Correlational Methods vs. Experimental Methods

by Andrew Gellert

Correlational methodologies and experimental ones are the two approaches to doing research. Experimental studies allow the researcher to control the variables in the study, while correlational ones involve just looking at the data that already exists. Experimental studies allow the researcher to draw conclusions about one variable causing changes in another.

Research Variables

In a basic study, the researcher has two variables of interest -- the dependent variable and the independent variable -- and the researcher believes that the independent variable affects the dependent variable. For example, in a study about how using fertilizer increases the amount of wheat grown on a farm, the amount of fertilizer used is the independent variable that affects the amount of wheat that grows, which is the dependent variable.

Correlational Methods

In a correlation study, the researcher or research team does not have control over the variables in the study. The researcher simply measures the data that she finds in the world. This allows her to see if the two variables are correlated -- whether changes in one are associated with changes in the other. Experimenters in such a study collect existing data, such as economic data from governments, and analyze it using statistical tools. Sometimes, the results of correlation studies can inspire hypothesis that can be tested with a more specific experimental one.

Experimental Methods

In a controlled experiment, the research team has control over the independent variable and other aspects of the experiment. This allows the researchers to make conclusions about whether the independent variable really affects the dependent variable, as opposed to the variables changing at the same time through coincidence. The researcher can also eliminate the effects of other variables. For example, the researcher can add precise amounts of fertilizer to different areas of the same wheat field, and measure the differences in wheat yield, as the other other factors, such as rainfall, sun exposure and soil make-up, are the same.

Differences and Overlap

The strength of the experimental treatment is that it isolates the relationship between the independent and dependent variable. In a correlation study, there might be other influences on the variables that make it hard to measure how strong the relationship between the two really is. Experimental studies are often more expensive and difficult to run. Correlation studies can explore a relationship to see if it is worth the later expense of a controlled experiment, as well as study a larger data set than may be feasible in an experiment. Some researchers will use both methods in a study, conducting an experiment and then carrying out correlation analysis on the results.

About the Author

Andrew Gellert is a graduate student who has written science, business, finance and economics articles for four years. He was also the editor of his own section of his college's newspaper, "The Cowl," and has published in his undergraduate economics department's newsletter.

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