1. Experiment
    • one manipulated variable - IV
    • one labile measured variable-DV
  2. Correlational Study
    • two measured variables
    • one called predictor (cause)
    • one called predicted (effect)
  3. Quasi-Experiment
    • one stable measured variable (SV) treated as the IV
    • one labile measured variable treated as DV
  4. "Third Variable"
    • a (typically unmeasured) variable that could be the cause of both the measured variables in a correlational study
    • To fix, identifiy, measure, and co vary it out
  5. Spurious
    a significant relationship that is not causal (in either direction)
  6. Cross-lagged
    • the correlation between one variable at one time and another variable at another time
    • used to determine the more likely direction of causation
    • if r2 X1Y2 > X2Y1 then X is probably the cause
  7. 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
  8. External Validity
    the extent to which the results (from an experiment) will generalize to other situations
  9. Context Specificity
    when the results from an experiment or study are unique to the situation
  10. Person Specificity
    when the results from an experiment or study are unique to the subjects
  11. Convenience Sampling
    when only easily-recruited subjects are used
  12. 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
  13. 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
  14. Quota Sampling
    • Most popular type of non-proportional stratified random sampling
    • when the sizes of the groups in the sample are forced to be equal
  15. Survey or Questionnaire
    a structured set of items designed to measure attitudes, beliefs, values, or behavioral tendencies
  16. Scale
    • a small set of items designed to measure a particular attitude, belief, value, or behavioral tendency
    • Likert: sets of strongly agree thru strongly disagree items
    • Guttman: sets of ascending questions
    • Thurstone: check all that apply (that are worth varying points)
    • Semantic Differential: indicate position between opposite pairs
  17. Naturalistic Observation
    • studying behavior in everyday environments without getting involved
    • key threat: reactivity (secondary observer bias)
  18. 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
  19. Observer Bias
    • when the beliefs or expectancies of the observers influence what is recorded
    • inter-coder reliability must be at least .90
  20. Experimenter Bias
    • in general, when the beliefs and or expectancies of the experimenter end up altering the results
    • standard experimenter bias: occurs when the experimenter behaves differently when collecting data in different conditions (experimenter reactivity)
    • defenses: remove experimenter or double-blind
    • observer bias is when only the recording of data is altered by the beliefs of the observer
    • defenses: checklists and or partial sampling
  21. Ex-post-facto Quasi Experiment
    when you take only one sample and then divide the subjects into the groups after-the-fact
  22. Planned Quasi-Experiment
    • when you take separate samples for each of the groups
    • note: this is another good example of the effort vs quality trade-off
  23. Longitudinal Study
    • (aging) when you follow the same subjects over time
    • major unique threat is time-frame (zeitgeist) effects
  24. 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
  25. Decision Tree
    • Domain: are you studying hidden, internal variables or behavior? This determines whether to use a survey or observation
    • Sampling: how much does this depend on who is measured? determines whether some fancy method (stratified, proportional, random) must be used
    • Causal Logic: is one of my variables either highly stable or the passage of time? determines whether one of the specific designs is used
  26. Directionality Problem
    • it could be that X causes Y or that Y causes X
    • to determine, run a cross-lagged study
Card Set
test 3