Research Design

  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
research design