What is correlational research

Table of Contents. What is correlational research? Book a Free Demo. Examples of correlational research:. What are different types of correlational research outputs? Positive correlation: A positive correlation indicates that there is a positive relationship between the two variables.

In this kind of relation, as one variable increases, the other variable also increases. For instance, the number of cars a person owns is positively correlated with their income.

More the income, more the number of cars. Negative correlation : A negative correlation indicates that there is a negative relationship between the two variables. In this kind of correlation, as one variable increases, the other variable decreases. For example, there is a negative relationship between levels of stress and life satisfaction. This indicates that as stress levels increase, life satisfaction decreases. Zero correlation: Zero correlation indicates that there is no relationship between the two variables.

A change in one variable does not lead to any changes in the other variable. An example of zero correlation is the relationship between intelligence and height. An increase in height does not lead to any changes in the intelligence of an individual.

Download Market Research Toolkit. Download Now. What are the characteristics of correlational research? Figure 6. For example, the circled point in Figure 6. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other.

A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

The circled point represents a person whose stress score was 10 and who had three physical symptoms.

As Figure 6. A value of 0 means there is no relationship between the two variables. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Those who get too little sleep and those who get too much sleep tend to be more depressed.

Even though Figure 6. Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book. This problem is referred to as restriction of range.

However, if we were to collect data only from to year-olds—represented by the shaded area of Figure 6. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages.

It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the directionality problem. Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise.

If two variables are correlated, it could be because one of them is a cause and the other is an effect. A confounding variable is a third variable that influences other variables to make them seem causally related even though they are not. Instead, there are separate causal links between the confounder and each variable. Even if you statistically control for some potential confounders, there may still be other hidden variables that disguise the relationship between your study variables.

There are many other variables that may influence both variables, such as average income, working conditions, and job insecurity.

A correlational research design investigates relationships between two variables or more without the researcher controlling or manipulating any of them. Controlled experiments establish causality, whereas correlational studies only show associations between variables. In general, correlational research is high in external validity while experimental research is high in internal validity.

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables. A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions. Have a language expert improve your writing. Check your paper for plagiarism in 10 minutes. Do the check. Generate your APA citations for free!

APA Citation Generator. Home Knowledge Base Methodology An introduction to correlational research. An introduction to correlational research Published on July 7, by Pritha Bhandari. 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 Table of contents Correlational vs experimental research When to use correlational research How to collect correlational data How to analyze correlational data Correlation and causation Frequently asked questions about correlational research.

Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviours independently and then showing that the different observers are in close agreement. Another approach to correlational research is the use of archival data , which are data that have already been collected for some other purpose.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences.

To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them, were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism.

These ratings were then averaged to produce an explanatory style score for each participant. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men.

This method is an example of content analysis —a family of systematic approaches to measurement using complex archival data. Just as naturalistic observation requires specifying the behaviours of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data.

These occurrences can then be counted, timed e. Discussion: For each of the following, decide whether it is most likely that the study described is experimental or correlational and explain why. Skip to content Chapter 7: Nonexperimental Research. Define correlational research and give several examples.

Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of nonexperimental research. Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.

Correlational research is not defined by where or how the data are collected. However, some approaches to data collection are strongly associated with correlational research. An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.

A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.

Kanner, A.