6.1 Experimental research designs

Experimental research design is a powerful tool for uncovering cause-and-effect relationships in market research. By manipulating variables and measuring outcomes, researchers can isolate the impact of specific factors on consumer behavior and market trends.

Random assignment and control groups are key to ensuring the validity of experimental results. These techniques help eliminate alternative explanations for observed effects, allowing researchers to draw more confident conclusions about the relationships between variables in the marketplace.

Experimental Research Design

Components of experimental design

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• Manipulates one or more independent variables
  • Researcher deliberately changes or influences variables (product packaging, price, advertising message)
• Measures dependent variables
  • Observes outcomes or effects resulting from independent variable manipulation (sales, customer satisfaction, brand preference)
• Randomly assigns participants to experimental conditions
  • Ensures differences between groups are due to independent variable manipulation, not pre-existing differences
• Uses control groups
  • Provides baseline for comparison and isolates effects of independent variables

Independent vs dependent variables

• Independent variables (IVs) manipulated or controlled by researcher
  • Hypothesized causes of changes in dependent variables
  • Examples: product features, pricing strategies, promotional campaigns
• Dependent variables (DVs) measured outcomes or effects
  • Hypothesized to be influenced by independent variables
  • Examples: purchase intention, brand loyalty, market share

Random assignment and control groups

• Random assignment eliminates systematic differences between groups before independent variable manipulation
  • Ensures observed differences are due to experimental manipulation, not pre-existing differences
  • Increases internal validity of experiment
• Control groups provide baseline for comparison
  • Allow researchers to isolate effects of independent variables
  • Help rule out alternative explanations for observed changes in dependent variables

Threats to experimental validity

• Internal validity threats
  • History: external events during experiment affect dependent variables
  • Maturation: natural changes in participants over time affect dependent variables
  • Testing: effects of taking pretest on subsequent measurements
  • Instrumentation: changes in measurement instruments or processes affect dependent variables
• External validity threats
  • Interaction of selection and treatment: results may not generalize to other populations due to unique sample characteristics
  • Interaction of setting and treatment: results may not generalize to other settings or contexts (lab vs real-world)
  • Interaction of history and treatment: results may not generalize to other time periods due to unique historical events