The Ultimate Guide to Cluster Sampling
Get a step-by-step guide for choosing the correct representative sample for survey research.
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In statistics, correlation is a measure that demonstrates the extent to which two variables are linearly related. It’s a tool used in research to express relationships between variables without making a statement about cause and effect.
Causation, on the other hand, is a statistical measure of the relationship between two variables where one variable is affected by the other. This takes place when the value of one variable increases or decreases as a result of a change in the other variable.
Correlation and causation are two different concepts that can exist at the same time, although one does not necessarily imply the other. As discussed above, causation occurs when one variable affects another, while correlation simply implies a relationship between the two variables.
Although causation does indicate a relationship between two variables, we cannot always assume causation because an association can take place for a range of different reasons, including:
When studying two variables, A and B, we may notice a correlation between the two. However, this may not be causation as the correlation may be caused by a third variable, variable C.
Sometimes when a correlation is found between A and B, it may be easy to assume that A caused B. However, in some cases, B may have caused A instead.
Other than A, B and even C, there may be many more variables in the equation even if these variables may not be clear or visible to you. This happens when A causes E which causes B, meaning that causation cannot be assumed.
Correlational research is a kind of research design in which a researcher measures two variables to understand and evaluate the relationship between the two, barring the influence of any extraneous variable. A distinctive feature of correlational research is that the researcher collects data on the variables without manipulating them.
There are three main types of correlations, and they are:
A positive correlation between two variables indicates that when there is an increase in the value of one variable, there is also an increase in the value of the other. This works the other way as well; when there is a decrease in the value of one variable, there is also a decrease in the value of the other. Hence, the two variables move in the same direction.
A negative correlation between two variables occurs when the two variables move in opposite directions. It indicates that when there is an increase in one variable, there is a decrease in the other, and vice versa.
When there is no correlation between two variables, when a change is seen in one, there may not be a change in the other.
Correlational research tends to have high external validity, allowing the results of the study to be generalised to real-life settings. However, it also has low internal validity because of the lack of a controlled environment, making it tedious to identify causal relationships between variables.
Causal research, also known as explanatory research, involves the investigation of the cause and effect relationship between two variables. A distinctive feature of causal research is that it can only be conducted through controlled experiments. Without a controlled experiment, causal relationships cannot be truly identified.
In contrast with correlational research, causal research has high internal validity due to the fact that variables are observed and manipulated in a controlled experiment. However, causal research may not have as high an external validity as correlational research as there are many additional variables at play in a real-life setting that may be left unaccounted for in the experiment.
Causal research has two main objectives:
As causal research is experimental in nature, it must have clearly outlined parameters and objectives. Without a research plan and clearly defined objectives, the findings of your study can become unreliable and can potentially have a lot of researcher bias.
Additionally, controlled experiments allow you to eliminate the influence of third variables by employing the use of control groups or random assignment.
Random assignment involves randomly placing study participants into different groups so that each group has a random set of participants. This can equalize participant characteristics across all conditions of the experiment.
A control group, on the other hand, consists of study participants that present the same characteristics (similar treatment or no treatment), allowing you to compare the experimental manipulation across elements.
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