UW-Madison PSYCH 210 - Factorial ANOVA and Correlation (3 pages)

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Factorial ANOVA and Correlation

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Factorial ANOVA and Correlation


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Lecture Note
University of Wisconsin, Madison
Psych 210 - Basic Statistics for Psychology

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PSYCH 210 Lecture 25 Outline of Last Lecture I Factorial ANOVA a Calculational example with hypothesis testing steps Outline of Current Lecture I Factorial ANOVA a Interpretation of interaction b Assumptions II Correlation Current Lecture I II Hmwk 10 due Friday at 1 00pm a Practice problems on Learn UW b Review Session Saturday 1 2 30 c Exam Sunday 2 45pm in room 121 Psychology i SPSS Assignment can be turned in during exam Factorial ANOVA a Hypothesis Testing Steps i For the interaction 1 Look at the pattern by making a line graph of the cell means a Remember that nonparallel line rule is just a guess i To determine whether the interaction is significant lines must be significantly nonparallel i e the pvalue is the definite determinant of significance However sometimes we only have the graph to use b For table Marginal Means go with Main Effects 2 Post Hoc Test a When do we need post hoc tests i For interaction run post hoc if 1 Overall F is significant b Underlying concept These notes represent a detailed interpretation of the professor s lecture GradeBuddy is best used as a supplement to your own notes not as a substitute III i Is the pattern we got the pattern we predicted c One existing option i Simple main effects test 1 Specifically for Interactions 2 Breaks Interaction down into smaller pieces a Are individual pairs significantly different 3 Describe pattern shown in graph a Describing Interactions conclusion i Include 1 IVs Tx Levels 2 Specific DV measurement 3 Full Comparison a Talk about all conditions 4 Systematic description a Whether significant or nonsignificant b Assumptions i Interval or ratio data ii Independent observations iii Underlying Normal distribution iv Homogeneity of variance in all conditions Correlation a Inferential or descriptive i Investigates the relationship between two DVs x and y 1 Note that up until now we ve been looking at differences between means ii Correlation itself is a descriptive statistic 1 Why are so many

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