1. Chapter 6
Research Validity

2. Research Validity:
Truthfulness of inferences made from a research study.

3. Statistical Conclusion Validity:
Valid statements of the covariation (relationship) between the Independent and Dependant Variable.
e.g., Heterogeneous results – present atypical cases.

4. Statistical Conclusion Validity:
Using Statistics as evidence for the null hypothesis. Failing to reject the null does not mean there is no difference between conditions anymore than failing to convict a defendant means that they are innocent. The study may not have had enough POWER to find differences.

5. Construct Validity
Is the construct adequately represented by the measures used in the research study?

6. Participant Reactivity
Participant Motivations and tendencies.

7. Demand Characteristics
Any cues in the experiment (e.g., instructions, rumors, others behaviors that influence the responses of the participants.
e.g., Yawn Study
Subliminal Perception study
Positive self presentation
“screw you” attitude!

8. A. Experimenter Effects
Actions and characteristics of researcher that influence the responses of participants.

9. B. Experimenter Expectancies
Recording Errors
Interpretation Biases
Unintentionally treating one group differently than another.

10. C. Experimenter Attributes
Biosocial (age, sex, race etc.)

11. Controlling for Demand Characteristics
Can’t get rid of demand characteristics.
Withhold the hypotheses until after the study.
Distract participants from real variables.
e.g., asked participants to rate the
commercials for emotions etc.
Do not tell participant which condition they are in.
BE SURE DEMAND CHARACTERISTS DO NOT VARY ACROSS CONDITIONS!!

12. Internal Validity - The extent to which all explanations for changes in the DV between conditions have been eliminated -- other than the IV.
ie(7a)

13. Extraneous variable - any variable other than IV that effects DV.
Control
Experimental
Extraneous
Variable
ie(7a)

14. Control Confound Experimental
Confounding Extraneous Variable - any extraneous variable that affects one condition differently than it affects other conditions.
ie(7a)

15. Between Subjects Designs
Two (or more) Groups of Participants compared to each other. Each group has a different level of the IV.
Experimental (Treatment) Group
Control Group
ie(7a)

16. Major Potential Confound: Individual Differences (I. e
Major Potential Confound: Individual Differences (I.e., perhaps the participants in one group are not comparable in many ways to participants in the other group).
ie(7a)

17. Within Subjects Design
One Group of participants measured under more than one condition of the IV.
Experimental Condition
Control Condition
Potential Confounds
Since subjects cannot be in more than one condition at one time, anything that is not the same at each of the times of measurement (other than the IV) is a potential confound.
ie(7a)

18. Pre-Post Test Designs (type of WS design)
Pre-test - DV measured before Treatment
- serves as baseline control condition
Post-test - DV measured after Treatment
ie(7a)

19. Confounds with Pre-Post Designs
Other than the treatment (IV) what could cause difference in DV between conditions (I.e., what possible confounds could there be?
Confounds with Pre-Post Designs
History - any changes that occurred between
Pre and Post Test other than IV.
ie(7a)

20. Maturation - biological/psychological changes between pre-post test.
Instrumentation - changes in measurement device or operational definition between pre and post test.
ie(7a)

21. Attention Deficit Disorder?
Are rates of Diabetes really increasing, or are we simply diagnosing more cases???
Attention Deficit Disorder?
ie(7a)

22. Testing : Changes is a person’s score for the post conditions that results from having been tested in the pre-test.
Sensitization
Boredom
Practice
ie(7a)

23. Attrition - subjects dropping out of the study.
ANYTHING that is a difference between the
before and after condition other than the IV is
a confound.
ie(7a)

24. Hokey Pokey
ie(7a)

25. Artifact - Effect caused by the procedure rather than by the IV.
Statistical Regression Artifact.
- problem when subjects are assigned to groups
based on the pre-test scores.
- group scores will be pulled towards the mean
of the DV.
- Pre-test high scorers will appear to do poorer.
Pre-test low scorers will appear to do better.
- problem with unreliable measures
ie(7a)

26. Not a problem if the DV is very reliable.
ie(7a)

27. Confounds (in pre and post designs) History Maturation Instrumentation
Internal Validity?
Confounds (in pre and post designs)
History
Maturation
Instrumentation
Testing
Attrition (Mortality)
Statistical Regression Artifact*
ie(7a)

28. Controlling for these confounds. Use a pre-post control group.
Treated the same as treatment Group except
IV is not manipulated between tests.
Control group affected by same, history,
Maturation and Regression effects. Any difference between the treatment and control groups are not due to confounds.
ie(7a)

29. Pre-test Treatment Post-test plus confounds Control Group
Experimental Group
Pre-test Treatment Post-test
plus confounds
Control Group
Pre-test confounds Post-test
ie(7a)

30. Compare Pre-test scores. Are they the same to begin with?
Selection – confound due to assignment of subjects to the Control and Treatment Groups.
Compare Pre-test scores. Are they the same to begin with?
But could be a difference that interacts with the treatment.
- use random assignment!!!!
ie(7a)

31. subjects assigned to treatment and control group
Selection Confound
subjects assigned to treatment and control group
biased on a criteria (bias).
Selection X (interactions)
Any of the five confounds effect the control group
differently than the treatment group.
i.e., Alcohol treatment study
- compare volunteers to non-volunteers
e.g., Differential History
ie(7a)

32. Differential Attrition
Selection X (interactions)
Any of the five confounds effect the control group
differently than the treatment group.
Differential History
Differential Attrition
i.e., Alcohol treatment study
- compare volunteers to non-volunteers
ie(7a)

33. Differential Attrition Weight loss Study (Diet and Exercise condition)
No-Treatment Control Group
Type of people that drop put of the study might depend on which study they are in.
ie(7a)

34. How can we ensure the subjects in each condition are comparable?
Random Assignment
Extraneous variables still effect DV, but it
should not be a confound.
ie(7a)

35. Treatment group = size of treatment effect plus confounds
Pre-test
Post test
Treatment
Control
Treatment group = size of treatment effect plus confounds
Control group = estimate of the size of pre-post confounds.
ie(7a)

36. Pre-test
Post test
Treatment
Control
Is the pre-post test change significantly greater in the Treatment Condition than in the control condition?
ie(7a)

37. Goal of Psychology: to determine the Underlying “laws” of behavior.
External Validity
Goal of Psychology: to determine the
Underlying “laws” of behavior.
External Validity – the extent to which the
results of an experiment can be applied to and across different persons, settings, and times.
(ie14)

38. Population Validity – ability to generalize results from the sample to a larger population.
Random Selection – from a population should insure generalization to the experimentally accessible population.
(ie14)

39. Target Population - larger population to which results are generalized.
(ie14)

40. ALL PERSONS WITH DEPRESSION
Persons Diagnosed
Willing
Control
Studied
(ie14)

41. Ecological Validity: extent to which results can be generalized across settings or environmental conditions.
(ie14)

42. Temporal Validity – generalizability across time. Seasonal Variation
- fixed-time variation
- variable time variation
Cyclical Variation
Personological Variation
e.g., are you planning a mid-life
crisis?
(ie14)

43. Treatment Variation Validity
- Degree the results of a study can be generalized across variations in the treatment
Outcome validity
- Degree to which the results of a study generalize across related dependant variables.
(ie14)

44. Relationship between Internal & External Validity
To obtain high Internal Validity we often create very artificial study settings.
To establish external validity we often need to determine if the finding applies across setting, locations, cultures etc.
(ie14)

45. (ie14)