The Nature and Purpose of Correlational Research, Explained

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The Nature and Purpose of Correlational Research

Research Methods in Psychology · Chapter 5: Non-Experimental and Correlational ResearchSection 5.3 · lesson 31 of 9410 min · free with a free account

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In this lesson

Studying how variables move together when manipulation is impossible.

  • Define correlational research within non-experimental methods
  • Describe positive, negative and zero relationships
  • Name the goals correlation can legitimately serve
  • Distinguish bivariate from multivariate analysis

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Everything said in the video, section by section (1,313 words). Read along, or search it.

Non-Experimental and Correlational Research

The Nature of Correlational Research

Correlation is the workhorse of social science, and the most misread idea in it. This section is about what it actually does. Not about what it is usually accused of doing. A correlational study measures variables, and asks whether they move together. It never touches them, and that is exactly the point. By the end you will define correlational research, and place it among the non-experimental methods. You will describe positive, negative, and zero relationships. You will name the goals correlation can legitimately serve, and the one it cannot. And you will separate bivariate analysis from multivariate analysis. That last distinction is where correlational research stops being simple.

What It Is

Measure, Then Look for Association

Start with the definition, carefully. Correlational research measures two or more variables in the same participants. Then it examines whether, and how strongly, those variables are statistically associated. The researcher manipulates nothing. The relationship is observed as it naturally exists. So what does a correlational study actually produce? A description of how the variables move together. Do they rise and fall in step, in opposite directions, or independently of one another? The simplest version examines a single pair of variables. More sophisticated designs examine many variables at once. And these designs are everywhere across the social sciences. Public-health studies linking lifestyle factors to health outcomes. Educational research relating teaching practices to achievement, and economics connecting policy to social indicators.

Three Basic Patterns

Positive, Negative, Zero

When two variables are measured across a sample, the relationship takes one of three forms. Positive, negative, or zero. In a positive relationship, both variables rise together, or fall together. In a negative relationship, they move in opposite directions. In a zero relationship, there is no systematic pattern of co-variation at all. A scatter-plot makes the three shapes immediately visible. Each participant contributes one point, placed by their pair of values. A cloud that slopes up, a cloud that slopes down, or no slope at all. Those three pictures are the whole vocabulary of direction. The second question, strength, is about how tightly the points hug the pattern.

Direction

The Two Classic Examples

Take positive relationships first. Both variables rise together. As one increases, so does the other. As one decreases, so does the other. The classic example in education research is hours of study and exam performance. Students who study more tend, on average, to score higher. Hold on to those two words. On average. A positive relationship is a tendency across a sample, not a promise about any individual. Positive relationships are sometimes called direct relationships. Negative relationships run the other way. As one variable rises, the other falls. The classic example is cardiovascular fitness and resting heart rate. People with greater fitness tend to have lower resting heart rates. Negative relationships are sometimes called inverse relationships.

The Informative Null

When Nothing Moves Together

The third pattern is the one students dismiss too quickly. A zero relationship means two variables show no systematic co-variation. Knowing one variable's value gives you no information about the other. On a scatter-plot the points look like a random cloud, rather than a sloping pattern. Now here is why that matters. Zero relationships are themselves informative. They tell you that two variables you expected to be related are not. That is a finding, not a failure. So do not treat a flat scatter-plot as a wasted study. It is a result, and one worth reporting.

Two Separate Questions

Direction Versus Strength

Now separate two features that beginners routinely merge. Direction and strength are distinct. Direction is positive, negative, or zero. It describes how the variables move together. Strength describes how tightly they do so. A relationship can be positive but weak. It can be negative but strong. Or anything in between. Those are two separate axes of description, and you need both. Reporting only the direction leaves half the description missing. Section five point four develops the machinery for measuring both at once. Correlation coefficients capture direction and strength in a single number.

Legitimate Goals

Five Things Correlation Can Do

So what is correlational research legitimately for? The answer is five goals, and they come up again and again. Description. Prediction. Theory testing. Hypothesis generation. And establishing boundary conditions. Before the details, notice the framing around that list. These are not consolation prizes for an experiment you could not run. Correlational research serves these purposes in their own right. Not merely because experiments are sometimes impossible. Each one is a job a well-run correlational study does properly. So hold the five in mind. We will take them one at a time. Then we will look at the goal that is conspicuously absent.

The Missing Entry

Causation Is Not on the List

Take those five goals one at a time. Description simply identifies that two variables are systematically related. Prediction uses one variable's value to predict another. And notice the striking part. You can do that without knowing the underlying mechanism. A predictor can be useful even when it explains nothing about why. Theory testing examines whether a relationship a theory predicts actually appears in real-world data. Hypothesis generation identifies unexpected relationships that may motivate later experimental work. Establishing boundary conditions shows where a relationship holds, and where it does not. Particular populations, particular settings, particular time periods. Now read the list again and notice what is missing. Establishing causation is not on it, and cannot be. That limitation does not diminish correlational work. It clarifies what the method is for.

Bivariate

The Simplest Correlational Design

Correlational designs vary in how many variables they examine at once. Bivariate correlation examines the relationship between exactly two variables. It is the simplest correlational design. And it is the foundation on which the more complex designs build. A bivariate study might examine hours of sleep and mood. Or income and life satisfaction. The result is a single correlation coefficient, describing how the two variables move together. One number, one relationship, no context. Which is convenient, and also the reason bivariate work is rarely enough. Because the real world does not arrive in tidy pairs.

Multivariate

Three or More Variables at Once

Multivariate correlation examines relationships among three or more variables simultaneously. It is essential for two reasons, and both are worth stating plainly. First, real-world phenomena rarely involve only two variables. Most outcomes are influenced by many factors operating together. Second, multivariate methods allow statistical control of confounding variables. That means examining the relationship between two variables while holding others constant. Read that second point slowly, because it is the one that matters. Statistical control is how correlational research answers the obvious objection. Namely, that some third variable explains the association you found. It cannot do that as decisively as random assignment can. But it is not nothing, and it is most of what social science actually has.

Multivariate Techniques

Five Common Techniques

Five multivariate techniques are worth naming. Partial correlation. The relationship between two variables after removing the influence of a third. Multiple regression. Predicting one variable from several others simultaneously. Mediation analysis. Asking whether a relationship operates through an intervening third variable. Moderation analysis. Asking whether the strength of a relationship depends on another variable. Structural equation modeling. Testing complex networks of hypothesized relationships at once. Mediation and moderation sound similar, and they ask different questions. Mediation asks how the relationship works. Through what intervening variable? Moderation asks when the relationship is strong. For whom, and under what conditions? Same two variables, two entirely different questions about them.

Choosing The Design

When Correlation Is the Right Tool

Finally, when is correlational research the right choice? When the independent variable cannot be ethically manipulated. Studying childhood adversity and adult mental health, for instance. When it cannot practically be manipulated. Gender, age, and birth order cannot be assigned to anyone. When the goal is description rather than causation. Establishing the prevalence and patterns of a relationship in a population. When the goal is prediction rather than explanation. Forecasting outcomes from existing measures. And when the phenomenon must be studied in natural settings, without artificial manipulation. Here is the framing to leave with. When both designs are possible, experiments typically support stronger causal conclusions. When only correlation is possible, the question is not whether to use it. It is how to use it well.

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FAQ

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About The Nature and Purpose of Correlational Research

What is the difference between a positive, negative, and zero correlation?
In a positive correlation, two variables rise and fall together; in a negative correlation, one rises as the other falls; in a zero correlation, there is no systematic pattern. Study hours and exam scores are a classic positive example, while cardiovascular fitness and resting heart rate are negatively related. Positive relationships are also called direct and negative ones inverse. These describe tendencies across a sample, not guarantees for individuals.
What can correlational research be used for?
Correlational research serves five legitimate goals: description, prediction, theory testing, hypothesis generation, and establishing boundary conditions. It can show that variables are related, use one variable to predict another even without knowing the mechanism, check whether relationships a theory predicts appear in real data, uncover unexpected links for later experiments, and show where a relationship holds. Establishing causation is not among its goals.
What is the difference between bivariate and multivariate correlation?
Bivariate correlation examines exactly two variables, while multivariate correlation examines three or more at once. A bivariate study, such as one relating hours of sleep to mood, produces a single coefficient. Multivariate methods, including partial correlation, multiple regression, and structural equation modeling, reflect the fact that most outcomes have many influences and allow statistical control, examining two variables while holding possible confounding variables constant.
What is the difference between mediation and moderation?
Mediation asks how a relationship works, while moderation asks when or for whom it is strong. A mediator is an intervening variable through which one variable is related to another. A moderator is a variable that changes the strength of a relationship, so the link may be stronger in one group or setting than in another. Both are examined with multivariate techniques that analyze three or more variables together.

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Research Methods in Psychology (Psychology 111) carries a NCCRS credit recommendation for 3 semester credits. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.
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