Experiment
one manipulated variable - IVone labile measured variable-DV
Correlational Study
two measured variablesone called predictor (cause)one called predicted (effect)
Quasi-Experiment
one stable measured variable (SV) treated as the IVone labile measured variable treated as DV
"Third Variable"
a (typically unmeasured) variable that could be the cause of both the measured variables in a correlational studyTo fix, identifiy, measure, and co vary it out
Spurious
a significant relationship that is not causal (in either direction)
Cross-lagged
the correlation between one variable at one time and another variable at another time used to determine the more likely direction of causationif r2 X1Y2 > X2Y1 then X is probably the cause
Partial (With respect to Z)
the correlation between two variables after the effects of a third variable (Z) have been removed used to test (and rule out) a third variable explanation if prXY.Z=rXY then Z is not a cause of both X and Y
External Validity
the extent to which the results (from an experiment) will generalize to other situations
Context Specificity
when the results from an experiment or study are unique to the situation
Person Specificity
when the results from an experiment or study are unique to the subjects
Convenience Sampling
when only easily-recruited subjects are used
Simple Random Sampling
when all members of the population have a definable probability of being sampled, but no attempt is made to match the group sizes
Proportional Stratified Random Sampling
when the relative sizes of the groups in the sample are forced to be the same as those in the population
Quota Sampling
Most popular type of non-proportional stratified random samplingwhen the sizes of the groups in the sample are forced to be equal
Survey or Questionnaire
a structured set of items designed to measure attitudes, beliefs, values, or behavioral tendencies
Scale
a small set of items designed to measure a particular attitude, belief, value, or behavioral tendencyLikert: sets of strongly agree thru strongly disagree itemsGuttman: sets of ascending questionsThurstone: check all that apply (that are worth varying points)Semantic Differential: indicate position between opposite pairs
Naturalistic Observation
studying behavior in everyday environments without getting involvedkey threat: reactivity (secondary observer bias)
Participant Observation
studying behavior from within the target group key threat: standard experimenter bias (secondary observer bias) note: Participant observation is not often possible since no consent observation can only occur when and where there is no reasonable expectation of privacy
Observer Bias
when the beliefs or expectancies of the observers influence what is recordedinter-coder reliability must be at least .90
Experimenter Bias
in general, when the beliefs and or expectancies of the experimenter end up altering the resultsstandard experimenter bias: occurs when the experimenter behaves differently when collecting data in different conditions (experimenter reactivity)defenses: remove experimenter or double-blindobserver bias is when only the recording of data is altered by the beliefs of the observerdefenses: checklists and or partial sampling
Ex-post-facto Quasi Experiment
when you take only one sample and then divide the subjects into the groups after-the-fact
Planned Quasi-Experiment
when you take separate samples for each of the groupsnote: this is another good example of the effort vs quality trade-off
Longitudinal Study
(aging) when you follow the same subjects over timemajor unique threat is time-frame (zeitgeist) effects
Cross-Sectional Study
(aging) when you take separate samples for each age group (at the same time)major unique threat is cohort effects
Solution to both longitudinal and cross sectional: run a hybrid study and verify same results either way
Decision Tree
Domain: are you studying hidden, internal variables or behavior? This determines whether to use a survey or observationSampling: how much does this depend on who is measured? determines whether some fancy method (stratified, proportional, random) must be usedCausal Logic: is one of my variables either highly stable or the passage of time? determines whether one of the specific designs is used
Directionality Problem
it could be that X causes Y or that Y causes X to determine, run a cross-lagged study