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When researchers compare more than two means, analysis of variance is the statistical tool to be used.
Analysis of variance reveals the location of differences among more than two means.
Following finding a significant effect from analysis of variance or t, a measures such as eta is used to compute effect sizes.
Factorial analysis of variance is a test of statistical significance that identifies differences among many levels of a single independent variable.
For pairwise comparisons following a significant F ratio, Tukey's HSD is the recommended test.
The analysis of variance is used to determine nonlinear effects of independent variables on dependent variables.
Nonparametric tests are statistical methods that do not make assumptions about population distributions or population parameters.
The chi square test of independence shows the degree of correlation between two "count" variables (nominal level variables whose frequency can only be counted).
Multivariate analyses deal with more than one independent variable at a time.
Eta or eta squared is used to compute effect sizes from the observed chi square value.
Multiple regression correlation is a method of correlating multiple predictors with a single output variable.
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.
Using these same data from the ANOVA table, there is a significant main effect for the receiver sex variable.
Using these same data from the ANOVA table, there is a significant interaction between humor use and receiver sex.
The total effect size of significant effects in this study is .17.