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PSY 657 MIDTERM EXAM STUDY GUIDE
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PSY 657 MIDTERM EXAM STUDY GUIDEcorrelation - ANSWER -index of linear association between a pair of variables
-how related are variables?
-magnitude of association
standardization - ANSWER 1st step in correlation coefficient
Pearson-product-moment correlation (PPMC) - ANSWER -another name for r
-each moment = deviation from the mean
years-Dodson law - ANSWER -optimal arousal graph for performance- low =
fatigue, high = anxiety
-normal distribution/curve
Pearson-product-moment correlation (2) - ANSWER -is a term used for r when
talking about two continuous variables (continuous, continuous = PPMC - put in
the scatterplot
r (correlation coefficient) - ANSWER -ranges from -1 to 1
-1 = perfect positive, -1 = perfect negative
-easier with linear and continuous measurements
correlation coefficient formula - ANSWER sum of products standardized x & y
scores (z-scores)
divided by
N
0, 1 - ANSWER average z-score mean = ___, SD = ___
product of moments - ANSWER ZxZy
moments - ANSWER correlation coefficient
,- ZxZy how far away from the mean (deviation)
point biserial correlation (r_pb) - ANSWER -a special case of PPMC
-relates a binary/dichotomous variable with a continuous variable
-t-test is helpful
-bar graph
-a direct indication of relationship strength
cohen's d - ANSWER -transforms the t-value to show relationship strength in the
t-test
- # of SDs away in both groups
-effect size
.1, .3, .5 - ANSWER correlation strength (r):
-small = ___
-medium = ___
-large = ___
.2, .5, .8 - ANSWER Cohen's d strength (d)
-small = ___
-medium = ___
-large = ___
phi coefficient r_phi - ANSWER -relates 2 binary/dichotomous variables
-extends logic for 2 binary values not mattering
-similar to chi-square
-contingency table
-gives a measure of effect size
,latent continua - ANSWER correlations involving ____ don't use PPMC
biserial r_bis - ANSWER -correlation involving latent continua
-relates a contingency variable with an artificially dichotomous variable
-tests usefulness of psychometric measure items
-a relative continuum that underlies the binary
latent continua (2) - ANSWER -biserial r_bis
-underlying binary
-reimagines variance if measured binary in the continuum isn't binary construct
ex. what color is the flag? red, white, and blue; people could say blue, red, and
white
tetrachoric r_tet - ANSWER -correlation involving latent continua
-relates 2 artificially dichotomous variables
-under dichotomy latent continua is needing to reconstruct correlation
-comparing 2 item scores that represent some correlation
point biserial correlation (r_pb) and phi coefficient (r_phi) - ANSWER the special
cases of PPMC
biserial (r_bis) correlation and tetrachoric (r_tet) correlation - ANSWER the
correlations that are non-PPMC and instead involve latent continua
spearman rho rank order correlation - ANSWER -correlation coefficient ranks
order of data
-doesn't use PPMC
-ex. correlation perfect between 2 races- time not important, serial position of
perfection important
interclass correlation - ANSWER -different scales in variables
, -PPMC is example
-(ex. weight = lbs, height = inches)
intraclass correlation - ANSWER -PPMC is indifferent to metrics of variables
-must use variables of same scale (ex. rating 2 faculty- you want similar values)
-considers mean difference as error
zero-order correlation - ANSWER denotes the correlation between just 2 variables
r-squared - ANSWER -variance accounted for, variance explained by
-identifies variance % value
r^2, R^2 - ANSWER -identifies variance in a single correlation
-identifies variance in multiple correlations
regression - ANSWER -used for prediction of outcome variable
-correlation/ how related are variables/magnitude of association = incidental
-weights are important
multiple correlation - ANSWER -tells how well optimal combo of predictive
variables (x) correlate with criterion variables (y)
-not zero-order --> more than 2 variables
-ex. compare variables and see how much better it is to use 1 predictor- do 2 do
better than 1?
-R
R - ANSWER multiple correlation is noted as __
partial correlation - ANSWER -multiple variables relate in different ways
-confound variable --> eliminate the influence of that variable
-rxy.z
-z = confound variable
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