# What is casual relationship in research

### Statistical Language - Correlation and Causation

On the other hand, if there is a causal relationship between two variables, they must be A study shows that there is a negative correlation between a student's . Definition of causal research: The investigation into an issue or topic that looks at the effect of one thing or variable on another. For example relationship. Causal research, also known as explanatory research is conducted in order to identify the extent and nature of cause-and-effect relationships. Causal research .

While there is a relationship between the number of roads built and the number of babies, we don't believe that the relationship is a causal one.

This leads to consideration of what is often termed the third variable problem. In this example, it may be that there is a third variable that is causing both the building of roads and the birthrate, that is causing the correlation we observe. For instance, perhaps the general world economy is responsible for both.

When the economy is good more roads are built in Europe and more children are born in the U. The key lesson here is that you have to be careful when you interpret correlations. If you observe a correlation between the number of hours students use the computer to study and their grade point averages with high computer users getting higher gradesyou cannot assume that the relationship is causal: In this case, the third variable might be socioeconomic status -- richer students who have greater resources at their disposal tend to both use computers and do better in their grades.

It's the resources that drives both use and grades, not computer use that causes the change in the grade point average.

• Australian Bureau of Statistics
• causal research
• Causal research

Patterns of Relationships We have several terms to describe the major different types of patterns one might find in a relationship.

First, there is the case of no relationship at all.

## Casual Relationships

If you know the values on one variable, you don't know anything about the values on the other. For instance, I suspect that there is no relationship between the length of the lifeline on your hand and your grade point average. Then, we have the positive relationship. In a positive relationship, high values on one variable are associated with high values on the other and low values on one are associated with low values on the other.

In this example, we assume an idealized positive relationship between years of education and the salary one might expect to be making. On the other hand a negative relationship implies that high values on one variable are associated with low values on the other.

This is also sometimes termed an inverse relationship. Half of the accounts that become overdrawn in one week are randomly selected and the manager telephones the customer to offer advice. Any difference between the mean account balances after two months of the overdrawn accounts that did and did not receive advice can be causally attributed to the phone calls.

If two variables are causally related, it is possible to conclude that changes to the explanatory variable, X, will have a direct impact on Y.

Non-causal relationships Not all relationships are causal. In non-causal relationships, the relationship that is evident between the two variables is not completely the result of one variable directly affecting the other. In the most extreme case, Two variables can be related to each other without either variable directly affecting the values of the other.

The two diagrams below illustrate mechanisms that result in non-causal relationships between X and Y.

### Causal research - Wikipedia

If two variables are not causally related, it is impossible to tell whether changes to one variable, X, will result in changes to the other variable, Y. For example, the scatterplot below shows data from a sample of towns in a region. The positive correlation between the number of churches and the number of deaths from cancer is an example of a non-causal relationship -- the size of the towns is a lurking variable since larger towns have more churches and also more deaths.

Clearly decreasing the number of churches in a town will not reduce the number of deaths from cancer!