What is a factorial design in experimental research?

What is a factorial design in experimental research?

• A. A design that includes two or more dependent variables
• B. A design that evaluates only one independent variable at a time
• C. A design that includes two or more independent variables
• D. A design focusing solely on participant observations

Answer: (C)

A factorial design in experimental research is characterized by the incorporation of two or more independent variables. This approach allows researchers to systematically investigate the effects and interactions of these variables on the dependent variable. By manipulating multiple independent variables simultaneously, researchers can identify how they jointly influence outcomes, providing more comprehensive insights compared to designs focusing on a single independent variable. This design is particularly powerful because it enables the assessment of not only the main effects of each independent variable but also the interaction effects between them. For example, if you were studying the effects of diet and exercise on weight loss, a factorial design would allow you to explore how different combinations of diet types and levels of exercise impact weight loss, resulting in richer and more nuanced findings. In contrast, designs that focus on a single independent variable or those that do not include independent variables at all (like participant observations) would miss this depth of analysis and fail to capture the complexity of real-world scenarios where multiple factors often interact.

A factorial design in experimental research is characterized by the incorporation of two or more independent variables. This approach allows researchers to systematically investigate the effects and interactions of these variables on the dependent variable. By manipulating multiple independent variables simultaneously, researchers can identify how they jointly influence outcomes, providing more comprehensive insights compared to designs focusing on a single independent variable.

This design is particularly powerful because it enables the assessment of not only the main effects of each independent variable but also the interaction effects between them. For example, if you were studying the effects of diet and exercise on weight loss, a factorial design would allow you to explore how different combinations of diet types and levels of exercise impact weight loss, resulting in richer and more nuanced findings.

In contrast, designs that focus on a single independent variable or those that do not include independent variables at all (like participant observations) would miss this depth of analysis and fail to capture the complexity of real-world scenarios where multiple factors often interact.