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Quantitative variable relationships
 linear: positive, negative, no correlation
 nonlinear: curved

Quantifying the relationship
 correlation coefficient: pearson's r represents the strength
 interval & ratio scales
 correlations are rarely bigge than .30
 measurement error tends to produce weaker correlations: one way to think about strength is the mean difference

Describing relations involving one categorical variable
 groups differ in mean score?
 direction?
 size of the difference?

Significance test
If probability is low the relation is real


One variable cause another
 making infeerences about causality: one variable causes another to occur
 directionality problem

3rd variable problem
 3rd variable cause both variables to be correlated
 very common
 there can be more than one
 indirect & direct
 negative & positive


DV & IV
 IV has levels
 they both have operational definitions: not a causal statement
 random assignment: different types of people are equally distributed to each condition
 manipulation of the IV: eliminates the 3rd variable problem


Internal & external validity
 internal: confirm in the experiment
 external: confirm outside the experiment

Experimental control
 maximizes internal validity
 sometimes limits external validity
 external validity matters, but without internal validity we dont know what's causing what in either the lab or the real world

Increasing external validity in experiment
 replicate with many samples
 when studying abstract variables, ues many concrete instances as feasible
 create a naturalistic atmosphere, both physically and/or psychologically with deceltion

Interpreting "null" results
 was your manipulation of your IV powerful enough?
 was your DV sensitive enough to detect differences in the score?: Ceiling effect & floor effect
 did you have enough people in your groups to detect a difference?
 was there a lot of random errorm in the measure of your DV?

Units of observation
 smallest level of data on all variables
 each unit if observation can be any defiable unit for which the variable differs from one unit to the next

Withinsubject manipulation

Between subjects
different people are assigned to different conditions (or levels) of the IV

Within subjects
(repeated measure) the same people are exposed to all levels or conditions of the IV

Benefits of withinsubjects design
 no random sampling error; only measurement
 mortality not a confound
 requires half as many subjects
 greater statistical power to eliminate chance as an explanation

Weaknesses of withinsubject designs
 order effects
 history effects: event ex. 9/11
 maturation effects
 carryover effects: something about the first condition caries over into the second condition

Carryover Effects
 fatigue effects: people get tired or bored during the study; performance effects
 practice effects: more engaging; having pratice
 testing effects: familiarity; study
 contamination effects: what happens in the 1st condition spills over and "contaminates" the 2nd

Counterbalancing
 order effects becaome confounds, if one condition always comes first
 confound is eliminated if each condition comes in each order
 large order effects can still weaken the observed effect of the IV
 complete: every subject gets every order
 incomplete: different subjects get different orders

Factorial Design
 more than one independent variable
 each level of the first IV is paired with each level if the second IV
 ex. 2x2= 4 conditions
 2 experiments in 1
 matrix table
 interaction: the difference between the IV's

