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PSY 657 MIDTERM EXAM STUDY GUIDE $11.99   Add to cart

Exam (elaborations)

PSY 657 MIDTERM EXAM STUDY GUIDE

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  • PSY 657
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  • PSY 657

PSY 657 MIDTERM EXAM STUDY GUIDE...

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  • August 12, 2024
  • 41
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers
  • PSY 657
  • PSY 657
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PSY 657 MIDTERM EXAM STUDY GUIDE


correlation - 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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