![]() ![]() To explore causal relationships between variables But a strong correlation could be useful for making predictions about voting patterns. 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. ![]() ExampleYou want to know if there is any correlation between the number of children people have and which political party they vote for. You want to find out if there is an association between two variables, but you don’t expect to find a causal relationship between them.Ĭorrelational research can provide insights into complex real-world relationships, helping researchers develop theories and make predictions. There are a few situations where correlational research is an appropriate choice. That helps you generalize your findings to real-life situations in an externally valid way. High internal validity: you can confidently draw conclusions about causationĬorrelational research is ideal for gathering data quickly from natural settings. High external validity: you can confidently generalize your conclusions to other populations or settings Limited control is used, so other variables may play a role in the relationshipĮxtraneous variables are controlled so that they can’t impact your variables of interest ![]() Variables are only observed with no manipulation or intervention by researchersĪn independent variable is manipulated and a dependent variable is observed Used to test cause-and-effect relationships between variables Used to test strength of association between variables But there are important differences in data collection methods and the types of conclusions you can draw. Frequently asked questions about correlational researchĬorrelational and experimental research both use quantitative methods to investigate relationships between variables. ![]()
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