Correlation and causation research methods are familiar in scientific research. An association between variables is there in correlation, and these variables are inter-dependent. If there is a change in one variable, then the other may also be changed. It is due to the statistical relationship between variables; nevertheless, such co-variation is not based on the causal connection either directly or indirectly. On the contrary, causation is associated with those variables where one can make changes in another variable. In causation, the relationship between variables is based on cause and effect. The major fact is that causation surely implies correlation, while on the other side, a correlation may not imply causation.
After introducing correlation and causation research, there are different types of correlation research. Correlation research is of three types:
- Positive Correlation
- Negative Correlation and
- No correlation
Positive correlation – when there is a positive correlation between two variables, any increase in one variable can have an effect on the other variable. Similarly, if there is any decrease in the variable, then the other variable is also facing a decrease. It is necessary to understand that both are interconnected through variable positions. For instance, if any person holds an equal amount of money as compared to other persons’ property, both have interacted between them.
A negative correlation is totally opposite to positive correlation. In one variable, if there is any increase, then there will be a decrease on the other side. The best example is the crime rate and education. If there is an increased rate of education in any society, then the rate of crime will be comparatively low on the other hand in the society. Similarly, if there is any decrease in school-going children, then after some years, the rate of criminal activities will be high in that society. Crime is directly related to poverty, and when people are unemployed, and they have no value in society, they become criminals to earn money through different criminal approaches.
No Correlation – in the last type there is no correlation exists. If there is any change on one side, it is not necessary that the other side may also have any change. Money and happiness are the best examples of such correlation. If you are facing any issues in correlational research, you can get dissertation proposal writing services.
In correlation and causation research, causation refers to the relationship between cause and effect. If there is any change in one variable, it makes the cause for another variable to be changed. If there is a causation relationship between variables, then the change is related to cause and effect where the cause is an independent variable and the effect is the dependent variable. Causality has three further criteria: a) there is a co-variation where all variables need to vary collectively; b) there is a plausibility where the causation claim must be plausible; and finally, there is c) temporality where causation must be taken place before any effect. Causation research is of two types:
- The law of demand is the first type where demand and price hold negative causal interconnection. In case the price increases, the demand for such product or service will be decreased. On the other hand, if the price decreases, the demand for that product will be increased.
- The second type is about the person’s caloric consumption. Here consumption is an independent variable, while weight is the independent variable. If there is any increase in calorie intake, there is an increase in the person’s weight and vice versa.
Similarities and Differences between Causation and Correlation
In correlation and causation research, there are several differences and similarities and have inverse relationships in some cases:
Correlation is based on the existence of any relationship between variables, particularly association with statistical relations. At the same time, causation also has variable relationships; nevertheless, it is essential to define the meaning of both correlation relation and causation relation before digging deep. According to causation connection, it exists only where a variable can make changes for other variables.
In most cases, causation infers correlation, even so correlation does not indicate any causation. In causal connection, two or more two variables have a statistical relationship and both need to be correlated. It is a precise fact, and for several reasons, that correlation may not infer any causation.
- The major reason that correlation does not imply causation is that in some cases, variables may not have any cause and effect inter-link, and there are some directional issues. Such issues make it hard to manage and determine which variable is defined as dependent and which one is the independent variable. So there may be no possibility of deriving any causation in such a situation.
- The second reason where correlation does not infer the causation is regarding confounding variables issues. In some cases, it is observable that two variables are directly related to each other; nevertheless, their interconnection is due to the third variable.
In correlation and causation research, there is no identified similarity found; however, causation and correlation indirectly have several similarities, but directly there are different from each other. It is due to the fact that correlation makes testing through two variables about the relationship building. Nevertheless, the major issue is that observing two variables with direct movement cannot be considered that one variable instigates the other to happen. It is the main issue which can cause the difference between correlation and causation.
In correlation and causation research, causation may be determined through appropriate designed experiments. In other words, the same groups of variables may generate different treatments with dissimilar results for each group. Researchers can possibly conclude that any treatment can cause an effect, in case the groups may have remarkably different results. Similarly, correlation and causation seem identical; however, considering the difference can conclude the actual variation between them. Correlation is denoted as a simple relationship where one specific action is related to action two; therefore, one event is not necessarily made and causes the happening of another event.