Variables

Intervening Variable – Definition, Types and Example

Intervening Variable

Intervening Variable

Definition:

An intervening variable, also known as a mediating variable, is a hypothetical construct that explains the relationship between two other variables. It is a variable that comes between the independent variable (cause) and the dependent variable (effect) in a causal relationship, and helps to explain how the independent variable influences the dependent variable.

In other words, an intervening variable is a variable that exists between the independent variable and the dependent variable, and it helps to explain how the independent variable affects the dependent variable.

Types of Intervening Variable

There are two main types of intervening variables: mediating variables and moderating variables.

Mediating Variables

A mediating variable is an intervening variable that explains the relationship between an independent variable and a dependent variable. It occurs when the independent variable affects the mediating variable, which in turn affects the dependent variable. For example, in the relationship between smoking and lung cancer, the mediating variable could be the damage to the lungs caused by smoking. Smoking damages the lungs, which increases the likelihood of developing lung cancer.

Moderating Variables

A moderating variable is an intervening variable that affects the strength or direction of the relationship between an independent variable and a dependent variable. It occurs when the relationship between the independent variable and the dependent variable depends on the level of the moderating variable. For example, in the relationship between stress and performance, the moderating variable could be the level of social support. High levels of social support can moderate the negative effects of stress on performance, whereas low levels of social support can exacerbate those negative effects.

Examples of Intervening Variable

Here are some examples of intervening variables:

  • Stress: Suppose you are investigating the relationship between workload and productivity in the workplace. However, you find that stress plays a mediating role in this relationship, meaning that as workload increases, so does stress, and as stress increases, productivity decreases.
  • Education: Let’s say you are researching the effect of parental income on a child’s academic performance. Education could be an intervening variable because it plays a role in how income affects academic performance. Children from families with higher income levels may have access to better schools and resources, which can lead to better academic performance.
  • Self-esteem: Suppose you are studying the relationship between bullying and mental health. Self-esteem could be an intervening variable because bullying can lower one’s self-esteem, which in turn can lead to depression and anxiety.
  • Motivation: Let’s say you are investigating the effect of rewards on performance in the workplace. Motivation could be an intervening variable because rewards can increase motivation, which in turn can lead to higher performance levels.
  • Social support: Suppose you are studying the effect of a new treatment for a chronic illness. Social support could be an intervening variable because individuals who receive social support may be more likely to adhere to the treatment regimen, which in turn can lead to better health outcomes.

Applications of Intervening Variable

Here are some applications of intervening variables in various fields:

  • Psychology: In psychology, intervening variables are used to understand how a particular behavior is influenced by internal and external factors. For example, if researchers want to understand how stress affects academic performance, they may measure the level of stress (intervening variable) and its impact on academic performance.
  • Education: In education, intervening variables can be used to understand how different teaching methods affect student learning. For example, if researchers want to understand how a particular teaching method improves student performance, they may measure the intervening variables, such as motivation or engagement, and their effect on academic achievement.
  • Health: In health research, intervening variables can be used to understand how a particular intervention affects health outcomes. For example, if researchers want to understand how a new medication affects blood pressure, they may measure intervening variables such as heart rate or cholesterol levels to help explain how the medication works.
  • Marketing: In marketing, intervening variables are used to understand how advertising and other marketing efforts affect consumer behavior. For example, if a company wants to understand how a new advertising campaign affects consumer purchasing decisions, they may measure intervening variables such as brand awareness or consumer attitudes towards the product.
  • Economics: In economics, intervening variables can be used to understand how changes in one economic factor affect another. For example, if economists want to understand how changes in interest rates affect consumer spending, they may measure intervening variables such as income or savings rates to help explain the relationship between the two.
  • Testing theories: Researchers may use intervening variables to test theories about how certain factors influence outcomes. For example, a researcher may hypothesize that social support mediates the relationship between stress and depression. By measuring social support as an intervening variable, the researcher can test whether the data supports the theory.

Purpose of Intervening Variable

The purpose of intervening variables is to provide a theoretical explanation for the relationship between the independent variable and the dependent variable. They help to explain how and why the independent variable affects the dependent variable by identifying the underlying mechanisms or processes that link the two variables.

By identifying an intervening variable, researchers can better understand the causal relationships between variables and can develop more accurate models and theories. Intervening variables also allow researchers to test the mechanisms through which the independent variable affects the dependent variable, providing insights into how interventions can be developed to bring about desired changes.

When to use Intervening Variable

Here are some situations where intervening variables may be useful:

  • When the relationship between two variables is complex and difficult to understand: In such cases, an intervening variable can help to clarify the relationship by identifying the underlying mechanisms that link the two variables.
  • When there are multiple potential causes for the dependent variable: In such cases, an intervening variable can help to identify the specific factor or factors that are responsible for the relationship between the independent variable and the dependent variable.
  • When the independent variable has an indirect effect on the dependent variable: In such cases, an intervening variable can help to identify the specific intermediate step or steps that link the independent variable to the dependent variable.

Advantages of Intervening Variable

Here are some of the main advantages of using intervening variables:

  • Provides a deeper understanding of the relationship between variables: Intervening variables help to explain the underlying mechanisms or processes that link the independent variable to the dependent variable, providing a more in-depth understanding of the relationship.
  • Tests causal relationships: Intervening variables allow researchers to test the causal relationship between the independent variable and the dependent variable by identifying the intermediate steps or processes that link the two variables.
  • Improves the accuracy of predictions: By identifying and including intervening variables in the analysis, researchers can create more accurate models and predictions about the relationship between variables.
  • Helps to develop interventions: By identifying the mechanisms that link the independent variable to the dependent variable, intervening variables can inform the development of interventions or programs to bring about desired changes.
  • Reduces the risk of spurious relationships: Intervening variables help to clarify the relationship between variables by accounting for other factors that may influence the relationship, reducing the risk of spurious or false relationships.

Limitations of Intervening Variable

Here are some of the main limitations of intervening variables:

  • Difficulty in identifying and measuring intervening variables: Intervening variables can be difficult to identify and measure accurately, which can lead to measurement error and bias in the analysis.
  • Over-reliance on statistical techniques: The identification of intervening variables often relies on statistical techniques, such as regression analysis, which can be limited by the assumptions and limitations of the statistical model.
  • Complexity of relationships: Intervening variables are most useful in situations where the relationship between the independent variable and the dependent variable is complex and not well understood. However, in some cases, the relationship may be simple, and the use of intervening variables may add unnecessary complexity to the analysis.
  • Lack of generalizability: The relationship between intervening variables and the dependent variable may be specific to the particular context or population being studied, limiting the generalizability of the findings to other contexts or populations.
  • Difficulty in establishing causality: While intervening variables can help to test causal relationships between variables, establishing causality can be challenging, and it may be difficult to rule out alternative explanations for the observed relationships.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer