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26 Cards in this Set
- Front
- Back
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3 Difference scores issues (with mixed designs S/AxB)
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1. masks main effects; only shows extent of change in A across B (interaction)
2. interpretation: one-way between groups ANOVA is computed but results must be interpreted as an interaction 3. if levels of B > 2, this fails |
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what is Spearman Rho correlation? give example
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correlation between ranks on two dimensions, ie hs class rank and college class rank
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Pearson product moment:
ex: |
correlation between two continuous variables
self esteem and depression |
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what does r estimate? between what and what?
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r estimates rho. btw 1 and -1
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what are the determinants of power for predicting y hat?
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small N, small r
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name the 3 cautions of hierarchical regression:
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1. multicollinearity
2. suppression 3. shrinkage |
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define mulitcollinearity, explain how it is fixed
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high correlation among predictors, which explain the same variance and lower DF.
FIX: examine correlation matrix and eliminate a predictor, or combine predictors |
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define suppression, explain fix
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suppression is when the prediction of x1 is enhanced only because x2 suppresses variance irrelevant to y.
FIX: examine correlation matrix, if b's and r's have different signs, you have suppression! |
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define shrinkage, explain fix
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R squared is inflated as K increases (worse with small N).
FIX 20-30 cases per predictor |
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name the 3 assumptions of r
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rectilinearity, homoschedasticity, x measured without error
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define rectilinearity
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relation is a STRAIGHT LINE
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define homoschedasticity
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variances of ys at each x are equal
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what are the three caveats of r?
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1. do not try to predict y beyond original range 2. do a scatter plot to look for leverage (outliers or extremes in mean) 3. truncated distributions reduce the size of r
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what is the common language size effect?
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if person A's x exceeds B's x, what is the probability that A's y will exceed B's y? CLE= y67, 67% chance
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what does the multiple correlation and regression equation do?
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comprizes more than one predictor, and minimizes squared differences of predicted y's from observed y's (least squares regression)
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what is R?
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correlation between obtained and predicted ys
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R = ?
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r times y times yhat
ALWAYS positive R is the best linear combination of the predictors of y |
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explain a standard multiple regression
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all variables added at once, each predictor is evaluated as though no overlap exists.
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what is crucial about standard multiple regression?
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each predictor is evaluated in terms of what it adds to the prediction of the criterion, that differs from predictability given by others
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what test allows for assessment of unique contribution in standard multiple regression?
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significance test of each regression coefficient (b)
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what do these divisions of S/AxB design mean?: A, S/A, B, AxB, S/AxB
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A = average variation between scores
S/A = error B = repeated measures factor AxB = interaction S/AxB = unexplained variability |
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define these variables of S/AxB design: A, B, AB, a, an, b, bn
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A = column totals
B = row totals AB = cell totals a = # levels of IV an = # of subjects b = # of times subject gives score bn = # of x's added to get A |
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DF for S/AxB design:
total btw A S/A within B AxB S/AxB |
total: N -1
btw: an-1 A: a-1 S/A: btw-A within: total-btw B: b-1 AxB: (a-1)(b-1) S/AxB: (S/A) times (B) |
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what N is needed given projected effect for power?
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4/rsquared = N-1
this gives you 50% power double for 80-90% power |
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least squares regression:
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Sigma (y - yhat)squared
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in uncorrelated predictors, what does Rsquared tell us? R?
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Rsquared: percent of variance in y explained by x
R: how strongly x1 x2 and y are correlated |
