Transcript Experimental Psychology - University of Richmond

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.
Outline
• Strengths of True Experiments
• Simple Research Designs
Strengths of True Experiments
1.
2.
3.
4.
Eliminate Confounds
Allow Observation of the Invisible
Provide Information about Interactions
Minimize Noise
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
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
– Trying to assess depression- may assess age as well
• Researcher needs to take care to avoid these
confounds
• Threatens construct validity of manipulation
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
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
Observation of the
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)
• How can we measure implicit stereotypes?
– Implicit Association Test (IAT)
– Are certain concepts more easily paired with one
another concepts?
Implicit Association Test
• Timer:
• http://www.speedcubing.com/games/OnlineTimer
2.html
• See website:
https://implicit.harvard.edu/implicit/
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
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!
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.
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?
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
Initial
Sample
Group I
Independent
variable
Measure on
dependent variable
Group II
Independent
variable
Measure on
dependent variable
Random
Assignment
Basic Experiments
• Posttest-Only design
R
Experimental
Group
Measure
R
Control
Group
Measure
Subjects
• Pretest-Posttest design
R
Pretest
Measure
Experimental
Group
Posttest
Measure
R
Pretest
Measure
Control
Group
Posttest
Measure
Subjects
Independent Groups Design
• Matched pairs Random assignment
– Used to ensure that groups are equal on subject
characteristics
– Matched variable is related to Dependent
variable
Group I
Independent
variable
Initial
Sample
Match
Measure on
dependent variable
Random
Assignment
Group II
Independent
variable
Measure on
dependent variable
Repeated Measures Design
• AKA: Within-subjects designs
• In a repeated measures design, all participants are
exposed to all conditions
Level II
Independent
Variable
Initial
Sample
Measure on
Dependent
Variable
Level I
Independent
Variable
Measure on
Dependent
Variable
Measure on
Dependent
Variable
Level II
Independent
Variable
Measure on
Dependent
Variable
Random
Assignment
Level I
Independent
Variable
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
Within Subjects Disadvantages
• Order effects
– Fatigue effect
– Practice effects
– Contrast effects
• Figuring it out!
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
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
Low
meaningfulness
Recall
Measure
High
meaningfulness
Recall
Measure
High
meaningfulness
Recall
Measure
Low
meaningfulness
Recall
Measure
– The number of orders is groups!
• 4 groups = 4! = 4 x 3 x 2 x 1 = 24.
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
A
B
C
D
B
C
D
A
D
A
B
C
C
D
A
B
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 studied withinsubjects (practice occurs “within subjects”
in real life)
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.
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): Withinsubjects variable
• Very common design