Statistics Final Exam Review

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Questions and Answers

What percentage of the final exam is derived from material covered after the midterm?

60% (correct)

70%

40%

50%

How many multiple choice questions are included in the final exam?

80 (correct)

100

Which of the following types of material is tested the least on the final exam?

Lab only material (correct)

Shared lecture and text material

Lecture only material

Textbook only material

Which of the following is NOT considered a type of hypothesis or design covered in the course?

Qualitative design Signup and view all the answers

Which statistical test is used to analyze factorial designs?

Analysis of Variance (ANOVA) Signup and view all the answers

What does the F-value represent in the context of ANOVA?

The amount of systematic variance relative to unsystematic variance Signup and view all the answers

What is indicated if a difference between means is treated as significant?

The p-value is less than 0.05 Signup and view all the answers

Which of the following patterns indicates a main effect of factor A but no main effect of factor B?

Main effect of A; no main effect B Signup and view all the answers

When interpreting factorial designs, which means are compared to assess success and failure?

Means of Important and Unimportant under Success Signup and view all the answers

What is a primary use of combining true independent variables (IVs) and participant variables (PVs)?

To test the generality of an IV’s effect on the DV for different populations. Signup and view all the answers

What does it mean if the participant variable is described as a 'moderator' in the context of an interaction?

It influences the strength or direction of the effect of the IV on the DV. Signup and view all the answers

Which of the following statements is true regarding causal conclusions in studies involving participant variables?

Causal conclusions cannot be made for effects associated with participant variables. Signup and view all the answers

What is the primary role of a moderator in an experimental context?

To change the strength of association between two other variables Signup and view all the answers

In the factorial design context, which method involves measuring the same participants across different conditions?

Within-groups design Signup and view all the answers

In a 2 x 2 factorial design, how many distinct combinations of the independent variables (IVs) are tested?

4 combinations Signup and view all the answers

Which is a caution to consider when interpreting results that involve participant variables?

Results involving participant variables can lead to misleading causal interpretations. Signup and view all the answers

How many conditions are present in a 2 x 3 factorial design?

6 conditions Signup and view all the answers

What factorial notation corresponds to a design with 2 independent variables, both having 3 levels?

3 x 3 Signup and view all the answers

What is the total number of cells in a 2 x 2 x 2 factorial design?

8 cells Signup and view all the answers

Which of the following describes how to represent the lowest number of conditions possible in factorial notation?

1 x 1 Signup and view all the answers

In a 3 x 4 factorial design, how many levels does each independent variable have?

3 levels for the first IV and 4 for the second Signup and view all the answers

In a factorial design with three factors that each have two levels, what would be its notation?

2 x 2 x 2 Signup and view all the answers

What does an interaction effect indicate in the context of Factor A and Factor B?

The effect of Factor A on DV changes depending on the level of Factor B. Signup and view all the answers

In the interaction hypothesis example involving anxiety and performance, how does task difficulty influence anxiety levels?

Increased anxiety improves performance on easy tasks but decreases performance on difficult tasks. Signup and view all the answers

What is one key characteristic of a main effect in an interaction analysis?

It shows the average effect of one factor, ignoring other factors. Signup and view all the answers

Based on the provided data example, what can be concluded about the effect of task importance on performance levels?

The data indicates no main effect of task importance on performance. Signup and view all the answers

How do parents' reactions influence the relationship between violent media and children's aggression?

Favorable parental reactions amplify the aggression associated with violent media exposure. Signup and view all the answers

What was indicated by the significant main effect of performance level?

Participants reported higher self-esteem when they succeeded compared to when they failed. Signup and view all the answers

What conclusion can be drawn regarding task importance based on the main effect analysis?

Task importance has no significant effect on self-esteem levels. Signup and view all the answers

How did the interaction between performance level and task importance manifest for important tasks?

Higher self-esteem was reported when participants succeeded on important tasks. Signup and view all the answers

Which statement accurately describes the overall interaction effect between performance level and task importance?

The interaction indicated that success had a varying impact on self-esteem based on task type. Signup and view all the answers

What does a significant interaction effect imply in the context of the study?

One variable's effect depends on the level of another variable. Signup and view all the answers

According to the findings, what was the result for self-esteem levels on unimportant tasks?

Self-esteem levels showed no difference between success and failure. Signup and view all the answers

Which of the following statements about the significance of interactions in this study is true?

Interactions are crucial as they reveal differences in marginal means. Signup and view all the answers

What statistical result indicates a significant main effect of performance level?

F (1, 37) = 4.23, p < .05 Signup and view all the answers

Study Notes

Final Exam Information

The final exam is on Saturday, December 7th, from 8:30 AM to 10:30 AM.
Multiple locations are available in BA. Refer to the Exam Information document for specific locations.
The exam is 2 hours long.
The exam is worth 100 points.
80 points are multiple choice.
20 points are short answer (10 questions, 2 points each).
The format of the short answer questions is similar to the midterm format.
No aids are permitted, including calculators.
Material covered includes lecture weeks 1–14, chapters 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13 (pages 401–418), chapter 14, and the statistics module (pages 467–479). Chapter 3 from the price text is also included (PDF version in the course information folder).
The exam emphasizes material covered after the midterm, which accounts for approximately 60% of the exam. This corresponds to lecture weeks 9-14 and text chapters 9-14.
Approximately 80% of the lecture and text shared material will be covered on the exam.
Approximately 10% of the lecture-only material will be tested.
Approximately 10% of the textbook-only material will be tested (by multiple-choice questions).
Approximately 2% of the lab-only material will be tested (by multiple-choice questions).

Week 1 Outline

Simple vs. more complex hypotheses and designs
Testing interaction hypotheses
Factorial designs
Describing features of factorial designs (Terminology: variables, levels, conditions) Independent-groups, within-groups, mixed
Manipulated IVs vs. Participant Variables
Interpreting results of a 2 x 2 factorial design
Example experiment
Types of effects: main effects, interaction
A note on statistical significance
The possible patterns of results
Identifying effects in tables and graphs
Extensions and variations
Increasing the number of levels of an IV
Increasing the number of IVs

Week 1 Outline (Continued)

More on interpreting main effects and interactions
Follow-up tests
Interaction effects are more important
Describing in words
Combining IVs and PVs
Uses of IV x PV designs
Cautions in interpreting results
Assignment to conditions in factorial design
Independent-groups designs (between)
Within-groups designs (within)
Mixed designs (between-within)
Advantages and uses of factorial designs
Advantages of factorial designs compared to one-way designs
Uses of factorial designs
Identifying factorial designs in your reading

Simple Causal Hypotheses

State the effect that one IV will have on a dependent variable (DV).
Tested in "one-way" designs with one IV.
Conclusions are "general" or "unqualified."
Prompts research to specify conditions under which the effect disappears or grows stronger.
Boundary conditions

More Complex Causal Hypotheses

State how two (or more) IVs affect a DV.
Called "interaction hypotheses" because they specify how independent variables "interact" to influence a dependent variable.
Can lead to more "qualified" conclusions about behavior

Interaction Hypothesis: Example (Examples of different hypothesis tests)

The effect that X has on Y depends on Z.
The effect that anxiety has on performance depends on task difficulty.
When tasks are easy, increases in anxiety produce increases in performance.
When tasks are difficult, increases in anxiety produce decreases in performance.
The effect that violent media has on children's aggression depends on their parents' reactions.
When parents react favorably, more violence watched, the more aggressive the child will be.
When parents react unfavorably, more violence watched, the less aggressive the child will be.

Interaction Hypotheses: Testing for Moderators

A moderator is a variable that changes the strength of association between two other variables.
In experiments, a moderator is an independent variable that changes the strength of the effect another IV has on the DV.
- The effect that anxiety has on performance is moderated by task difficulty.
- The effect that media violence has on aggression is moderated by the parents' reactions.

Describing Factorial Designs

A research design that tests the effect of two or more factors (IVs) simultaneously.
Each factor is "crossed" with each other factor, creating all possible combination.
The number of conditions (or "cells") is the product of the number of levels of each factor.

2 x 2 Factorial Design

See tables in document.

2 x 4 Factorial Design

See table in document.

2 x 3 Design, example #1 and #2

See tables in document.

3 x 3 Design Example

See table in document.

2 x 4 Design Example

See table in document.

2 x 2 x 2 Design Example

See table in document.

Interpreting Higher Order Factorial Designs

With three factors, there are three main effects (performance level, task importance, presence of others), three two-way interactions (performance level x task importance, performance level x presence of others, task importance x others), and one three-way interaction (performance level x task importance x others).

Examining 2-way Interactions in a Higher Order Design

Examine each 2-way interaction by ignoring or "collapsing across" the remaining factors.

Examining the 3-way Interaction in a 3-way Design

A 3-way design can provide information about the combined effects of all three factors.
There will be a 3-way interaction if a two-way interaction between two factors differs depending on the other remaining factor.
Ask: Does a two-way interaction differ across the levels of the remaining factor?
Examine the two-way interaction pattern at each level of the remaining factor.

Follow-Up Tests

If the IV has only two levels, no further tests are needed.
If the IV has more than two levels, further tests are needed to interpret the findings.
- Called "post hoc tests" or "multiple comparisons"
Want to determine precisely which of the marginal means differ significantly from each other

Post Hoc Tests

See tables in document.

Describing Main Effects and Interactions in Words

Main effects (without an interaction)
Main effects (with an interaction)
Interaction effects

Example: One Main Effect without an Interaction

There was no significant main effect of task importance.
There was a significant main effect of performance level
The interaction between performance level and task importance was not significant.

Example: One Main Effect with an Interaction

There was no significant main effect of task importance
There was a significant main effect of performance level
The effect of performance level was qualified by a significant interaction between performance level and task importance.

Interactions are More Important than Main Effects

When a study shows both a main effect and an interaction, the interaction is almost always more important.
This interaction qualifies the main effect.
There may be real differences in marginal means, but the more interesting part is the interaction.

Describing an Interaction: Generic Templates

The interaction between factor A and factor B indicates the effect of factor A differs depending on the levels of factor B.
For example, when first level B is present, increases in factor A result in higher scores in DV, and in the contrast, when second level B is present, increases in factor A result in lower scores in DV.

Pattern #6b – Alternate Example

See table in document.

Description of Interactions in Words: Textbook Examples

Describe the interactions.

Recall: Interaction Hypothesis Example

The effect of X on Y depends on Z.
The effect of anxiety on performance depends on task difficulty.
When tasks are easy, increases in anxiety increase performance.
When tasks are difficult, increases in anxiety decrease performance.
The effect that violent media has on children's aggression depends on their parents' reactions.
When parents react favorably, more violence watched, the more aggressive the child will be.
When parents react unfavorably, more violence watched, the less aggressive the child will be.

Combining IVs and PVs

Sometimes called IV x PV designs
Use to test the generality of an IV's effect on the DV for different kinds of people.
Use to test the association between a participant variable (PV) and the DV in different situations
Cautions in interpreting results: cannot draw causal conclusions for effects involving the participant variable; Participant variable might moderate the effect of the IV; participant variable can be called a moderator if there is an interaction. Given examples with Depression diagnosis and Experience with Task (2 variables to the participant).

Assignment to Conditions and Orders

Independent Groups (Between-subjects)
Within-Groups (Repeated Measures)
Mixed-factorial design

Independent-Groups Design

Random assignment of participants to conditions. For example, 2 x 3 design will have 6 different conditions.

Within-Groups Design

Each participant is in each factorial condition. For example, conditions were arranged as a sequence or order, that is measured or assessed across time.

Mixed-Factorial Designs (Between-Within)

A study with at least one independent-groups and one within-groups factor.

Tests of Simple Effects

Simple effects of A at B1
Simple effects of A at B2
Simple effect of B at A1
Simple effect of B at A2

Identifying Factorial Designs in Your Readings

The method section will describe the design of the study (including factorial notation).
The results section will examine whether main effects and interactions were significant.
Look for “it depends” or “only when” to highlight an interaction.
Look for participant variables (age, gender, ethnicity).

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Description

Prepare for your Statistics final exam with this comprehensive quiz that covers key concepts from factorial designs to ANOVA. Test your knowledge on the material, multiple choice questions, and the significance of various hypothesis types. This quiz will help you assess your readiness for the exam and deepen your understanding of the course content.