# SAGE Journal Articles

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Summary/Abstract: This is the second in a series on design of experiments that started in the last issue. Here we introduce the basic idea of a factorial design using an invented, but at least qualitatively realistic, example about baking bread.

#### Questions to Consider

1. What are the fundamental differences between a single factor experimental design and a factorial design?

2. When we say that there is an interaction between two factors, this means that the effect on the response of changing one factor depends on:

1. the type of experimental design.
2. the level of the other one.
3. one level of the other one.
4. an average of the other one.

3. What are some advantages of the factorial experiment?

1. The main effects provide efficient estimates of quantities.
2. You can see if there is an interaction.
3. A and B.
4. None of the above.

Summary/Abstract: The authors provide a cautionary note on reporting accurate eta-squared values from multifactor analysis of variance (ANOVA) designs. They reinforce the distinction between classical and partial eta-squared as measures of strength of association. They provide examples from articles published in premier psychology journals in which the authors erroneously reported partial eta-squared values as representing classical eta-squared values. Finally, they discuss broader impacts of inaccurately reported eta-squared values for theory development, meta-analytic reviews, and intervention programs.

#### Questions to Consider

1. Compare and contrast classical eta-squared and partial eta-squared.

2. Which is not a measure of strength of association when using ANOVA to analyze data?

1. eta-squared
2. omega-squared
3. beta-squared
4. epsilon-squared

3. Assuming nonerror sources accounted for 100% of the total variation, the authors believe that the classical eta-squared values were inflated by ___.

1. 34%
2. 46%
3. 53%
4. 65%