Complex Experimental Designs and Factorial Designs

30 question-and-answer flashcards summarizing key points about complex experimental and factorial designs, main effects, interactions, participant assignment, and design notation.

Why might a two-level independent variable fail to capture the relationship between an IV and DV?

Because the relationship may be non-linear but monotonic; only two levels cannot reveal such complex patterns.

What are the two main strategies researchers use to go beyond a simple two-level IV design?

(1) Increase the number of levels of a single IV. (2) Increase the number of IVs by using a factorial design.

Give an example reason for increasing the number of IV levels beyond two.

There may be more than two theoretically relevant conditions to compare, such as superordinate, basic, and subordinate category levels.

List two advantages of increasing the number of levels of an IV.

It allows detection of non-linear relationships and increases the design’s sensitivity (it can also help control confounding variables).

List two disadvantages of increasing the number of levels of an IV.

It requires a larger sample size and places greater cognitive load or time demands on participants, especially in within-subjects designs.

What is a factorial design?

A design that includes two or more independent variables, with every level of each variable combined with every level of the others.

When do researchers typically choose to use a factorial design?

When they wish to study the simultaneous effects of at least two IVs or expect that the effect of one IV depends on the level of another.

In a 2 × 2 factorial design, how many experimental conditions are there?

Four conditions (cells).

How is a factorial design usually denoted?

By listing the number of levels in each IV separated by × signs, e.g., 3 × 3 or 5 × 4 × 2.

Define a "main effect" in a factorial design.

The overall effect of one independent variable on the dependent variable, averaged across the levels of the other IV(s).

Define a "simple main effect."

The effect of one IV on the DV at a single level of another IV.

Define an "interaction effect."

A situation in which the effect of one IV on the DV depends on the level of another IV.

In the biased-question example, what main effect was found for question type?

Biased (misleading) questions produced more memory errors than unbiased questions.

In the same example, what main effect was found for investigator knowledge?

Investigators who knew the crime details elicited more memory errors than naïve investigators.

Describe the interaction found in the biased-question example.

Biased questions increased memory errors only when asked by a knowledgeable investigator; with a naïve investigator, question type had little effect.

After finding a significant interaction, what analytical step should follow?

Examine the simple main effects to understand the pattern within each level of the moderating variable.

What graphical pattern indicates no interaction in a 2 × 2 design?

Parallel lines for the two levels of one IV across the levels of the other IV.

What is an IV × PV design?

A factorial design combining a manipulated independent variable (IV) with a measured participant variable (PV), such as personality or demographic traits.

Name the three participant-assignment variants of factorial designs.

Independent groups (between-subjects), repeated measures (within-subjects), and mixed designs.

In an independent-groups factorial design, how are participants assigned?

Each participant is tested in only one cell of the factorial matrix (one combination of IV levels).

In a repeated-measures factorial design, how are participants assigned?

Each participant experiences all levels of at least one IV, providing data for multiple cells.

What is a mixed factorial design?

A design that includes at least one between-subjects IV and at least one within-subjects IV.

State one benefit of using a mixed design.

It controls individual differences for within-subjects factors while avoiding carry-over effects for between-subjects factors.

What are two ways to make a factorial design more complex?

Increase the number of levels within existing IVs or add additional IVs beyond two.

How many experimental conditions does a 2 × 2 × 2 design include?

Eight conditions.

What is a three-way (triple) interaction?

An effect showing that a two-way interaction between two IVs differs across the levels of a third IV.

Provide one challenge of adding more IVs or levels to a design.

The total number of conditions grows quickly, demanding more participants and more complex analyses.

What term describes the pattern where an IV has opposite effects at different levels of another IV?

A crossover interaction.

How can increasing IV levels help neutralize confounding variables?

By including intermediate levels that control potential confounds, clarifying the true IV–DV relationship.

Why does increasing the number of IV levels often require a larger sample in between-subjects designs?

Because each additional condition needs its own participant group to maintain statistical power.