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 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 dont find a difference in the
  DV across your different groups
• Is this because the IV and DV arent related?
• Or is it because your levels of IV werent
  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 cant (or dont 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 arent controlled

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Experimental designs

• So far weve covered a lot of the about details
  experiments generally
• Now lets 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

Observed difference btwn conditions
T-test
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 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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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

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

• 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

Independent Variable
Dependent Variable
Dependent Variable
participants
Pre-test
Training group
Post-test Measure

• 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 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