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Causation is an important and widely used term in research, and it refers to the phenomenon that causes a change in a second event or action. In research, when we say two variables have a causal relationship (or a cause-and-effect relationship), we mean that a change in one variable (known as the independent variable) causes a change in the other (the dependent variable).
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Causal research, also referred to as explanatory research, is used to identify the extent of the cause-and-effect relationship between two variables. Experiments are one of the most popular methods of carrying out causal research through primary data.
There are three integral components to causal relationships:
Although causation and correlation can exist at the same time, it is important to note that correlation does not imply causation. Correlation simply means that there is a statistical association or pattern between two variables, while causation not only implies a specific kind of relationship, known as a cause-and-effect relationship. This means that a change in one variable is causing a change in the other.
There are two main reasons why correlation does not imply causation:
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Causation occurs when a change in one variable (the independent variable) leads to a change in the other (the dependent variable).
Correlation between two variables simply implies a statistical association between the two. Causation, on the other hand, implies not only that the two are related, but that one causes a change in the other.
Correlation does not always imply causation for the following reasons;
Causal research involves the investigation of the relationship between two variables, dependent and independent.
The relationship between two variables can only be a causal one if it satisfies the three following conditions;
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