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What does PxE stand for in a mixed factorial design?
What does PxE stand for in a mixed factorial design?
In a PxE mixed factorial design, what type of variable must be a between-subject factor?
In a PxE mixed factorial design, what type of variable must be a between-subject factor?
Why should simple effects analysis be performed only if the interaction effect is statistically significant?
Why should simple effects analysis be performed only if the interaction effect is statistically significant?
Which of the following comparisons is NOT conducted in a simple effects analysis?
Which of the following comparisons is NOT conducted in a simple effects analysis?
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What does a significant interaction effect allow researchers to do?
What does a significant interaction effect allow researchers to do?
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What does a 3 x 5 factorial notation indicate?
What does a 3 x 5 factorial notation indicate?
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How many conditions are present in a 2 x 2 x 4 factorial design?
How many conditions are present in a 2 x 2 x 4 factorial design?
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What is the primary advantage of using factorial designs compared to single-factor designs?
What is the primary advantage of using factorial designs compared to single-factor designs?
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What statistical test is used for a main effect in a two-level single-factor design?
What statistical test is used for a main effect in a two-level single-factor design?
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In factorial designs, how is a main effect determined?
In factorial designs, how is a main effect determined?
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What is the relationship between 'levels' and 'conditions' in factorial designs?
What is the relationship between 'levels' and 'conditions' in factorial designs?
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What is an interaction effect in factorial designs?
What is an interaction effect in factorial designs?
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In a factorial design with 3 IVs, how many main effects can be measured?
In a factorial design with 3 IVs, how many main effects can be measured?
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What is necessary to determine the statistical significance of main and interaction effects in a study?
What is necessary to determine the statistical significance of main and interaction effects in a study?
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In the context of factorial designs, what is the preferred method of data visualization?
In the context of factorial designs, what is the preferred method of data visualization?
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Which statement best describes an interaction effect?
Which statement best describes an interaction effect?
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When examining data in a matrix for inferential statistics, what do you compare to infer potential main effects?
When examining data in a matrix for inferential statistics, what do you compare to infer potential main effects?
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What does it imply if the lines in a line graph depicting interaction effects are not parallel?
What does it imply if the lines in a line graph depicting interaction effects are not parallel?
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When describing the results that indicate higher recall with imagery, which statement reflects the findings?
When describing the results that indicate higher recall with imagery, which statement reflects the findings?
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What does it mean if one can say 'it depends' when comparing grades between two subjects?
What does it mean if one can say 'it depends' when comparing grades between two subjects?
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Why is the interaction effect considered more important than main effects?
Why is the interaction effect considered more important than main effects?
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What is the main effect of an interaction in an ANOVA with three independent variables?
What is the main effect of an interaction in an ANOVA with three independent variables?
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Which of the following effects would be tested in an ANOVA involving independent variables A, B, and C?
Which of the following effects would be tested in an ANOVA involving independent variables A, B, and C?
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What is a ceiling effect in the context of ANOVA?
What is a ceiling effect in the context of ANOVA?
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In a mixed factorial design, what characterizes at least one of the independent variables?
In a mixed factorial design, what characterizes at least one of the independent variables?
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What is the primary challenge faced when implementing a mixed factorial design?
What is the primary challenge faced when implementing a mixed factorial design?
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What distinguishes a PxE factorial design from other types of designs?
What distinguishes a PxE factorial design from other types of designs?
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What is an effect of inconsistent results in an ANOVA?
What is an effect of inconsistent results in an ANOVA?
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How should results be graphed if there are multiple dependent variables in an ANOVA?
How should results be graphed if there are multiple dependent variables in an ANOVA?
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Study Notes
Factorial Design
- A factorial design involves two or more independent variables (IVs).
- The number of IVs determines the number of digits in the factorial notation.
- The value of each digit represents the number of levels for that IV.
- For example, a 2 x 3 factorial design has two IVs, one with two levels and the other with three levels.
Analyzing Effects in Factorial Designs
- Main Effect: The overall effect of a single IV, disregarding the other IVs. There are as many main effects as there are IVs.
- Interaction Effect: When the effect of one IV depends on the level of another IV.
- Factorial designs allow you to investigate interactions, which provide more detailed and interesting results.
Testing for Main Effects
- To determine the main effect of one IV, combine (collapse) the data across all levels of the other IV.
- For example, to examine the main effect of presentation rate (2 vs 4 seconds), collapse data across the different memorization types (imagery vs rote).
Identifying Interaction Effects
- Interaction effects can be visualized by plotting the data in a line graph. If the lines are not parallel, this suggests an interaction.
- For example, you might find that recall is better with imagery than rote memorization, but this advantage is more pronounced at a faster presentation rate.
ANOVA in Factorial Designs
- For factorial designs, ANOVA is used to determine whether main and interaction effects are statistically significant.
- ANOVA uses "f-tests" to test each potential effect.
- For example, a 2x2 factorial design would have tests for main effects of A, B, and the interaction AxB.
Ceiling Effect
- A ceiling effect can occur when the means for different conditions are close to the maximum possible score, giving the illusion of no difference between them.
Types of Factorial Designs
- Completely Between-Subjects: Each condition has a separate set of participants.
- Completely Within-Subjects: The same participants are tested in all conditions.
- Mixed Factorial: At least one IV is between-subjects, while at least one is within-subjects.
PxE Factorial Design
- A PxE factorial design involves both a manipulated IV and a subject variable (often referred to as a person variable).
- This design examines the effects of an environmental manipulation on different groups of people.
Simple Effects Analysis
- If an interaction effect is found to be significant, a simple effects analysis is conducted to further explore and understand the interaction.
- It examines the effect of each IV at each level of the other IV.
- For example, you might compare the effects of different colors on recall at different alcohol levels.
Summary
- Factorial designs are more complex than single-factor designs but offer a more detailed examination of relationships between IVs and the dependent variable.
- They allow for the detection of interactions, which are often considered more important than main effects.
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Description
This quiz covers the concept of factorial design in research, focusing on independent variables, main effects, and interaction effects. Understand how to analyze and interpret factorial designs to gain insights into complex interactions between variables.
