Causal research can be defined as a research method that is used to determine the cause and effect relationship between two variables. This research is used mainly to identify the cause of the given behavior. Using causal research, we decide what variations take place in an independent variable with the change in the dependent variable.
Meaning and explanation of causal research
The meaning of causal research is to determine the relationship between a cause and effect. It is also known as explanatory research. A variation in an independent variable is observed, which is assumed to be causing changes in the dependent variable. The changes in the independent variable are measured due to the variation taking place in the dependent variable.
To get the accurate output, other confounding variables that might influence the results are kept constant while creating the data or are controlled using statistical methods. The nature of causal research is very complicated as a researcher can never be sure that no other hidden variables are influencing the causal relationship between two variables. For example, when a company wants to study the behavior of their consumers towards the changing price of their goods, they use causal research.
They might test the behavior of customers depending on different variables. Still, they can never be sure as there can be some hidden variables that might affect the decisions of customers. For instance, no matter how much caution you to take to get the accurate results but there can always be a few psychological considerations that a consumer might be influencing the concerns of the customer even when he is not aware.
The cause and effect relationship between two variables can only be confirmed if causal evidence exists that support the relationship.
The following are the three components for causal evidence
1. Non-Spurious association
The correlated variation between two variables can only be valid if there is no other variable related to both cause and effect.
2. Temporal sequence
A cause and effect can exclusively be connected if the cause has taken place before the occurrence of the effect. For example, it is not right to assume the cause of a dip in sales was the new entrants in the market when sales were already decreasing before the entrance of new entrants.
3. Concomitant variation
Concomitant variation is referred to as the quantitative change occurred in effect is only because of the quantitative change happened in the cause. That means the variation taking place between two variables must be systematic.
For example, if a company does not put effort into increasing sales by hiring skilled employees or by providing training to the employees, then the credit of an increase in sales can’t be given to the recruitment of experienced employees. There will be other causes which caused an increase in sales.
Advantages of causal research
- Causal research helps identify the causes behind processes taking place in the system. Having this knowledge helps the researcher to take necessary actions to fix the problems or to optimize the outcomes.
- Causal research provides the benefits of replication if there is a need for it.
- Causal research helps identify the impacts of changing the processes and existing methods.
- In causal research, the subjects are selected systematically. Because of this, causal research is helpful for higher levels of internal validity.
Disadvantages of causal research
- The causal research is difficult to administer because sometimes it is not possible to control the effects of all extraneous variables.
- Causal research is one of the most expensive research to conduct. The management requires a great deal of money and time to conduct casual research. Sometimes it costs more than 1 or 2 million dollars to test real-life two advertising campaigns.
- One disadvantage of causal research is that it provides information about your plans to your competitors. For example, they might use the outcomes of your research to identify what you are up to and enter the market before you.
- The findings of causal research are always inaccurate because there will always be a few previous causes or hidden causes that will be affecting the outcome of your research. For example, if you are planning to study the performance of a new advertising campaign in an already established market. Then it is difficult for you to do this as you don’t know the advertising campaign solely influences the performance of your business understudy or it is affected by the previous advertising campaigns.
- The results of your research can be contaminated as there will always be a few people outside your market that might affect the results of your study.
- Another disadvantage of using causal research is that it takes a long time to conduct this research. The accuracy of the causal research is directly proportional to the time you spend on the research as you are required to spend more time to study the long-term effects of a marketing program.
- Coincidence in causal research is the biggest flaw of the research. Sometimes, the coincidence between a cause and an effect can be assumed as a cause and effect relationship.
- You can’t conclude merely depending on the outcomes of the causal research. You are required to conduct other types of research alongside the causal research to confirm its output.
- Sometimes, it is easy for a researcher to identify that two variables are connected, but to determine which variable is the cause and which variable is the effect is challenging for a researcher.
Examples of Causal Research
- To test the market for a new product by collecting data about its sales potential.
- To check the performance or effectiveness of a new advertising campaign to decide whether to continue it or not.
- To measure the improvement in the performance of employees after providing them training on a new skill.
- To examine the effects of re-branding initiatives based on the level of loyalty of customers.
