experimental design: single factor designs
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Experimental Design: Single factor designs. Psych 231: Research Methods in Psychology. Announcements. Reminder: your group project experiment method section is due in labs this week Remember to download, print and READ the class exp articles. Methods of Controlling Variability. Comparison - PowerPoint PPT PresentationTRANSCRIPT
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Experimental Design: Single factor designs
Psych 231: Research Methods in Psychology
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Announcements
Reminder: your group project experiment method section is due in labs this week
Remember to download, print and READ the class exp articles
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Methods of Controlling Variability
Comparison Production Constancy/Randomization
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Methods of Controlling Variability
Comparison – An experiment always makes a comparison, so it
must have at least two groups• Sometimes there are control groups
– This is typically the absence of the treatment» Without control groups if is harder to see what is really
happening in the experiment» it is easier to be swayed by plausibility or inappropriate
comparisons• Sometimes there are just a range of values of the IV
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Methods of Controlling Variability
Production– The experimenter selects the specific values of the
Independent Variables • Need to do this carefully
– Suppose that you don’t find a difference in the DV across your different groups
» Is this because the IV and DV aren’t related?» Or is it because your levels of IV weren’t different
enough
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Methods of Controlling Variability
Constancy/Randomization– If there is a variable that may be related to the
DV that you can’t (or don’t want to) manipulate• Control variable: hold it constant• Random variable: let it vary randomly across all of the
experimental conditions
– But beware confounds, variables that are related to both the IV and DV but aren’t controlled
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Experimental designs
So far we’ve covered a lot of the about details experiments generally
Now let’s consider some specific experimental designs.– 1 Factor, two levels– 1 Factor, multi-levels– Factorial (more than 1 factor)– Between & within factors
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Poorly designed experiments
Example: Does standing close to somebody cause them to move?– So you stand closely to people and see how long before
they move
– Problem: no control group to establish the comparison group (this design is sometimes called “one-shot case study design”)
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Single variable – One Factor designs
1 Factor (Independent variable), two levels– Basically you want to compare two treatments
(conditions)– The statistics are pretty easy, a t-test
T-test = Observed difference btwn conditions
Difference expected by chance
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1 factor - 2 levels
Example– How does anxiety level affect test performance?
• Two groups take the same test– Grp1 (moderate anxiety group): 5 min lecture on the
importance of good grades for success– Grp2 (low anxiety group): 5 min lecture on how good
grades don’t matter, just trying is good enough
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1 factor - 2 levels
participants
Low
Moderate Test
Test
Random Assignment
Anxiety Dependent Variable
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Single variable – one Factor
anxiety
low moderate
8060
low moderatete
st p
erfo
rman
ce
anxiety
One factor
Two levels
Use a t-test to see if these points are statistically different
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Single variable – one Factor
Advantages:– Simple, relatively easy to interpret the results– Is the independent variable worth studying?
• If no effect, then usually don’t bother with a more complex design
– Sometimes two levels is all you need• One theory predicts one pattern and another predicts a
different pattern
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Single variable – one Factor
Disadvantages:– “True” shape of the function is hard to see
• interpolation and extrapolation are not a good idea
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Interpolation
low moderate
test
per
form
ance
anxiety
What happens within of the ranges that you test?
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Extrapolation
low moderate
test
per
form
ance
anxiety
What happens outside of the ranges that you test?
high
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Poorly designed experiments
Example 1: – Testing the effectiveness of a stop smoking
relaxation program– The subjects choose which group (relaxation or no
program) to be in
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Poorly designed experiments Non-equivalent control groups
participants
Traininggroup
No training (Control) group
Measure
Measure
Self Assignment
Independent Variable
Dependent Variable
RandomAssignment
– Problem: selection bias for the two groups, need to do random assignment to groups
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Poorly designed experiments
Example 2: Does a relaxation program decrease the urge to smoke?– Pretest desire level – give relaxation program – posttest
desire to smoke
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Poorly designed experiments One group pretest-posttest design
participants Pre-test Training group
Post-testMeasure
Independent Variable
Dependent Variable
Dependent Variable
– Problems include: history, maturation, testing, and more
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1 Factor - multilevel experiments
For more complex theories you will typically need more complex designs (more than two levels of one IV)
1 factor - more than two levels– Basically you want to compare more than two
conditions– The statistics are a little more difficult, an ANOVA
(analysis of variance)
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1 Factor - multilevel experiments
Example (same as earlier with one more group)– How does anxiety level affect test performance?
• Three groups take the same test– Grp1 (moderate anxiety group): 5 min lecture on the
importance of good grades for success– Grp2 (low anxiety group): 5 min lecture on how good
grades don’t matter, just trying is good enough– Grp3 (high anxiety group): 5 min lecture on how the
students must pass this test to pass the course
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1 factor - 3 levels
participants
Low
Moderate Test
Test
Random Assignment
Anxiety Dependent Variable
High Test
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1 Factor - multilevel experiments
anxiety
low mod high
8060 60
low modte
st p
erfo
rman
ceanxiety
high
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1 Factor - multilevel experiments
Advantages– Gives a better picture of the relationship (function)– Generally, the more levels you have, the less you have
to worry about your range of the independent variable
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Relationship between Anxiety and Performance
low moderate
test
per
form
ance
anxiety
2 levels
highlow modte
st p
erfo
rman
ce
anxiety
3 levels
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1 Factor - multilevel experiments
Disadvantages– Needs more resources (participants and/or stimuli)– Requires more complex statistical analysis (analysis of
variance and pair-wise comparisons)
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Pair-wise comparisons
The ANOVA just tells you that not all of the groups are equal.
If this is your conclusion (you get a “significant ANOVA”) then you should do further tests to see where the differences are– High vs. Low– High vs. Moderate– Low vs. Moderate
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Next time
Adding a wrinkle: between-groups versus within-groups factors
Read chapter 11