Question

1. A two-way ANOVA consists of two DVs and one IV.

T

F

2. The purpose of factorial ANOVA is to test the mean differences with respect to some IV.

T

F

3. The two-way ANOVA tests two separate hypotheses simultaneously in one analysis.

T

F

4. Any dependent differences produced by either Factor A or Factor B are called main effects.

T

F

5. The null hypothesis for the main effect states that there is a difference in the scores due to the level of A.

T

F

6. Interaction between factors occurs when the effect of one factor depends on different levels of the other factors.

T

F

7. The null hypothesis for the tests of an interaction effect with a factorial ANOVA states that there is no interaction between Factor A and Factor B.

T

F

8. The validity of the results of a factorial ANOVA is dependent upon three assumptions, one of them being that the distributions of scores on the DV must have equal variances.

T

F

9. Eta squared is commonly viewed as the proportion of variance in the DV explained by the IVs in the sample.

T

F

Answer 1

1. A two-way ANOVA consists of two DVs and one IV.

FALSE. Two-way ANOVA consists of 1 DV and 2 IV. It is mainly used to understand the interaction effect of 2 IV on a DV.

2. The purpose of factorial ANOVA is to test the mean differences with respect to some IV.

TRUE. The two-way ANOVA is a special case of factorial ANOVA. Factorial ANOVA can have more than 2 IV. For 3 IV, it is three-way ANOVA.

3. The two-way ANOVA tests two separate hypotheses simultaneously in one analysis.

FALSE. The two-way ANOVA tests 3 separate hypotheses simultaneously in single analysis. First 2 are of main effects of 2 factors and third is interaction effect between these 2 factors.

4. Any dependent differences produced by either Factor A or Factor B are called main effects.

TRUE. Another effect is an interaction effect between these factors. For 3 way, there are 4 interaction effects. (AB, AC, BC, ABC) and so on.

5. The null hypothesis for the main effect states that there is a difference in the scores due to the level of A.

FALSE. The null hypothesis for main effect states that there is NO SIGNIFICANT DIFFERENCE due to level of factor A, where alternate hypothesis claims that there is a difference in scores due to level of factor A which means factor A is responsible for the difference in DV.

6. Interaction between factors occurs when the effect of one factor depends on different levels of the other factors.

TRUE.

7. The null hypothesis for the tests of an interaction effect with a factorial ANOVA states that there is no interaction between Factor A and Factor B.

TRUE. Null hypothesis here states that interaction between Factor A and Factor B has no relation with difference made in DV.

8. The validity of the results of a factorial ANOVA is dependent upon three assumptions, one of them being that the distributions of scores on the DV must have equal variances.

TRUE. Other 2 assumptions are 1. errors follow Normal Distribution. 2. There is no multicollinearity between independent variables i.e. independent variables are independently distributed.

9. Eta squared is commonly viewed as the proportion of variance in the DV explained by the IVs in the sample.

TRUE. The very purpose of eta-sqaured is to determine the total amount of variance in DV accounted by the IV. It is the proportion of variance in the DV explained by the particular factor. It is calculated as the factor effect sum of squares divided by total sum of squares.