Exam 14: Two-Factor Analysis of Variance

If the mean and variance are computed for each sample in an independent-measures two-factor experiment, then which of the following types of sample data will tend to produce large F-ratios for the two-factor ANOVA?

If the results of a two-factor ANOVA show no main effect for factor A but show a significant interaction, then the results indicate that factor A has no effect on the subjects' scores.

For a two-factor analysis of variance, the significance of any specific F-ratio is completely independent of the significance of the other F-ratios.

In a two-factor ANOVA, what is the implication of a significant AxB interaction?

For a two-factor experiment with 2 levels of factor A and 3 levels of factor B and n = 10 subjects in each treatment condition, how many participants are in each level of factor A?

A two-factor study compares two different treatment conditions (factor 1) for males and females (factor 2).In this study, the males have an average score of 20 in the first treatment and an average of 25 in the second.The females average 35 in the first treatment and 45 in the second.For this study, there is no interaction.

For a two-factor study with 2 levels of factor A, 2 levels of factor B, and a separate sample of n = 10 participants in each treatment condition, the two means for level A1 are 4 and 1, and the two means for level A2 are 3 and 2.For these data, what is the SS value for factor A (SS_A)?

For an independent-measures two-factor experiment, the bigger the values for the sample variances, the more likely it is that at least one of the F-ratios will be significant.

Obtaining a significant interaction means that both factors A and B have significant main effects.

A two-factor study compares 2 levels of factor A and 3 levels of factor B with a sample of n = 10 participants in each treatment condition.For this study, the F-ratio for factor A has df = 2, 54.