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1

When researchers compare more than two means, analysis of variance is the statistical tool to be used.

2

Analysis of variance reveals the location of differences among more than two means.

3

Following finding a significant effect from analysis of variance or t, a measures such as eta is used to compute effect sizes.

4

Factorial analysis of variance is a test of statistical significance that identifies differences among many levels of a single independent variable.

5

For pairwise comparisons following a significant F ratio, Tukey's HSD is the recommended test.

6

The analysis of variance is used to determine nonlinear effects of independent variables on dependent variables.

7

Nonparametric tests are statistical methods that do not make assumptions about population distributions or population parameters.

8

The chi square test of independence shows the degree of correlation between two "count" variables (nominal level variables whose frequency can only be counted).

9

Multivariate analyses deal with more than one independent variable at a time.

10

Eta or eta squared is used to compute effect sizes from the observed chi square value.

11

Multiple regression correlation is a method of correlating multiple predictors with a single output variable.

12

Look at these results for a study on the effect of humor (or not) among male and female receivers (the sex of receivers variable) on attitude change. At alpha risk of .05, the critical value of F for 1 and 80 degrees of freedom is 3.96

Interpret these results by identifying if the statements to follow are true or false.
There is a significant main effect for the humor variable.

13

Using these same data from the ANOVA table, there is a significant main effect for the receiver sex variable.

14

Using these same data from the ANOVA table, there is a significant interaction between humor use and receiver sex.

15

The total effect size of significant effects in this study is .17.