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17 Cards in this Set

  • Front
  • Back
causality requires
-control of other possible causes
correlation coefficient
demonstrates value of variablitiy
-1.00 to +1.00
positive:as scores one 1 variable inc, scores on other variable inc
negative:as scores on 1 variable inc, scores on other dec
strength of linear relationship
farther from zero, the stronger the relationship
(-.70 has great magnitude than .40)
interpreting r
<0.10 is trivial
0.10< r <0.30 is weak
0.30< r <0.50 is moderate
r>0.5 is strong

r^2 tells how much variance is accounted for
tells whether correlation is statistically significant or not
Null hypothesis: no relationship, r=0

alternative hypothesis: positive, r>0.00 or negative, r<0.00
if prob. that correlation = 0.00 is low (less than 5%) than can say correlation is statistically significant and reject null hypothesis

however, cannot 'prove' alternative hypothesis
+/- 3 sd from the mean
can have big impact on r
on-line vs. off-line
on-line can boost size of correlation

off-line can drag correlation down
prediciting scores on 1 variable based on the participant's scores on another variable

assess relationships of >1 predictors simultaneously
multiple regression
assessing the relationship b/w mulitple predictor variables and an outcome variable

predicting an outcome variable based on predictor scores

linear relationship, best fitting line
moderator variables
interactions:when the relationships b/w a predictor and outcome variable depends on the value of another variable (moderator)

ex. licorice color; more ppl like red than black
moderator variables can help understand...
can be a 3rd variable that influences the relationship b/w a predictor and outcome variable

understand the relationship

establish boundary conditions
types of moderator variables
Discrete: examine predictor-outcome variable for each category (licorice either black or red)

Continuous: one solution- change continuous to discrete

high category- group > +1 sd
low category- group < -1 sd
moderator analysis
predictor, moderator, and new interaction variable (PxM) are entered into regression equation

high values: both P and M are large
low values: either P, M, or both values are small
mediator variables
an intervening 3rd variable that (partly) explains the influence of the predictor on the outcome

predictor incluences mediator, which influences outcome variable

ex. viewing violent tv-->arousal (mediated effect)-->agressive play
mediated effect if..
the magnitude of direct relationship b/w the predictor and outcome is drastically reduced (or 0) when the relationship b/w the mediator and outcome is considered

the predictor and mediator are related