test results).
Identifying the Independent Variable
If you are having trouble identifying the independent variables of an experiment, there are some questions that may help:
Is the variable one that is being manipulated by the experimenters?
Are researchers trying to identify how the variable influences another variable?
Is the variable something that cannot be changed but that is not dependent on other variables in the experiment?
Researchers are interested in investigating the effects of the independent variable on other variables, which are known as dependent variables (DV). The independent variable is one that the researchers either manipulate (such as the amount of something) or that already exists but is not dependent upon other variables (such as the age of the participants).
Identifying the Most Important Independent Variables in Regression Models
You’ve settled on a regression model that contains independent variables that are statistically significant. By interpreting the statistical results, you can understand how changes in the independent variables are related to shifts in the dependent variable. At this point, it’s natural to wonder, “
Which independent variable is the most important?”
Surprisingly, determining which variable is the most important is more complicated than it first appears. For a start, you need to define what you mean by “most important.” The definition should include details about your subject-area and your goals for the regression model. So, there is no one-size fits all definition for the most important independent variable. Furthermore, the methods you use to collect and measure your data can affect the seeming importance of the independent variables.In this blog post, I’ll help you determine which independent variable is the most important while keeping these issues in mind.
First, I’ll reveal surprising statistics that are not related to importance. You don’t want to get tripped up by them! Then, I’ll cover statistical and non-statistical approaches for identifying the most important independent variables in your linear regression model. I’ll also include an example regression model where we’ll try these methods out.
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