Research in social science involves identifying causes or figuring out the reason for the occurrence of a particular event. Although identifying the valid & precise explanations of the causes & its effect in social science is arduous, it can be accomplished by using causal research design.
Causal research, aiming to identify the cause-and-effect relationship, is commonly used to evaluate the impact of processes, existing norms, etc. This type of research design focuses on a specific event and explains the patterns of relationship between variables. However, the presence of a cause-and-effect relationship can be confirmed if there exists specific causal evidence. In other words, cause-and-effect occurs if the variation in the dependent variable follows the variation in the independent variable.
The establishment of causal relationship involves consideration of five major criteria.
- Association
This is the first criterion for establishing a cause-and-effect relationship in an observed or empirical association between the dependent and independent variables. They must go hand-in-hand or must vary together. I.e., when one variable increases or decreases, the other variable should increase or decrease simultaneously.
For instance, when the quantity of alcohol consumed in a day increases, the chances of liver failure increases considerably. Here, the change in the independent variable is associated with the change in the dependent variable. However, if there is no association, then there exists no causal relationship. For example, empirically, there is no correlation between the increase in crime and the use of the death penalty. Therefore, there is no causal relationship.
- The time order of occurrence of variables
Although the association is vital for a causal relationship, it is not sufficient to achieve the same. It is a must to make sure that the variation in the independent variable occurred prior to the variation in the dependent variable. That is, the cause should come prior to its presumed effect. This is known as the time order of occurrence of variables or temporal priority of the independent variable.
For example, consider the retention of customers of a private bank. If the service of the bank is the cause retention, then the service improvement must be made either before the retention or simultaneously with the retention. This can include employee training etc. resulting in an increase of retention customers in subsequent months.
- Nonspuriousness
Another important criterion for establishing a causal relationship is nonspuriousness. Relationship between two variables is considered to be spurious (false or not genuine) when the change is due to the existence of a third variable.
Consider an example, where the academic knowledge and shoe size of children are measured. From the association results, we can conclude that the older children have large feet and more academic knowledge. Here the third factor, the age affects knowledge & shoe size and ensures they correlate. However, it should be noted that one variable doesn’t cause the other. That is, knowledge does not cause shoe size or vice-versa.
- Determining the causal mechanism
The causal mechanism creates a connection between the variation in the dependent variable and variation in the independent variable, which is hypothesized to a cause. However, it is assumed that there is no causal explanation until a mechanism is identified.
For example, there exists an empirical association between the delinquency and poverty level. The child living in an impoverished home is likely to be involved in petty crime. The reason may be low parenting or inadequate child supervision. Here, we can say that the independent variable has influenced the dependent variable variation, resulting in cause-and-effect.
- Specifying the context in which the effect occurs
The cause is a group of interrelated factors required for effect. After larger content, including variable, no cause will have its effect. Determining the context in which the causal effect occurs, although it is rarely used, it helps in understanding the causal relationship.
For instance, typically, if the employees are paid higher, they indeed increase their productivity or efficiency. However, in non-capital society, the employees would require money to meet their basic needs and work less rather than working hard to earn more pay.
Causal research design, used at the final stage of decision making includes several key advantages such as:
- Determines reasons behind a range of processes
- Associates with higher levels of internal validity as a result of systematic selection of participants
Causal research, the most sophisticated approach, involves experimentation and simulation methods. To effectively use this research design for your study, ensure you are aware of the key concepts & techniques of the elements of experimentation & simulation that are concerned with causal research.