1 / 39

Duck with Human Brain: A Leader's Journey

After a successful operation, a duck with a human brain becomes the leader of a flock, while his creator, Irwin, is ostracized and wanders south. Explore the strengths of true experiments, how they eliminate confounds, allow observation of the invisible, provide information about interactions, and minimize noise.

pmildred
Download Presentation

Duck with Human Brain: A Leader's Journey

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. The operation was a success: Later, the duck, with his new human brain, went on to become the leader of a great flock. Irwin, however, was ostracized by his friends and family and eventually just wandered south.

  2. Outline • Strengths of True Experiments • Simple Research Designs

  3. Strengths of True Experiments • Eliminate Confounds • Allow Observation of the Invisible • Provide Information about Interactions • Minimize Noise

  4. Eliminate Confounds • Confounds • Nuisance variable that • 1) varies systematically with the IV • 2) influences the DV in a way similar to the way the IV is expected to • 3 types of confounds • Operational confound • Person confounds • Procedural confounds

  5. Operational Confounds • When a manipulation designed to manipulate one construct (such as self-esteem) manipulates another one as well (such as happiness) • EXAMPLE: • Giving people positive feedback designed to increase their self-esteem may also increase their happiness • Researcher needs to take care to avoid these confounds • Threatens construct validity of manipulation

  6. Person Confounds • When individual differences covary with the IV and are related to the DV. • Not very problematic in true experiments….. WHY??? • Threatens internal validity

  7. Procedural Confounds • An unintentional manipulation of 2 or more things at once • Importantly, the confounding variable must be manipulated with the IV for there to be a problem. • Example: • Testing if crowding impairs cognitive functioning • One group in crowded room in lab • Other group in not crowded room down the hall • Threatens internal validity

  8. Apply It! • Confounding variables exercise

  9. Allow Observation of the Invisible • Researchers can observe things they could never observe any other way • The Implicit Association Test is designed to tap automatic associations between concepts and attributes (e.g., male:science female:liberal arts) • Do our true views always agree with our stated views? • How can we measure implicit stereotypes? • Implicit Association Test (IAT) • Are certain concepts more easily paired with one another concepts?

  10. IAT • Explores individuals' unconscious beliefs & preferences • Social psychologists find most people unwilling or unable to report their real attitudes towards groups (e.g., African-Americans, the elderly, women, homosexuals) • Take the IAT • https://implicit.harvard.edu/implicit/demo/

  11. Implicit Association Test

  12. Web Test Results • Race • 75% of White participants showed pro-White/anti-Black preference • 42% of Black participants showed pro-White/anti-Black preference • Age • preference for young over old, held by old and young, the strongest effect yet observed. • Gender+Career and Gender+Science • Males and females equally linked women to ‘home’ and ‘Liberal Arts’ and men to ‘career’ and ‘Science.’

  13. Implicit Stereotypes Mahzarin Banaji "I was taken aback by my inability to make the intended association, the difficulty in making the counter-stereotypical association between, say, female and career, or male and home." “If we are aware of our biases, we can correct for them—as when driving a car that drifts to the right, we steer left to go where we intend." -- Mahzarin Banaji

  14. Provide Information About Interactions • Interaction information • The effect of 1 variable depends on the level of the other variable • Social facilitation/inhibition • Variables: Presence of others and type of task • Another example: • Bower, Monteiro & Gilligan • SS learned a word list in either happy or sad mood • Asked to recall the list in either happy or sad mood • People only recalled the list well if they were in the same mood as they were when they learned it!

  15. Minimize Noise • Noise is random variation that exists in all conditions of an experiment • It is not a confound • It is not a threat to internal validity • Disadvantages of noise • Harder to find differences among conditions of and experiment • Advantages of noise • experiment is more lifelike, i.e, less artificial.

  16. Concept Check Dr. Doolittle wants to test his hypothesis that petting causes anxiety in cats. He randomly assigns cats to the petting and no-petting condition. Both conditions are run at the same time: the control condition in room A and the experimental condition in room B. The Dr. runs the control condition, where he observes each cat without petting. The research assistant runs the experimental condition where she observes each cat for five minutes while petting the cats. The researchers noted that the building’s thermostat was broken during the experiment, the two rooms were about equally but uncomfortably cold at times and too hot at other times because of the broken thermostat. The frequency of purring was tape recorded and measured in both groups. • 1) Any problems with the construct validity of the DV? • 2) Are there any confounds? • 3) Is there anything in this experiment that might qualify as noise?

  17. “Pseudo” Experiments • Missing control group • One-Group Pretest-posttest design Subjects IV DV Pretest DV Posttest DV Subjects IV

  18. Independent Groups Design • AKA: Between-Subjects designs • Simple Random Assignment • Membership in groups is determined entirely by chance • Flip a coin or random number table

  19. Basic Experiments • Posttest-Only design • Pretest-Posttest design Experimental Group Measure R Subjects Control Group Measure R Pretest Measure Experimental Group Posttest Measure R Subjects Control Group Posttest Measure Pretest Measure R

  20. Independent Groups Design • Matched pairs Random assignment • Used to ensure that groups are equal on subject characteristics • Matched variable is related to Dependent variable

  21. Repeated Measures Design • AKA: Within-subjects designs • In a repeated measures design, all participants are exposed to all conditions

  22. Repeated Measures Design • Advantages • Fewer participants needed • Perception experiments • Very sensitive to group differences • Participants in the groups are matched on every characteristic except the IV • Disadvantages • Order effects

  23. Within Subjects Disadvantages • Order effects • Fatigue effect • Practice effects • Contrast effect • Figuring it out!

  24. Between & Within Subjects Designs • Between subjects • Each participant is in only one group • Within subjects • Participants are in all conditions • Relationship between meaningfulness of material and learning it • Between subjects: • One group given meaningful material and tested another group low meaningful material and tested • Within subjects: • All subjects read low-meaningful material and tested and then given high meaningful material and tested

  25. Dealing with Order Effects • Counterbalancing • Presenting conditions in different orders to different participants • Complete counterbalancing • All possible orders are included • Can check if order influences results • The number of orders is groups! • 4 groups = 4! = 4 x 3 x 2 x 1 = 24. Low meaningfulness Recall Measure High meaningfulness Recall Measure High meaningfulness Recall Measure Low meaningfulness Recall Measure

  26. Counterbalancing • Latin squares • A limited set of orders constructed to ensure that • 1) each condition appears at each ordinal position • 2) each condition precedes and follows each condition once • Example: • Latin squares with 4 conditions

  27. Case 1 • You are studying effects of brain lesions and practice on how well rats run mazes. • Brain lesions would be best studied as a between-subject variable (they occur between subjects in real life) • However, practice could be studies within-subjects (practice occurs “within subjects” in real life)

  28. Case 2 • You are studying the effects of subliminal messages and marijuana on creativity. • You expect that if subliminal messages have an effect, it will be so small that only a within subjects design could detect it. • However, you feel that oral ingestion of marijuana should not be studied in a within-subjects design because of huge carryover effects.

  29. Mixed-Model Designs • Combines between and within subjects designs • Case 1 • Brain lesion: Between-subjects variable • Practice: within-subjects variable • Case 2 • Marijuana: Between-subjects variable • Subliminal messages (creative & neutral): Within-subjects variable • Very common design

  30. Laboratory Demonstration: Cola Taste Test

More Related