Correlational Study Overview & Examples

What is a Correlational Study?

A correlational study is an experimental design that evaluates only the correlation between variables. The researchers record measurements but do not control or manipulate the variables. Correlational research is a form of observational study.

A correlation indicates that as the value of one variable increases, the other tends to change in a specific direction:

• Positive correlation: Two variables increase or decrease together (as height increases, weight tends to increase).
• Negative correlation: As one variable increases, the other tends to decrease (as school absences increase, grades tend to fall).
• No correlation: No relationship exists between the two variables. As one increases, the other does not change in a specific direction (as absences increase, height doesn’t tend to increase or decrease).

For example, researchers conducting correlational research explored the relationship between social media usage and levels of anxiety in young adults. Participants reported their demographic information and daily time on various social media platforms and completed a standardized anxiety assessment tool.

The correlational study looked for relationships between social media usage and anxiety. Is increased social media usage associated with higher anxiety? Is it worse for particular demographics?

Learn more about Interpreting Correlation.

Using Correlational Research

Correlational research design is crucial in various disciplines, notably psychology and medicine. This type of design is generally cheaper, easier, and quicker to conduct than an experiment because the researchers don’t control any variables or conditions. Consequently, these studies often serve as an initial assessment, especially when random assignment and controlling variables for a true experiment are not feasible or unethical.

However, an unfortunate aspect of a correlational study is its limitation in establishing causation. While these studies can reveal connections between variables, they cannot prove that altering one variable will cause changes in another. Hence, correlational research can determine whether relationships exist but cannot confirm causality.

Remember, correlation doesn’t necessarily imply causation!

Correlational Study vs Experiment

The difference between the two designs is simple.

In a correlational study, the researchers don’t systematically control any variables. They’re simply observing events and do not want to influence outcomes.

In an experiment, researchers manipulate variables and explicitly hope to affect the outcomes. For example, they might control the treatment condition by giving a medication or placebo to each subject. They also randomly assign subjects to the control and treatment groups, which helps establish causality.

Learn more about Randomized Controlled Trials (RCTs), which statisticians consider to be true experiments.

Types of Correlation Studies and Examples

Researchers divide these studies into three broad types.

Secondary Data Sources

One approach to correlational research is to utilize pre-existing data, which may include official records, public polls, or data from earlier studies. This method can be cost-effective and time-efficient because other researchers have already gathered the data. These existing data sources can provide large sample sizes and longitudinal data, thereby showing relationship trends.

However, it also comes with potential drawbacks. The data may be incomplete or irrelevant to the new research question. Additionally, as a researcher, you won’t have control over the original data collection methods, potentially impacting the data’s reliability and validity.

Using existing data makes this approach a retrospective study.

Surveys in Correlation Research

Surveys are a great way to collect data for correlational studies while using a consistent instrument across all respondents. You can use various formats, such as in-person, online, and by phone. And you can ask the questions necessary to obtain the particular variables you need for your project. In short, it’s easy to customize surveys to match your study’s requirements.

However, you’ll need to carefully word all the questions to be clear and not introduce bias in the results. This process can take multiple iterations and pilot studies to produce the finished survey.

For example, you can use a survey to find correlations between various demographic variables and political opinions.

Naturalistic Observation

Naturalistic observation is a method of collecting field data for a correlational study. Researchers observe and measure variables in a natural environment. The process can include counting events, categorizing behavior, and describing outcomes without interfering with the activities.

For example, researchers might observe and record children’s behavior after watching television. Does a relationship exist between the type of television program and behaviors?

Naturalistic observations occur in a prospective study.

Analyzing Data from a Correlational Study

Statistical analysis of correlational research frequently involves correlation and regression analysis.

A correlation coefficient describes the strength and direction of the relationship between two variables with a single number.

Regression analysis can evaluate how multiple variables relate to a single outcome. For example, in the social media correlational study example, how do the demographic variables and daily social media usage collectively correlate with anxiety?

Reference

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research. Nurse Researcher. 2016;23(6):20-25. doi:10.7748/nr.2016.e1382