A Brief Quiz on Chapter 14
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1When researchers compare more than two means, analysis of variance is the statistical tool to be used.

2Analysis of variance reveals the location of differences among more than two means.

3Following finding a significant effect from analysis of variance or t, a measures such as eta is used to compute effect sizes.

4Factorial analysis of variance is a test of statistical significance that identifies differences among many levels of a single independent variable.

5For pairwise comparisons following a significant F ratio, Tukey's HSD is the recommended test.

6The analysis of variance is used to determine nonlinear effects of independent variables on dependent variables.

7Nonparametric tests are statistical methods that do not make assumptions about population distributions or population parameters.

8The chi square test of independence shows the degree of correlation between two "count" variables (nominal level variables whose frequency can only be counted).

9Multivariate analyses deal with more than one independent variable at a time.

10Eta or eta squared is used to compute effect sizes from the observed chi square value.

11Multiple regression correlation is a method of correlating multiple predictors with a single output variable.

12Look 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
anova1.gif

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

13Using these same data from the ANOVA table, there is a significant main effect for the receiver sex variable.

14Using these same data from the ANOVA table, there is a significant interaction between humor use and receiver sex.

15The total effect size of significant effects in this study is .17.