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Questions and Answers
What does a factorial design with a notation of 3x5 indicate?
What does a factorial design with a notation of 3x5 indicate?
In a factorial design, what is meant by an interaction effect?
In a factorial design, what is meant by an interaction effect?
What is the main implication of the presence of an interaction effect in relation to main effects?
What is the main implication of the presence of an interaction effect in relation to main effects?
How is counterbalancing typically treated in mixed factorial designs?
How is counterbalancing typically treated in mixed factorial designs?
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What can be inferred from Jenkins and Dallenbach's (1924) study regarding sleep and memory?
What can be inferred from Jenkins and Dallenbach's (1924) study regarding sleep and memory?
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What does a 2x3 factorial design consist of in terms of levels?
What does a 2x3 factorial design consist of in terms of levels?
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How should a researcher approach the calculation of participants needed for factorial designs?
How should a researcher approach the calculation of participants needed for factorial designs?
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Which statement best defines a main effect in factorial designs?
Which statement best defines a main effect in factorial designs?
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What is the main effect of training type based on the provided hypothetical data?
What is the main effect of training type based on the provided hypothetical data?
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In the context of factorial designs, what does the absence of main effects imply?
In the context of factorial designs, what does the absence of main effects imply?
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What is an outcome when study and test conditions match based on the provided examples?
What is an outcome when study and test conditions match based on the provided examples?
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What defines a mixed factorial design?
What defines a mixed factorial design?
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In a P x E design, what does the P factor represent?
In a P x E design, what does the P factor represent?
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What is the main finding of Research Example 22 with the mixed factorial design?
What is the main finding of Research Example 22 with the mixed factorial design?
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What does an interaction effect suggest about two factors?
What does an interaction effect suggest about two factors?
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Why are nonparallel lines in line graphs significant?
Why are nonparallel lines in line graphs significant?
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Which of the following correctly identifies the outcome for caffeine, aging, and memory study?
Which of the following correctly identifies the outcome for caffeine, aging, and memory study?
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What type of research design involves both subject variables and manipulated variables?
What type of research design involves both subject variables and manipulated variables?
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What does the ceiling or floor effect refer to in research outcomes?
What does the ceiling or floor effect refer to in research outcomes?
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In factorial designs, what does it imply when two main effects are observed without a significant interaction?
In factorial designs, what does it imply when two main effects are observed without a significant interaction?
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What is the significance of using 20 subjects per cell in a 2x2 factorial design?
What is the significance of using 20 subjects per cell in a 2x2 factorial design?
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Study Notes
Factorial Designs
- Factorial designs involve more than one independent variable (IV), also called factors.
- Notation: Digits representing IVs; numerical values indicate the number of levels for each IV. A 2x3 factorial has two IVs, one with 2 levels and the other with 3 levels, resulting in 6 total conditions.
Factorial Matrix
- A matrix (table) displaying data from factorial designs. Illustrative example: a 2x2 design looking at training type (imagery or rote) and presentation rate (2 seconds or 4 seconds).
Main Effects
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The overall effect of one IV, independent of the levels of other IVs.
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Compare row means (one IV's levels) across all levels of another IV. Example: comparing recall scores for imagery vs. rote training, regardless of presentation rate.
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Compare column means (levels of one IV) across all levels of another IV. Example: comparing recall scores for 2-sec vs. 4-sec presentation rate, regardless of training type.
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Calculations: involve finding row and column means. Hypothetical example: Row mean for imagery = 20, for rote = 15; column mean for 2-sec = 14.5, for 4-sec = 20.5.
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Examples:
- Two IV (s); Gender of rater and time period. Men rating women (6.2) > women rating men (5.2); Highest ratings at 12:00 (6.1), then 10:30 (5.8), then 9:00 (5.2). These all show main effects
Interactions
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Exist when the effect of one IV depends on the levels of another IV.
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Example: Course emphasis (lab or lecture) and student major (science or humanities): Lab emphasis better for science majors, and lecture emphasis better for humanities majors.
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Another example: Study conditions (silent or noisy) and test conditions (silent or noisy): Best memory when study and test conditions match.
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Interactions can sometimes mask or make less important main effects.
Types of Factorial Designs
- Independent groups factorial: Each participant is in only one cell of the factorial matrix.
- Repeated measures factorial: Each participant experiences all levels of all factors.
- Mixed factorial: Contains both between-subjects and within-subjects IVs.
P x E Factorial
- Involve a subject variable (P) and a manipulated variable (E).
- Examples:
- Introverts vs. Extroverts and room size.
- Gender and group composition for a math test.
- Driving age (young/old) and cell phone use (with/without).
Participant Recruitment
- Number of participants needed depends upon the design type (between-subjects, within-subjects, or mixed).
- Example: 2x2 independent groups design with 20 participants/cell needs 80 participants. A 2x2 repeated measures design with 20 participants per cell needs 20 per cell for all 4 cells (so 50 participants total).
Statistical Analysis
- Factorial ANOVAs are used to analyze data from factorial designs.
Famous Study (Jenkins & Dallenbach)
- Examined the effect of sleep on memory using a 2x4 repeated measures factorial design (asleep/awake & hours between study/recall). Revealed an interaction effect, suggesting impact of sleep on recall depends on the delay before recall.
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Description
This quiz explores factorial designs, focusing on multiple independent variables (IVs) and their levels. It covers concepts such as factorial matrices and the calculation of main effects, providing examples for better understanding. Test your knowledge on how to analyze data from different training types and presentation rates.
