Question 1

Which of the following is not a problem which post hoc comparisons need to correct for?

Accepted Answer

A)  Familywise error rates 
B)  Per comparison error rates 
C)  Sibling error rates 
D)  Experimentwise error rates 
A)  Familywise error rates 
B)  Per comparison error rates 
C)  Sibling error rates 
D)  Experimentwise error rates

Question 2

What is an interaction effect?

Accepted Answer

A)  Where the main effects are explained by the influence of the dependent variables on the independent variable 
B)  Where the main effects are explained by the influence of the independent variables on the dependent variable 
C)  Where the main effects do not jointly explain all of the influence of the independent variables on the dependent variable. 
D)  None of the above 
A)  Where the main effects are explained by the influence of the dependent variables on the independent variable 
B)  Where the main effects are explained by the influence of the independent variables on the dependent variable 
C)  Where the main effects do not jointly explain all of the influence of the independent variables on the dependent variable. 
D)  None of the above

Question 3

How many dependent variables does a two-way ANOVA have?

Accepted Answer

A)  One 
B)  Two 
C)  Three 
D)  Four 
A)  One 
B)  Two 
C)  Three 
D)  Four

Question 4

Which of the following is not an example of factorial ANOVA design?

Accepted Answer

A)  Experimental 
B)  Independent 
C)  Repeated-measures (related) 
D)  Mixed design 
A)  Experimental 
B)  Independent 
C)  Repeated-measures (related) 
D)  Mixed design

Question 5

When cell means for subgroups are examined, what is being investigated?

Accepted Answer

A)  The nature and direction of the interaction 
B)  The point at which the data has no significance 
C)  The point at which the data is highly significant 
D)  None of the above 
A)  The nature and direction of the interaction 
B)  The point at which the data has no significance 
C)  The point at which the data is highly significant 
D)  None of the above

Which of the following is not a problem which post hoc comparisons need to correct for?

What is an interaction effect?

How many dependent variables does a two-way ANOVA have?

Which of the following is not an example of factorial ANOVA design?

When cell means for subgroups are examined, what is being investigated?

Why Is My Evil Lecturer Forcing Me to Learn Statistics

The Spine of Statistics

The Phoenix of Statistics

The Ibm Spss Statistics Environment

Exploring Data With Graphs

The Beast of Bias

Non-Parametric Models

Correlation

The Linear Model Regression

Comparing Two Means

Moderation, Mediation and Multicategory Predictors

Glm 1: Comparing Several Independent Means

Glm 2: Comparing Means Adjusted for Other Predictors Analysis of Covariance

Glm 4: Repeated-Measures Designs

Glm 5: Mixed Designs

Multivariate Analysis of Variance Manova

Exploratory Factor Analysis

Categorical Outcomes: Chi-Square and Loglinear Analysis

Categorical Outcomes: Logistic Regression

Multilevel Linear Models

Filters

Exam 14: Glm 3: Factorial Designs

Which of the following is not a problem which post hoc comparisons need to correct for?

What is an interaction effect?

How many dependent variables does a two-way ANOVA have?

Which of the following is not an example of factorial ANOVA design?

When cell means for subgroups are examined, what is being investigated?

Why Is My Evil Lecturer Forcing Me to Learn Statistics

The Spine of Statistics

The Phoenix of Statistics

The Ibm Spss Statistics Environment

Exploring Data With Graphs

The Beast of Bias

Non-Parametric Models

Correlation

The Linear Model Regression

Comparing Two Means

Moderation, Mediation and Multicategory Predictors

Glm 1: Comparing Several Independent Means

Glm 2: Comparing Means Adjusted for Other Predictors Analysis of Covariance

Glm 4: Repeated-Measures Designs

Glm 5: Mixed Designs

Multivariate Analysis of Variance Manova

Exploratory Factor Analysis

Categorical Outcomes: Chi-Square and Loglinear Analysis

Categorical Outcomes: Logistic Regression

Multilevel Linear Models

Filters