|Positive correlation||Both variables change in the same direction||As height increases, weight also increases|
|Negative correlation||The variables change in opposite directions||As coffee consumption increases, tiredness decreases|
|Zero correlation||There is no relationship between the variables||Coffee consumption is not correlated with height|
When to use a correlational research design
- You want to find out if there is a relationship between two variables, but you don’t expect to find a causal relationship between them.
You want to know if people who have higher incomes are more likely to be vegetarian. You don’t think that income causes vegetarianism (or vice versa), but finding a relationship could lead to a better understanding of the factors that influence or limit people’s dietary choices.
You want to know if there is any correlation between the number of children people have and which political party they vote for. You don’t think having more children causes people to vote differently — it’s more likely that both are influenced by other variables such as age, religion, ideology and socioeconomic status. But a strong correlation could be useful for making predictions about voting patterns.
- You think there is a causal relationship between two variables, but it is impractical or unethical to conduct experimental research that manipulates one of the variables.
You hypothesize that passive smoking causes asthma in children. You can’t do an experiment to test the hypothesis — it would be unethical to deliberately expose some children to passive smoking. But you can do a correlational study to find out if children whose parents smoke are more likely to have asthma than children whose parents don’t smoke.
You want to test the theory that greenhouse gas emissions cause global warming. It is not practically possible to do an experiment that controls global emissions over time, but through observation and large-scale data analysis you can show a strong correlation that supports the theory.
How to do correlational research
The most common data collection methods for this type of research include surveys, observations and secondary data. Academic research often combines various methods. It’s important to carefully choose and plan your methods to ensure the reliability and validity of your results.
A simple way to research the relationship between variables is through surveys and questionnaires. You can conduct surveys online, by mail, by phone, or in person. You ask respondents questions related to the variables you are interested in, and then statistically analyze the responses.
- Quick and flexible
- Responses may not always be honest or accurate
To find out if there is a relationship between vegetarianism and income, you send out a questionnaire about diet to a sample of people from different income brackets. You statistically analyze the responses to determine whether vegetarians generally have higher incomes.
This is a type of field research, where you gather data about a behaviour or phenomenon in its natural environment without intervening.
This method often involves recording, counting, describing and categorizing actions and events. Naturalistic observation can include both qualitative and quantitative elements, but to find correlation, you focus on data that can be analyzed quantitatively (e.g. frequencies, durations, scales and amounts).
- Eliminates researcher influence and respondent inaccuracy that might affect the variables
- Can be time-consuming and unpredictable
To find out if there is a correlation between gender and class participation, you observe college seminars, note the frequency and duration of students’ contributions, and categorize them based on gender. You statistically analyze the data to determine whether men are more likely to speak up in class than women.
Instead of collecting original data, you can also use data that has already been collected for a different purpose, such as official records, polls, or previous studies.
- Allows access to large amounts of data to observe changes over time or space
- The data may be unreliable or incomplete
To find out if working hours are related to mental health, you use official national statistics, health records and scientific studies from several different countries to find data on average working hours and rates of mental illness. You statistically analyze the data to see if countries that work fewer hours have better mental health outcomes.
Correlation and causation
It’s important to remember that correlation does not imply causation. Just because you find a correlation between two things doesn’t mean that one of them causes the other.
You find a strong negative correlation between working hours and mental health: in countries with lower average working hours, people report better mental health. However, this doesn’t prove that lower working hours cause an improvement in mental health. There are many other variables that may influence the relationship, such as average income, access to mental healthcare, and cultural norms.
Although correlational research can’t prove causation, with a large amount of carefully collected and analyzed data, it can strongly support a causal hypothesis. In the examples above, the health effects of passive smoking and the greenhouse effect have been supported by so much robust correlational evidence that a causal relationship is accepted by scientists.