1. Univariate
    How much does one variable change across observations & what is typical or average
  2. Multivariate
    How two or more variables change together
  3. Laws
    Describe relationships among observed variables and presume the relationship is consistent and universal.
  4. Theories
    • Include assumed causes & unobserved mechanisms that explain how and why variables are related (whether consistently lawful or not)
    • Guess
  5. Hypotheses
    predict what will occur in a specific situation based on prior observations and/ or the logical implications of theories.
  6. What makes some theories better? (more plausible)
    • Make unique & specific predictions
    • Consistent with established theories
    • Explain the most data
    • Simplicity (parsimony)
  7. Simplicity
    • Each assumption has a probability of bring wrong
    • More assumptions=more probability of being wrong
    • Unless assumptions verified by better prediction, explanation, consistency
  8. 5 Limitations of Personal Experience
    • Expectation Bias: our expectations tend to influence the way we perceive and interpret events.
    • The Confirmation Bias: We tend to only use experience and info that are consistent with our current ideas.
    • Limited Data: We make inferences from very little info.
    • No Baserate/Comparison Group: We often lack and overlook the importance of comparisons, when inferring a relationship between variables.
    • Lack of Control: We don't systematically control the variables, which is needed to infer a causal relationship.
  9. How research overcomes these limitations
    • Defines variables in terms of specific, objective criteria.
    • Uses settings where observations is optimal.
    • Evaluates the reliability and validity of observations.
    • Records and replicates observations.
    • Collects larger, random sample & estimates chance.
    • Uses appropriate comparisons
    • Manipulates variables and uses statistical controls to test competing causal explations.
  10. Essentialism
    • What is the complete idealized essence of a thing?
    • -Concepts are mystical ideals that may or may not exist
    • -What should a word apply to?
    • -No objective answer & evidence is irrelevant
  11. Operationism
    • What are the real world properties of a thing?
    • -Concepts correspond to the actual world.
    • -They must be connected to what is observable.
    • -Abstract ideas are shaped and reshaped empirical data
  12. Concrete Variables
    • An actual thing/event in the physical world
    • Directly observable and measurable
  13. Abstract Variables
    • Idea or concept
    • Not directly observable or measurable
    • Either a particular variable or composed of multiple variables
  14. To test theories, we must
    • Define the abstract concepts in terms of observable variables.
    • Then create operational definitions
    • -The precise operations, methods, and procedures used to assign a quantitative value or a qualitative category to each observation.
  15. An operational definition should...
    • Specify exactly what was measured and how.
    • Allow for placing each observation on a numerical scale along 1 or more dimensions.
  16. Quantifying
    • Continuous vs categories
    • Presence vs Absence of a particular thing is Quantitative
    • Numerical value does not always translate into exact number of "units"
  17. Qualitative Variables
    • Abstract variables composed of multiple variables that cannot be separated.
    • Inherently "Confounded"
    • -Limits ability for explanation
    • Why are they used so often?
    • -Still allows us to describe relationships
  18. Why is specificity so important?
    • Increases reliability
    • Allows for evaluation of validity
    • Allows others to replicate the work
    • Reduces opportunity for fraud or expectancy bias
  19. Refining our constructs
    • Concepts are like theories
    • Concepts should be modified in light of data
  20. Latent Variables
    • Advantages
    • -Reduces the influence of other variables
    • -Scores better representation the underlying concept
    • Disadvantages
    • -More complex to use
    • -Units of measurement no longer have concrete meaning.
  21. Measurement Error
    • O=T+R+S
    • Observed score= True score + Random error + Systematic error
  22. R: Random, inconsistent influences on measurements
    Lack of control or specifying in the method
  23. S: Systematic, consistent influences on measurements
    • Calibration error in equipment or consistent error by observer
    • Same amount and direction for each observation
  24. Random Error & Reliability
    Lots of R can reduce reliability.
  25. How to reduce random error?
    • To reduce amount of R in each observation:
    • -Identify and eliminate possible random influences
    • To reduce the effects of R on overall results:
    • -Measure each observation multiple times and take average
    • -Collect a large sample of observations
  26. Validity
    • Measuring what you think
    • If unreliable, then its invalid
    • -Lots of R mean O unequal to T
    • -Reliability is rarely perfect
    • But, reliable measures can still be invalid
    • -Due to systematic error
    • -Reliability is just the first step in ensuring validity
  27. How do we assess Validity?
    • Predictive Validity
    • Convergent
    • Discriminate
  28. Predictive Validity
    • Does it predict scores on previously validated measures of the same thing?
    • Why not just use the other measure?
  29. Discriminant Validity
    Should not correlate with variables that are supposed to be unrelated
  30. How do we improve Validity?
    • Pick a new operational definition
    • Improve reliability
    • Create a Latent Variable
  31. O=T+R+S
    • We only know the observed score
    • We can only estimate R and S
    • W never know for sure that O=T
    • So, we must take steps to minimize any potential error and its impact
Card Set
Psych 242 exam 1