The statistical association between the variables is termed a correlation, whereas the effect of change of one variable on another is called causation.
In research, there is a common phrase that most of us have come across; “correlation does not mean causation.” Though both are related ideas, understanding the difference between correlation and causation will help you critically analyse and interpret the research.
Correlation is used to describe the relation or association between the associated variables of the research. It means a change in one variable would induce a change in the other. Thus, correlation is used as a statistical indicator of the association of the different variables. The changes occur in the variables simultaneously; however, the change is not necessarily due to the link between the variables.
Causation refers to the change in one variable that is induced by the change in the other. It describes a cause and effect relationship between the variables of the research. The change in one variable affects the other, which establishes correlation, influenced by a link between them.
In correlation vs causation, correlation does not mean causation; however, causation always implies correlation.
To understand the difference between correlation and causation, we first need to understand why both cannot be used interchangeably. There are two main factors behind this reason. Identifying these factors is important as it helps to draw sound conclusions from the research. The two factors include
To properly distinguish the correlational vs causal relationship, you will need to use an appropriate research design.
To demonstrate correlational links between the variables, correlational research designs can be used; however, experimental designs must test causation.
The difference between correlation vs causation is made clear with how correlational research is conducted. When conducting correlational research, data is collected without manipulating them.
Example of correlational research
Survey data is collected to determine the relationship between physical activity and happiness levels. The participants were asked how much they work out and if they feel positive and happy.
You conclude that physical activity is positively linked to happiness levels; i.e., those who don’t work out regularly often have lower happiness levels, while those who workout regularly tend to be happier.
There is high scope of external validity while conducting correlational research; therefore, it becomes easy to generalise the findings with the real-world setting. However, these studies suffer from low internal validity, which makes linking the changes in one variable to the other quite challenging.
Such types of research are conducted when performing controlled experiments become too costly, difficult or even unethical. In addition, correlational research is also used to study relationships that are not supposed to be causal.
Example of correlational research
In order to determine if violent video games are linked to aggression in children, data on the children’s video game use and their behaviour is collected. In addition, the parents are asked to report on how many hours their children spend playing violent video games in a week, and a survey is conducted to collect data on their behavioural tendencies.
A positive correlation is found between the two variables; children who spend more time playing violent video games tend to display more aggression.
The third variable problem
To better understand the difference between correlation vs causation, we need to understand the third variable problem. It can be difficult to deduce if a change in one variable induced a change in the other one without conducting controlled experiments. A third variable that can affect the results apart from the variables of interest is called an extraneous variable.
In correlational research, limited control means that the extraneous or confounding variables work as an alternative explanation for the result. Such variables also make the implication that there is a correlational relationship when in fact, there is not any.
Example of extraneous and confounding variables
In the example of the impact of violent video games on the behaviour of children, you might consider “parent attention” to be a confounding variable that can influence how much the children get to play violent video games and how they behave. Poor attention from the parents can lead to the child spending more time playing violent videos games and get aggressive.
But there is no control for it; therefore, only the correlation between the main variables can be drawn.
When there is a correlation between two variables, all that can be said is that the change in one variable occurs simultaneously as the other one.
Spurious correlations: The next step of understanding the difference between correlation vs causation is understanding what a spurious correlation is. When two variables appear connected through hidden third variables or by coincidence, it is known as spurious correlation.
Example of spurious correlation
Statistical studies conducted over decades in Germany and Denmark indicate a positive correlation between the stork population and birth rates. Furthermore, it was noticed that fluctuating numbers of storks in an area affects the number of newborns. So, how can this pattern be accounted for?
To explain this, we need to understand the Theory of the Stork. This theory links the variables to argue that storks physically deliver newborn babies. This study highlights why causation cannot be concluded from correlational research alone.
In reality, this can be explained by the influence of third variables like urban development, weather patterns etc., that helped both the stork and human population in the area to increase, or it can all be coincidental.
When a large enough data set with several variables are used to analyse correlations, finding at least one statistically significant result becomes quite high. Chances are you will make a Type I error in this case. This means that you incorrectly conclude the correlation between the variables based on a distorted sample data set.
Directionality problem: Causation can be established by demonstrating a directional relationship. The relation can be both unidirectional i.e., only one variable affects the other and not the other way around or bidirectional, i.e., both variables affect each other.
Though the possibilities cannot be distinguished by a correctional design, each possible direction can be tested one at a time with the help of an experimental design.
Example of directionality problem
Variables of happiness levels and physical activity can be related in three ways.
Since the researcher has limited control, the directionality of a relationship is not clear in correlational research. Therefore, there is a risk of concluding with the wrong directionality of the relation, called reverse causality, which is to be avoided.
In the next step of understanding the difference between correlation and causation, let us understand what causal research is and how it is conducted.
The true demonstration of the causal links between the variables can be done with controlled experiments. Formal predictions called hypotheses are tested by the experiment to establish causality.
Owing to their high internal validity, these experiments can confidently demonstrate cause and effect relationships.
Directionality is established in one direction since the independent variable is manipulated before the change in a dependent variable is measured.
Example of testing directionality in experimental design
It is believed that higher levels of physical activity can enhance happiness levels. So, this hypothesis can be tested with the help of a controlled experiment. The experiment prohibits physical activity among the participants and then measures the drop in the participants’ happiness levels. To establish directionality, the restrictions must be placed before any change in the happiness levels is noticed.
Bi-directionality can be established by designing a similar experiment that demonstrates whether happiness levels can impact the level of physical activity in the participants.
The influence of third variables can be removed by using control groups or random assignment when conducting a controlled experiment.
Random assignments help to evenly distribute participant characteristics between groups to ensure that the groups are comparable and similar. Thus, experimental manipulation can be compared to a similar treatment or no treatment with the help of a control group. Now that you are familiar with the differences between correlation and causation let us examine how third variables are controlled in an experimental design.
Example of controlling third variables in an experimental design
Each participant is randomly placed in the experimental or the control group. Assigning participants randomly eliminates the influence of third variables like mental health, age on your results.
The control group receives the unrelated intervention, and the experiment group receives intervention on physical activity. As all the variables are kept constant among the groups except for the intervention, any differences the group exhibits would result from the intervention.
The association or relation between two or more variables is called a correlation.
Correlation refers to the relationship between variables, while causation refers to one variable’s effect on the other.
The third variable problem and the directionality problem are two main reasons that correlation does not imply causation.
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