Back in the 1930s, it was established that smoking cigarettes is in some way correlated with lung cancer. But tobacco companies had a ready reply – “Ok, while that MIGHT be true, a mere correlation doesn’t prove that cigarettes CAUSE lung cancer.”
This example shows both the pros and cons of correlational research. While it can be very useful in preliminary research, allowing us to determine that there’s some type of relationship between two variables. I’s not suitable for proving a cause-and-effect relationship between those variables.
Understanding Correlational Research: Definition, Methods, and Importance
Researchers and companies often rely on correlational research to avoid wasting money. How come? Well, you first want to demonstrate that there’s some kind of correlation between variables before pouring money. And spending time on more substantial research.
But before we go into more detail, let’s start by defining correlational research and the types of correlation that can exist between variables.
What is correlational research?
Correlational research is a non-experimental type of research where we measure two variables to understand. And assess the statistical relationship (correlation) between them without attempting to influence, control, or change the variables.
Since the two variables are in correlation, when you see one variable changing, you can have a good idea of how the other is expected to change. There are three kinds of correlation between variables:
- Positive correlation – When both variables change in the same direction (as height increases, weight also increases)
- Negative correlation – When the two variables change in opposite directions As coffee consumption increases, tiredness decreases
- Zero correlation – When the two variables are in no relation (Coffee consumption is not correlated with height)
Why and when to use correlational research (examples)
There are two main cases when you would prefer to use correlational research rather than experimental:
1. When you are looking to determine whether there is some kind of a relationship between two variables, but you don’t expect it to be causal.
Let’s say that you want to find out whether people with lower income are more likely to smoke cigarettes. While you don’t assume that income causes smoking, you can still find a relationship between the two. Understanding this connection helps you better grasp the factors that influence people’s nicotine intake choices.
Correlational Research 101: What It Is and How to Use It Effectively
Another example — let’s say you’re looking to learn whether there’s any correlation between marital status and political preferences. Even though being married doesn’t cause people to vote in a certain way, both variables are likely influenced by other factors. These include age, ethnicity, ideology, religion, and socioeconomic status. If there’s a correlation between marital status and political views, you can explore these related factors. This will help you try to predict voting patterns more accurately.
2. You might believe there is a causal relationship between variables. However, conducting experiments to manipulate these variables could be unethical or impractical.
For example, you might research whether passive smoking leads to asthma in children. It would be completely unethical to expose children to cigarette smoke on purpose for an experiment.
A better workaround is to conduct correlational research. You can discover whether children of smokers are more likely to have asthma than those whose parents don’t smoke.
How to collect data for correlational research
In correlational research, you are not allowed to manipulate any of the variables. However, you are free to collect data in any way you find most purposeful. Here, we will discuss some of the most reliable data collection methods. These include surveys, naturalistic observation, and secondary (archival) data.
Surveys
The quickest and simplest way to obtain data for correlational research is by using surveys and questionnaires. You can distribute surveys by mail, in person – or most efficiently, online. You can simply send out surveys asking respondents various questions related to variables you’re studying. After you’ve gathered the responses, you can statistically analyze your surveys and determine whether there’s a correlation between the variables or not.
For example, if you’re looking to research the correlation between age and physical activity, you could send out a survey like this to learn more about your respondents’ training routine and habits:
Make sure to send out the survey to a sample of people from different age groups. After you’ve collected your correlational research data, you need to statistically analyze the responses to find out whether people of a certain age tend to be more physically active.
As you could see from all of the examples above, in correlational research, one variable will often come in the form of some piece of demographic information. Ideally, you would already have this data readily-available (from another research someone else did) but in case you need to do it yourself, here’s a demographic survey you can use:
With LeadQuizzes, you can easily create surveys from scratch or use one of our ready-made templates (like the ones above), and it all starts with a risk-free trial.
Naturalistic observation
I’d like to emphasize the word “naturalistic” here. It means that you collect data about a certain phenomenon in its natural environment without manipulating the behavior or intervening in any way.
Naturalistic observation includes recording, counting, describing, and categorizing what you hear and see. Even though it entails both quantitative and qualitative material, for correlational research, you need to focus on quantitative data (such as amounts, durations, frequencies, and more).
Compared to surveys, it can be more reliable (depending on the proficiency of the researcher) but it’s also more time-consuming and difficult to predict and control.
Secondary (archival) data
One way to approach correlational research is by collecting original data. Another, faster and equally valid way is to use data that has already been gathered for another purpose (this could be official records, previous research, various public polls, and so on).
This method of obtaining correlational data works best in cases where you’re looking to research a gradual change or progression over a longer period of time. In other words, it’s much faster and easier to analyze data for the previous 3 years than to start gathering data now and wait for 3 years to complete your study. Of course, this isn’t always applicable.
How to calculate correlation? The Pearson Correlation Coefficient
In statistics, there are several types of correlation coefficients, but the most commonly used and most widely accepted is the Pearson correlation coefficient.
Also known as the Pearson product-moment correlation coefficient, it shows a linear relationship between two variables and can hold a value between +1 and −1 (positive or negative correlation, or no correlation at all).
To learn more about how to calculate and interpret the correlation coefficient, check out this guide on the Pearson correlation coefficient formula. And start doing your own correlational research right away!