Intervening Variable - The Mysterious Middlemen: Simple Explanation with an Example - Urdu/Hindi

Hi and welcome to this video!
Today, we'll be discussing intervening variables in social science research. If you're a researcher in the social sciences, you may have heard of intervening variables, but you may not be exactly sure what they are or how they fit into your research. In this video, we'll define intervening variables, explain their role in research, and provide some examples to help you understand how they work.
So, what is an intervening variable? Simply put, an intervening variable is a factor that can affect the relationship between two other variables. For example, let's say that you're studying the relationship between education level and income. You might find that people with higher levels of education tend to have higher incomes. However, an intervening variable could be something like job experience, which might also affect income. If you take job experience into account, you might find that the relationship between education level and income is not as strong as you thought.
Intervening variables are important in social science research because they help researchers to understand the complexity of real-world phenomena. By taking into account the influence of intervening variables, researchers can better understand the relationships between different variables and draw more accurate conclusions about how they might be related.
So, what are some examples of intervening variables in social science research? Some common examples include factors like age, gender, race, and socioeconomic status. These variables can all affect the relationship between other variables, such as education level and income.
In summary, intervening variables are important to consider in social science research because they can help researchers to better understand the complexity of real-world phenomena. By taking into account the influence of intervening variables, researchers can draw more accurate conclusions about the relationships between different variables.

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