Intervening Variable
Definition:
An intervening variable is a third variable that affects the relationship between two other variables. In other words, it is a variable that lies between the independent and dependent variables in a causal chain. The purpose of an intervention is to change the effect of an independent variable on a dependent variable. It is also known as a mediating variable.
For example, let’s say you are interested in the relationship between reading ability and grades in school. You might find that there is a positive correlation between the two: the more reading ability a student has, the higher their grades tend to be. However, this relationship may be due to another factor, such as intelligence. In this case, intelligence would be an intervening variable.
Intervening variables can either increase or decrease the effect of the independent variable on the dependent variable.
Intervening Variable in Research
In research, an example of an intervening variable would be if a study found that there was a relationship between watching television and obesity, but then another study found that the amount of time spent exercising was also related to obesity. The amount of time spent exercising would be considered an intervening variable.
Intervening Variable in Psychology
Intervening variables are important to consider in psychological research because they can help explain why a certain effect occurs. In some cases, an intervening variable might even be more important than the independent or dependent variable.
In psychology, intervening variables are often studied in experiments. In one experiment, researchers might study how stress affects health by looking at how different levels of stress affect heart rate. However, they would also need to account for other factors that could affect heart rate, such as coffee consumption or exercise. In this case, coffee consumption and exercise would be intervening variables.
Example of Intervening Variable
An Example of Intervening Variable would be: Let’s say we’re interested in the relationship between income and happiness. An intervening variable could be education level, which affects both income and happiness.
In this example, education level would be an intervening variable because it’s affecting the relationship between income and happiness. If we want to accurately predict how happy someone is based on their income, we need to account for education level.
When to use Intervening Variable
There are three main situations when researchers use an intervening variable:
- When the cause-and-effect relationship is not clear.
- When the effect of the independent variable is too weak to be observed.
- When there are multiple independent variables and it is unclear which one is causing the effect.
In each of these cases, using an intervening variable can help researchers understand the complex relationships between different variables.
Purpose of Intervening Variable
The purpose of an intervening variable is to help explain the relationship between the two other variables.
For example, consider the relationship between hours of studying and grades. We might expect that the more hours a student studies, the better their grades will be. However, there are many other factors that can affect grades, such as intelligence and class difficulty. An intervening variable can help us to understand how these other factors affect the relationship between hours of studying and grades.
Advantages of Intervening Variable
Here are some advantages of intervening variable:
- They can help to explain why the effects of some variables are not always linear.
- Intervening variables can also help to explain why the same cause may result in different effects in different situations.
- Additionally, intervening variables can help researchers to understand complex relationships between variables.
- They can help researchers to identify potential mediators and moderators of relationships between variables.
Limitations of Intervening Variable
There are some limitations of intervening variables:
- Can be hard to measure. This is because they are often hidden or unobservable. For example, someone’s attitudes or motivation might be an intervening variable in the relationship between their job satisfaction and performance. But it’s hard to directly measure someone’s attitudes or motivation.
- May not be directly related to the independent variable. For example, a researcher might be interested in the relationship between physical attractiveness and job performance. But it’s possible that there is another variable (such as intelligence) that causes both better performance and better physical attractiveness.
- May have unknown effects on the dependent variable. For example, a researcher might want to know if physical attractiveness affects job performance. But it’s possible that there are other variables (such as intelligence) that affect both physical attractiveness and job performance.
