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18 Cards in this Set

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