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### 17 Cards in this Set

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 causality requires -covariation -directionality -control of other possible causes correlation coefficient =r demonstrates value of variablitiy range -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 magnitude 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 p-value 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 probablity 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 outliers +/- 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 regression 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