Complex Experimental Designs

Features of Complex Experimental Designs

• Complex experimental designs have features that provide richer data:

  • Multiple Levels of Independent Variable: Allows investigation of variations (e.g., Snacking behavior with IV: 2, 4, 8 people).

  • Multiple Independent Variables (Factorial Design): Investigates interactions (e.g., Type of task: Solitary vs. Cooperative).

Importance of Levels in Independent Variables

• Impact on Information Yielded:

  • More levels can identify complex relationships (e.g., Caffeine and test performance).

  • Example questions:

  • Is caffeine beneficial at all levels?

  • How does test difficulty affect caffeine’s efficacy?

• Minimum Levels for Curvilinear Relationships:

  • Three levels needed for detecting:

  • Inverted-U relationships;

  • Positive monotonic relationships.

Understanding Linear and Monotonic Functions

• Monotonic: Dart Throwing Scores Example:

  • Scores increase with more mental practice but not at a constant rate.

Variables and Their Relationships

• **Dependent and Independent Variables: **

  • Curvilinear relationships can portray complex scenarios (e.g., child IQ vs family size).

Types of Experimental Designs

1. Simple Experimental Design:

   • Only one IV with a control group.

2. Factorial Design:

   • Multiple IVs (e.g., 2 x 2 design:

     • Example: Test difficulty (low/high) x caffeine (present/absent).

3. More Complex Factorial Designs:

   • Include additional variables like Age and Time of day (e.g., 2 x 3 x 4 design).

Main Effects and Interactions in Factorial Designs

• Main Effect:

  • The impact of one independent variable viewed independently of others.

  • Assess if values vary significantly across levels.

• Interaction:

  • Examines if one IV’s effect is modified by another IV (lines intersect in plots).

Example of Interaction

• Paradox of Choice:

  • Differences in interactions can lead to varying outcomes based on categorical grouping (e.g., array and gender).

Graphical Representation of Data

• Lines vs Bars:

  • Line graphs for continuous data, bar graphs for categorical.

Outcomes of Factorial Designs

• Potential Outcomes:

  • Main effects for A and B, or interaction between A and B.

• Analysis Techniques:

  • ANOVA for determining significance of main effects and interactions.

Designing Experiments: Participant Considerations

1. Independent Groups (Between-Subjects) Design:

   • Requires largest number of participants (different groups for each condition).

2. Repeated Measures (Within-Subjects) Design:

   • Fewest participants needed (same group across conditions).

3. Mixed Factorial Design:

   • Combines independent and repeated measures.

Examples of Mixed Designs

• Study on Alcohol and Stress:

  • Independent Groups: Separate groups for different combinations of alcohol and stress levels.

  • Repeated Measures: Same participants experience different conditions.

  • Mixed Design: Two groups experiencing varied conditions of alcohol and stress.

Practical Considerations in Complex Factorial Designs

• Incorporate factors like cost and time while ensuring clear interpretations for all variable interactions.