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

Suppose an experimenter manipulated type of music and the loudness of the music as independent variables. The results of an analysis of variance for the interaction were reported as F(2, 24) = 2.24, p > .05. Given this

A 3 × 3 factorial design creates treatment conditions.

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

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

If the results for a main effect in an analysis of variance for a 2 × 3 between-subjects design were reported as F(1, 30) = 5.69, p < .05, then you know that there were different treatment conditions and a total of subjects in the experiment.

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

A two-factor between-subjects analysis of variance partitions the total variation into between-groups and within-groups sources.

The degrees of freedom for the interaction of factors A and B in a two-factor between-subjects analysis of variance are given by.

If SSTotal = 500.00, SSA = 150.00, SSB = 50.00, and SSError = 100.00 in a two-factor between-subjects analysis of variance, then SSA × B =.

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

A 2 × 3 factorial design has.

Which of the following is not true if Fobs for the interaction in a two-factor between-subjects analysis of variance is statistically significant?

Factorial designs are research designs in which independent variable(s) is/are simultaneously varied.

The alternative hypothesis for the interaction of factors A and B in a 3 × 4 between-subjects analysis of variance is H1:.

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

The null hypothesis for the interaction of factors A and B in a 3 × 4 between-subjects analysis of variance is H0:.

A 3 × 2 factorial design creates treatment conditions.

A limitation of the one-factor analysis of variance is that it can only be used to analyze research designs with.

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

The effect of one independent variable in a factorial design is called a(n) effect.