Experimental Design: Single factor designs

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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 Methods
  section ( Appendix) and IRB protocol are due in
  labs this week

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Poorly designed experiments

• Bad design example 1 Does standing close to
  somebody cause them to move?
• hmm thats an empirical question. Lets see
  what happens if
• 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

Observed difference btwn conditions
T-test
Difference expected by chance

• Although there are several types of t-tests
• Depends on your design

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Poorly designed experiments

• Bad design example 2
• 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
Problem selection bias for the two groups, need
to do random assignment to groups

• Non-equivalent control groups

Self Assignment
Independent Variable
Dependent Variable
Training group
Measure
participants
No training (Control) group
Measure
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Poorly designed experiments

• Bad design example 3 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

Independent Variable
Dependent Variable
Dependent Variable
participants
Pre-test
Training group
Post-test Measure
Add another factor
Problems include history, maturation, testing,
and more
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1 factor - 2 levels

• Good design 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 dont matter, just trying is good
  enough

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1 factor - 2 levels
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Single variable one Factor
anxiety
80
60
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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 dont 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
What happens within of the ranges that you test?
test performance
low
moderate
anxiety
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Extrapolation
What happens outside of the ranges that you test?
test performance
low
moderate
anxiety
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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

• Good design example (similar to earlier ex.)
• 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 dont 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
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1 Factor - multilevel experiments
60
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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
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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