Research Design

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  1. Parsimony/Occam's Razor
    the idea that we should accept the simpliest explanations of the data; what are the minimal concepts that can explain the data?
  2. Internal validity
    how well have I asked the question
  3. External validity
    how well can I generalize these data
  4. Construct validity
    • conceptual basis/theoretical foundations this is far more inferential and base on expert opinion
    • "What might be going on here to cause the effects I am observing or predicting?
  5. Threats to internal validity
    • history (anything that occurs during the study)
    • maturation (change over time)
    • testing (familiarity with pre-post assessment
    • instrumentation (changes in the instrument or procedures over time)
    • statistical regression (extreme score regress to the mean)
    • selection bias (systematic difference btwn groups differenc of people who self-select)
    • attrition (ppl who drop out are different from those who stay)
    • combination of selection and other threats (multiple threats, especially selection X history bias or maturation bias)
    • diffusion or imitation of treatment ("leaking" tx into control group)
    • special controls or reactions of controls (kind of like the placebo)
  6. Threats to External Validity
    • sample characteristics (if characterists of sample don't match the sample you cannot generalize)
    • stimulus characteristics and settings (can this be generalized beyond the setting of the lab, clinic, etc.)
    • reactivity of experimental arrangements (responding in a way to make self look a specific way rather than authentically)
    • multiple tx interference (the effects obtained in the experiment may be due in part to the context or series of conditioning in which it was presented)
  7. Type 1 error
    • alpha error - probability of rejecting the null hypothesis when it is true
    • you say there is a differnce but there isn't
  8. Type II error
    • Beta error
    • probablity of accepting the null hypothesis whe it is false
    • you say there isn't a difference, but there is
  9. effect size
    magnitude of difference between the two groups
  10. correlate
    • hypothesis that when something happens to one, something happens to the other
    • positive - both go the same way
    • negative - go opposite ways
  11. moderate
    when one variable directly influences the nature, direction of another variable
  12. mediator
    variable or process that explains how a variable produces particular outcome
  13. mulriple regression
    a repeated measures correlation - gives information about the amount of variabilty in your DV is accounted for by the IVs
  14. Informed consent
    • knowledge
    • competence
    • volition
  15. What is the definition of selection bias?
    influences to types of subjects who participate in experiments
  16. List types of subject selection bias
    • samples of convenience
    • volunteer
    • attrition
  17. Why is it important to base research questions on theory?
    • 1. provides order where there is variability
    • 2. explain change
    • 3. inform choice of moderators
    • 4. aids in connection to practice
  18. grounded theory
    a term used in qualitative research referring to hypotheses that emerge from intensive observations
  19. What are the primary types of research?
    • 1. true experiments
    • 2. quasi-experimental
    • 3. case-control designs
    • 4. qualitative
  20. true experiment
    subjects are randomly assigned to conditions- gold standard for determining effectiveness of an intervention
  21. quasi experiment
    conditions of true experiment are approximated but cannot randomize, often due to impossiblility or ethical concerns
  22. case-control design
    chose subjects (cases) sho vary in the characteristic of interest
  23. What is the difference between cross-sectional and longitudinal studies?
    • cross-sectional - makes a comparison between groups at a given point in time (cohort effect [differences really due to varying group histories])
    • longitudinal - makes a comparison over an extend (shows change, attrition)

    sometimes these designs are combined
  24. What are some sample characteristics that should be matched when randomly assigning subjects?
    • age
    • sex
    • SES
    • IQ
  25. What are some types of group designs?
    • pretest-posttest
    • posttest only design
    • Solomon four-group design
    • factorial designs
    • crossover design
    • multiple treatmetn counterbalance design
  26. Pretest-posttest control group design
    • needs two groups; one receives tx one doesn't
    • allows you to match subjects and assign randomly
    • w/in group variability is reduced and allows for more powerful statistcial tests
    • weakness: does not control for the possibility of testingXtreatment or pretest sensitization
  27. Posttest only design
    needs two groups; no pretest

    lack of pretest make it less used b/c there could have been pre-existing differences
  28. Solomon Four-Group design
    • evaluates the effect of pretesting on the effects obtained with a particular intervention
    • needs 4 groups
    • 2 in pre/post; 2 in post only
    • controls for prestest impact; controls for re-testing
  29. Factorial designs
    looks at two or more variables unlike other designs

    allows you to look at interaction effects (e.g., between sex and treatment)
  30. Crossover Design
    • partway through the experiment, subjects cross over and receive the other condition
    • allows for random assignment
  31. counterbalanced design
    cross over design for more than two condintions which takes greater planning in order to ensure random sequencing
Card Set:
Research Design
2010-08-05 20:04:11
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