research methods

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research methods
2011-03-02 02:09:01
research methods

research methods review
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  1. Other names for "tests"
    • assessment
    • test
    • measure
    • instrument
    • scale
  2. reliability
    • a test is reliable if the data is consistent over time when the test is
    • repeated
  3. Tests of responsibility
    • Test-retest
    • split-test
  4. Test-retest
    when someone is administered the same test over time
  5. Split-test
    Taking parts of a test and comparing those parts to one another
  6. Validity
    Is the test supposed to test what it is supposed to test?
  7. Tests of validity
    • content validity
    • construct validity
    • predictive validity
  8. content validity
    does the content make sense according to the topic?
  9. predictive validity
    does it predict a difference in behavior?
  10. Theory
    A general statement about the relationship between constructs or events.
  11. Hypothesis
    • Formal prediction about the relationship between two or more variables
    • that is logically derived from the theory
    • derived from a theory
  12. correlational research
    • When: used to investigate relationships between variables
    • How:
    • measure both or all groups involved in study
    • limit: does not give a
    • cause
    • keywords: relationship, related, linked to, associated
  13. Correlation coefficient
    • between -1 to 1
    • the closer to one the correlation is, the stronger
    • the relationship is
  14. positive correlation
    • Both variables travel in the same direction
    • 0-1 (0 to .2= no
    • correlation, .2 to .4= weak correlation, .4 to .6 =moderate correlation,
    • .6 to .8 =strong correlation
    • Example: The more time one spends
    • studying, the better grades that person will recieve on an exam
  15. Negative Correlation
    • Both variables that travel in opposite directions
    • -0 to -1 (0 to
    • -.2= no correlation,
    • -.2 to -.4= weak correlation, .-4 to .-6
    • =moderate correlation, -.6 to -.8
    • =strong correlation
    • Example:
    • The more time someone spends partying the person is most likely to
    • recive a lower score on an exam
  16. Experimental Research
    • When: when you want to draw cause and effect
    • How: manipulate one of
    • the variables under controlled conditions
    • limit: hard to make
    • entirely random and hard to control variables
  17. Independent variable
    • The variable in a experiment that is being manipulated
    • causes direct
    • variable
    • Example: Different temperatures may cause subjects to
    • score higher on test ( temperature is independant variable)
  18. Dependent variable
    The outcome that is affected by the independent variable
  19. Why you may want more than 1 Independent variable
    There may be interactions between variables
  20. Why you may want more than 1 Dependent variable
    The Independant variable may have influenced another dependent variable
  21. A true experiment
    • A manipulated independent variable while everything else is controlled
    • randomly
    • assigned groups
    • There will be an infinite amount of independent
    • variables if groups aren't randomly assigned
  22. Experimental Groups
    The group that recieves treatment(independent variable)
  23. Control Group
    The group that doesn't get treatment
  24. Sampling Bias
    • subjects tested are not chosen by random
    • corrected by random
    • assignment
  25. Confounding bias
    • other variables may have affected dependent variable
    • corrected by
    • randomly assigning groups
  26. Placebo effects
    • People belive that treatment actually works
    • corrected by blind study
  27. Experimentor effect
    • The experimenter's expectations influence results
    • corrected by a
    • double blind study
  28. Social desirability effect
    • subject is not comfortable with test or answers on test
    • anonymity or
    • non-self report
  29. Significance Test
    • Tests that determine if group averages were found from real effects or
    • just by chance
    • Example: ANOVA, Chi-square, or t-test
  30. P-value
    The number that determines the significance between dependent variables
  31. Interpreting P-value
    • n = number of subjects in group
    • N = total number of subjects in group
    • P
    • < .05 means that there is a significant difference
    • P > .10
    • means that there is not a significant difference
    • P between .05 and
    • .10 means that there is a marginally significant. This usually means
    • that you need to get more data and withold judgement
  32. Replication
    • When an experiment is done by another researcher. If similar results are
    • found in future experiments then the experiment is seen as significant
    • in terms of a genuine relationship