Analysis of Variance (ANOVA)

What is the primary advantage of using the analysis of variance (ANOVA) over the t-test?

• ANOVA is more complex and difficult to understand.

• ANOVA allows for the comparison of more than two conditions.

• ANOVA is only used for repeated measures designs.

• ANOVA is only used for independent measures designs.

What does a large value of F in ANOVA indicate?

• There is no effect of the independent variable on the dependent variable.

• There is an effect of the independent variable on the dependent variable, but not which conditions are producing it.

• There is no variability in the scores between conditions.

• There is no variability in the scores due to random factors or error.

What are the assumptions underlying the ANOVA?

• Only that participants are randomly allocated to conditions.

• Only that scores come from normally distributed populations and are measured on an interval scale.

• Only that scores come from populations with equal variances.

• All of the above.

What is the purpose of a post hoc multiple comparison test in ANOVA?

• To determine if there is an effect of the independent variable on the dependent variable.

• To determine which conditions are producing the effect of the independent variable.

• To compare the variance between multiple groups.

• To calculate the value of F.

What is the difference between the one factor independent measures design and the one factor repeated measures design in ANOVA?

• The independent measures design is used for repeated measures, and the repeated measures design is used for independent measures.

• The independent measures design is used for different participants, and the repeated measures design is used for the same participants.

• The independent measures design is used for more than two conditions, and the repeated measures design is used for two conditions.

• The independent measures design is used for continuous data, and the repeated measures design is used for categorical data.