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


Lecture number:
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 correlational studies done if they cannot tell us about causation a When variables are impossible to manipulate b When variables are unethical to manipulate b Scatter Plots i How to construct 1 2 measures can be of different scales from each participant plotted on coordinate ii They can tell us about 1 Direction of relationship a Ex Positive i As x increases y increases b Ex Negative Inverse i As x increases y decreases 2 Degree of relationship a Regression Line or Line of Best Fit i Weak relationship 1 Lines very scattered far from the regression line ii Strong Relationship 1 Points not very scattered close to line iii No scatter at all perfect correlation c Calculational example Pearson r Correlation i This single value can tell us 1 Direction of relationship and degree of relationship ii Ranges from 1 to 1 1 Sign indicates whether relationship is positive or negative 2 Absolute Value describes strength of relationship a Values closer to 1 are stronger values closer to 0 are weaker iii Definitional formulas 1 r Degree to which x y vary together Degree vary separately 2 Similar to a t Actual diff btwn Ms Diffs expected by chance

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