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26 Cards in this Set

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3 Difference scores issues (with mixed designs S/AxB)
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
what is Spearman Rho correlation? give example
correlation between ranks on two dimensions, ie hs class rank and college class rank
Pearson product moment:
correlation between two continuous variables
self esteem and depression
what does r estimate? between what and what?
r estimates rho. btw 1 and -1
what are the determinants of power for predicting y hat?
small N, small r
name the 3 cautions of hierarchical regression:
1. multicollinearity
2. suppression
3. shrinkage
define mulitcollinearity, explain how it is fixed
high correlation among predictors, which explain the same variance and lower DF.
FIX: examine correlation matrix and eliminate a predictor, or combine predictors
define suppression, explain fix
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!
define shrinkage, explain fix
R squared is inflated as K increases (worse with small N).
FIX 20-30 cases per predictor
name the 3 assumptions of r
rectilinearity, homoschedasticity, x measured without error
define rectilinearity
relation is a STRAIGHT LINE
define homoschedasticity
variances of ys at each x are equal
what are the three caveats of r?
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
what is the common language size effect?
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
what does the multiple correlation and regression equation do?
comprizes more than one predictor, and minimizes squared differences of predicted y's from observed y's (least squares regression)
what is R?
correlation between obtained and predicted ys
R = ?
r times y times yhat
ALWAYS positive
R is the best linear combination of the predictors of y
explain a standard multiple regression
all variables added at once, each predictor is evaluated as though no overlap exists.
what is crucial about standard multiple regression?
each predictor is evaluated in terms of what it adds to the prediction of the criterion, that differs from predictability given by others
what test allows for assessment of unique contribution in standard multiple regression?
significance test of each regression coefficient (b)
what do these divisions of S/AxB design mean?: A, S/A, B, AxB, S/AxB
A = average variation between scores
S/A = error
B = repeated measures factor
AxB = interaction
S/AxB = unexplained variability
define these variables of S/AxB design: A, B, AB, a, an, b, bn
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
DF for S/AxB design:
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)
what N is needed given projected effect for power?
4/rsquared = N-1
this gives you 50% power
double for 80-90% power
least squares regression:
Sigma (y - yhat)squared
in uncorrelated predictors, what does Rsquared tell us? R?
Rsquared: percent of variance in y explained by x
R: how strongly x1 x2 and y are correlated