What is the primary difference between correlational and experimental designs in terms of causality?

What is the primary difference between correlational and experimental designs in terms of causality?

• A. Correlational designs manipulate an independent variable to establish causality
• B. Experimental designs manipulate an independent variable and use random assignment to establish causality
• C. Experimental designs assess relationships without manipulation
• D. Correlational designs use random assignment to control for confounds

Answer: (B)

Causality in psychology is demonstrated when changing one variable reliably produces a change in another, while ruling out alternative explanations. Experimental designs achieve this by actively manipulating the independent variable and randomly assigning participants to conditions. The manipulation shows the effect of the specific variable of interest, and random assignment helps ensure groups are equivalent at the start, so observed differences are more likely due to the manipulation rather than preexisting differences or confounding factors. Correlational designs, on the other hand, only measure relationships between variables as they occur naturally; there is no manipulation or random assignment, so you can’t determine which variable causes the other and you can’t reliably rule out third variables. That combination is why the experimental approach—manipulating the IV and using random assignment to establish causality—is the best way to make causal inferences.

Causality in psychology is demonstrated when changing one variable reliably produces a change in another, while ruling out alternative explanations. Experimental designs achieve this by actively manipulating the independent variable and randomly assigning participants to conditions. The manipulation shows the effect of the specific variable of interest, and random assignment helps ensure groups are equivalent at the start, so observed differences are more likely due to the manipulation rather than preexisting differences or confounding factors.

Correlational designs, on the other hand, only measure relationships between variables as they occur naturally; there is no manipulation or random assignment, so you can’t determine which variable causes the other and you can’t reliably rule out third variables. That combination is why the experimental approach—manipulating the IV and using random assignment to establish causality—is the best way to make causal inferences.