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18 Cards in this Set
- Front
- Back
Discriminant Analysis
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which IV has the most impact on the DV
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Factor Analysis
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underlying explanatory structure
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ANOVA
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1 IV, 1 IV with more than 1 level or categories, 1+ IV
but only 1 DV |
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The DV in ANOVA
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must be ordinal or nominal
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ANOVA assumptions
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homogeneity of variance in the population
normal distribution in population (random selection) independent observations in the sample |
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ANOVA statistic result
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F > 1 reject null
F = 1 retain null (between-group and within group are same and therefore due to chance) critical value of F (a table) |
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Why do you do 2-way ANOVA?`
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to see if there's a MAIN OR INTERACTION effect
to get the EFFECT SIZE |
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ANCOVA
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partials out the effect of a co-variate (thought to influence the DV) when it runs the analysis
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ANCOVA Assumptions
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reliability of covariate
linear relationship (correl) of DV & IV homogeneity of regression for covariate (same for each group of IV's) |
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the DVs in MANOVA
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interval or ratio
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MANOVA & Type I
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MANOVA decreases Type I error because you compare all the DV's at the same time
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MANCOVA covariate
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related to DV, not the IV
continuous (interval or ratio) |
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What do you do if you get an interaction effect?
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Stop the analysis.
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MANOVA assumptions
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random samples
homescedasticity linear relationship between DV's for "Franken-DV" (linear combination of DV's that maximize group differences) |
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Multiple Regression stat
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R2
how much variability in DV can be explained by a linear combination of IV's aka coefficient of determination |
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Multiple regression threat
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multicollinearity:
the predictor IV' are highly correlated to each other |
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Multiple Regression Assumptions
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homoscedasticity of variance (the same across IV's)
linear relationship IV and DV prediction errors are normal dist, don't correlate w/IV, independent observations IV's are FIXED |
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Coefficient of determination
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R2
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