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FRHD 3070 (131)
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Lecture 13

# FRHD 3070 Lecture 13: Lecture 13 Premium

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Department
Family Relations and Human Development
Course Code
FRHD 3070
Professor
Hendry

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Description
Results of Factorial Designs Main Effects Determining if an independent variable has had an effect on the dependent variable while ignoring the other independent variables in the design Main effect - separate effects of each independent variable regardless of the other independent variable For each independent variable, you may have a main effect E.g., A 2 X 2 factorial designs yields 2 main effects, one for each factor A main effect is an outcome that is a consistent difference between levels of an IV. The row mean differences depict the main effect for one factor For instance, we would say theres a main effect for content if we find a statistical difference between the averages for the <1, <5, and >5 groups at both levels of pacing. The column mean differences depict the main effect for the second factor Alternately, we would say theres a main effect for duration if we find a statistical difference between the averages for the different pacing groups at all levels of duration. Interactions Occur when the effect of one independent variable depends on the levels of another independent variable An interaction represents the joint effect of the IVs on the DV. An interaction effect is depicted in a graph by the presence of nonparallel data lines, or lines that cross or appear to cross at some time in the future. How do you detect interactions? 1. Statistical analysis will report on all main effects and interactions. 2. When can't talk about effect on one factor without mentioning the other factor. When you have an interaction it is impossible to describe your results accurately without mentioning both factors. 3. You can identify an interaction in the graphs of group means: whenever there are lines that are not parallel there is an interaction present. Note that an interaction effect can occur when: There are no main effect(s) for any independent variable (as in the above example) There is a main effect for independent variable A but not B; There is a main effect for independent variable B but not A There is main effect for independent variables A and B Why Use a Facto
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