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

H0 is not rejected if Fobs in a two-factor between-subjects analysis of variance is its corresponding critical value.

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

If the independent variables interact in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

In a two-factor between-subjects analysis of variance, the interaction of two independent variables is reflected in the remaining deviation of a mean from a mean after the main effects of each independent variable have been removed.

If factor B produces a main effect in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

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

A main effect mean in a factorial design represents the.

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

In a factorial design a treatment condition represents a.

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

A 3 × 2 between-subjects factorial design with ten scores per cell requires participants.

A distinguishing characteristic of a figure of an interaction is that the lines representing the levels of the independent variables are.

The degrees of freedom for the error in a two-factor between-subjects analysis of variance are given by.

The F statistic for factor A in a two-factor between-subjects analysis of variance is formed by dividing MSA by.

In a 2 × 2 factorial design, there will be a total of cell mean(s) for the analysis of variance.

If $S S _ { \text {Total } } = 150.00 , S S _ { A } = 8.00 , S S _ { B } = 10.00 , S S _ { A } \times B = 12.00 , S S _ { \text {Error } } = 120.00 , d f$ Total $= 65$ , $d f _ { A } = 2 , d f _ { B } = 1 , d f _ {{ A } \times B} = 2$ , and $d f _ { \text {Error } } = 60$ in a two-factor between-subjects analysis of variance, then $M S _ { A } = \ldots$ and $M S _ { \text {Error } } =$

A cell mean in a factorial design is represented by.

A 2 × 2 × 2 factorial design indicates independent variables are used with each variable assuming levels.

An interaction in a factorial design is defined as.

If the results of a two-factor between-subjects analysis of variance were summarized as F(3, 72) = 4.06, p < .05 for factor B, then you would know that the number of levels of factor B was .