Exam 16: Analysis of Variance and

The refers to the fact that ordinarily the assumption in analysis of variance that the categories of the independent variable are fixed.

An advanced analysis of variance procedure in which the effects of one or more metric- scaled extraneous variables are removed from the dependent variable before conducting the ANOVA is called .

The most commonly used measure in ANOVA indicating the relative importance of factors is omega squared, (m²).

ANOVA and ANCOVA can include more than one independent variable and at least one of the independent variables must be categorical.

In one- way ANOVA, the null hypothesis may be tested by _ .

are contrasts made after the analysis.

are determined before conducting the analysis, based on the researcher's theoretical framework.

A test of the significance of the interaction between two or more independent variables is a test of the .

The last step in the procedure for conducting one- way analysis of variance is to test the significance.

The estimated value of m² can be negative, in which case the estimated value of m² is set equal to one.

In one- way analysis of variance, under the null hypothesis, SS_x and SS_error come from different sources of variation.

In examining the differences among means, one- way analysis of variance involves the decomposition of the total variation observed in the independent variable.

When using SPSS Windows, for nonmetric analysis of variance, including the k- sample median test and Kruskal- Wallis one way analysis of variance, the program _ _ should be used.

Categorical independent variables are . The independent variables must all be categorical (nonmetric) to use .

The effects of X on Y is measured by .

Multivariate analysis of variance is appropriate when there are two or more dependent variables that are correlated.

Analysis of covariance is most useful when the covariate is not linearly related to the dependent variable and is not related to the factors.

Which statement about MANOVA is not true?

Draw the ANOVA interactive cases listed below (Figure 16.5 in the text). Name your dependent variable Y and assume that there are two factors, X₁ with three levels (X₁₁, X₁₂, and X₁₃), and X₂ with two levels (X₂₁ and X₂₂). Briefly discuss/describe the interaction shown in each of your drawings. 1. no interaction 2. ordinal interaction 3. disordinal interaction (noncrossover) 4. disordinal interaction (crossover)

is an ANOVA technique used when respondents are exposed to more than one treatment condition and repeated measurements are obtained.