In experimental research, what is the best way to assess if the changes in the dependent variable are due to the independent variable?

The best way to assess if changes in the dependent variable are due to the independent variable is by manipulating only one factor at a time. This approach, known as controlling variables, ensures that any observed changes can be attributed specifically to the independent variable being tested, rather than to other confounding factors.

When only one variable is manipulated, researchers can isolate the effects of that variable on the dependent variable, allowing for clearer conclusions about the causal relationship. This is fundamental to experimental design because it helps in establishing a direct link between the independent and dependent variables.

The other approaches have their merits, but they do not directly address the isolation of cause and effect to the same extent. For instance, comparing results across diverse experiments could introduce variability that complicates understanding the specific impact of the independent variable. Random assignment helps ensure that participants are evenly distributed across experimental conditions, which controls for pre-existing differences, but it does not eliminate the need to isolate the variable in question. Meanwhile, using multiple independent variables can provide richer data but makes it challenging to determine which variable is influencing the dependent variable. Thus, manipulating only one factor at a time is the most effective method for establishing a clear causal relationship.