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

A simple effect refers to the in a factorial design.

A 2 × 2 factorial design creates treatment conditions.

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

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

In a two-factor between-subjects analysis of variance, if SSTotal is 1000, SSA is 100, SSB is 200, SSA × B is 300 and SSError is 400, then η2 for factor B = and η2 for the interaction of factors A and B is.

A main effect of an independent variable in a factorial design is defined as the.

Suppose a 2 × 2 between-subjects design had 11 participants randomly assigned to each cell. The df for SSTotal are equal to and the df for SSError are equal to for the analysis of variance of this design.

The mean square for the error term in a two-factor between-subjects analysis of variance is defined as the SSError divided by.

In a two-factor between-subjects analysis of variance, F statistic(s) is/are calculated.

An interaction is analyzed by comparing differences.

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

A statistically significant interaction obtained in a factorial analysis of variance indicates that.

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

The total degrees of freedom in a two-factor between-subjects analysis of variance is defined as the number of minus one.

If the results of a two-factor between-subjects analysis of variance were summarized as F(2, 84) = 3.70, p < .05 for factor A, F(1, 84) = 2.94, p > .05 for factor B, and F(2, 84) = 3.55, p < .05 for the interaction of factors A and B, then you would know that N =)

A two-factor between-subjects analysis of variance is based upon the assumption that the in the populations sampled are.

Post hoc comparisons in a two-factor between-subjects analysis of variance using the Tukey HSD test are used to compare simple effects.

A 3 × 3 design has independent variables with levels for each.

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

The following values of Fobs occurred in a two-factor between-subjects analysis of variance: F(1, 28) for factor A = 3.47, F(1, 28) for factor B = 4.29, and F(1, 28) for the interaction of factors A and B = 4.10. Fcrit(1, 28) = 4.20 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.