In research work, it is common to observe that correlation and causal research appear simultaneously. Also, it is common to observe that most of the students take both of them as the same thing, and this misconception leads towards the bad end results of the study. If you are also working on a research study and you have to use correlation or causal research, you are supposed to study both of these research types in detail. It is very important to learn about the importance of both research and, similarly, the difference between both types of research. This approach helps you target the right area of discussion, and you can better meet the best results of a study which meets the designed objectives.
As per its importance, this article aims to discuss the importance as well as differences between correlation and causal research in detail.
Why is Correlational Research Important?
In order to make it easy to understand, the research study is divided into two major groups, including an experimental as well as a non-experimental group. From the non-experimental group, one of the types of research is correlational, in which variables play a vibrant role. Correlational research is important to consider whenever you have to work on the relationship between multiple variables for calculating a particular result of the study.
There are three main types of correlational research, and each type has a particular role to play. Based on the problem statement of research and objective, you have to see which of the type is the best fit. Let’s have a look at all three types:
The first type is cohort studies which aim to define the characteristics of research variables. In this type of correlational research, you need to set a specific milestone which acts as a benchmark for detailed discussions. In the case of multiple variables, you have to set a different benchmark for each variable.
The second type of correlational research is cross-sectional studies. In this type, you do not only study multiple variables, but the main task is to work on the relationship between them. There can be a single comparison or more than one as well in other research studies, but cross-sectional studies entertain just one relationship.
The last type of correlational research is case-control studies. The speciality of this type is that you can deal with unknown variables. In other types, you do not get to deal with known variables. Whenever you need to study unknown variables, case-control studies are highlighted. The process of this type is to define a known variable and suppose an unknown variable as well. Here, the comparison is made between the problems of both subjects.
What is the Importance of Causal Research?
Causal research is used in most of the studies which are inclined towards innovation. It plays a vital role in the assessment of marketing-related problems. Furthermore, it does not remain difficult to possess the internal factors and find the solution for the selected problem of study. At an industrial level, the use of causal research is very frequent, which is similar to academic fields.
Before working on this research, it is necessary to grasp the basic points and foundational understanding. First of all, you need to understand that this research is focused on the cause and effects of any factor. While finalizing the factors, you must see that the selected factors alternate with each other. In this way, design relevant experiments for the exploration of objectives. The experiment helps you get the statistical evidence for the problem which is under study. Now, you can make all possible connections, and the detail of cause and effect would be easy to learn.
For learning, the second most important thing is to get all the key terms of causal research. Some of the key terms are mentioned below:
Dependent Variable:
It is the variable which is used in causal research, and it is measurable. The changes in dependent variables are measures with respect to the independent variable. For example, there is a problem statement as “association of tea with the sleep schedule.” Here, sleep schedule is the dependent variable, which has to face changes with respect to tea consumption.
Independent Variable:
The Independent variable is the opposite of the dependent variable used in causal research. It is the variable which causes changes in the other variable and does not experience a change in itself. In the above-discussed example of tea and schedule, the tea is an independent variable.
Hypothesis:
A hypothesis is a testable statement which helps in causal research to study some occurrence. If you find any problem while making a hypothesis, you can get masters dissertation help to craft a perfect hypothesis.
Mention the Differences Between Correlational and Causal Research
The intensity of correlation and causal research makes a major difference between them. First of all, let’s understand the difference, and after that, let’s see how one research leads to another one.
So, the difference is that correlational research is inclined towards finding the relationships. Also, it can be the association of one selected variable of research with another one. There can be the identification of a relationship between two as well as more variables. So, this can be taken as the first basic step of research, which can help you lead towards the other research.
On the other hand, causal research is not the same as correlational one. You can take it as the advanced level required after correlation, but it is different. In the causal study, you have to see how one factor of study is creating effects on something. For example, there is an event happening in Europe. Now the impacts created by that event are the effects in the result of a particular cause.
Final Thoughts
Correlational research and causal research both have great significance in finding the solution to a research problem. It is a problem statement and objective of research which help to decide which one is better. When there is a need to find relations, correlation research is best. In contrast, a causal study is perfect for finding cause and effect.
