Exam 11: Two-Factor Between-Subjects Analysis of Variance

If an interaction of the independent variables occurs in a two-factor between-subjects analysis of variance, then the simple effects of a factor will be to each other and to the main effect for that factor.

represents the effect of factor A and sampling error in the partitioned score of a subject in a two-factor between-subjects analysis of variance.

represents the total variation in the partitioned score of a subject in a two-factor between-subjects analysis of variance.

A statistically significant main effect in a factorial design is said to be when it cannot be meaningfully interpreted because of the pattern of the interaction obtained.

represents within-cells error variation in the partitioned score of a subject in a two-factor between-subjects analysis of variance.

The one-factor analysis of variance is limited to analyzing research designs using.

An artifactual main effect may occur in a two-factor design because.

Suppose a 3 × 2 between-subjects design had 10 participants randomly assigned to each cell. The df for SSA × B are for the analysis of variance of this design.

A main effect mean for factor A in a factorial design is represented by.

The F statistic for interaction in a two-factor between-subjects analysis of variance is formed by dividing MSError into.

If the results of a two-factor between-subjects analysis of variance were summarized as F(2, 60) = 2.46, p > .05 for factor A, then you would know that the number of levels of factor A was .

A effect of an independent variable in a factorial design is the effect of that independent variable at of the other independent variable.

The term $\bar { X } _ { A B } - \bar { X } _ { A } - \bar { X } _ { B } + \bar { X } _ { G }$ is involved in the computation of SS in a two-factor between-subjects analysis of variance.

The MSA term in a two-factor between-subjects analysis of variance responds to the systematic variation due to factor A and.

In a factorial design, a cell is another term for a(n).

The following values of Fobs occurred in a two-factor between-subjects analysis of variance: F(1, 40) for factor A = 4.76, F(1, 40) for factor B = 3.81, and F(1, 40) for the interaction of factors A and B = 5.03. Fcrit(1, 40) = 4.08 for alpha = .05. In this experiment you would H0 for factor A, H0 for factor B, and H0 for the interaction of factors A and B.

H0 is rejected if Fobs in a two-factor between-subjects analysis of variance is its corresponding critical value.

The mean square for the interaction of factors A and B in a two-factor between subjects analysis of variance is defined as SSA × B divided by.

The difference -is involved in the computation of SS in a two-factor $X - \bar { X } _ { A B }$ between-subjects analysis of variance.

The mean square for factor A in a two-factor between-subjects analysis of variance is defined as SSA divided by.