Title: Experimental Design: Single factor designs
1Experimental Design Single factor designs
- Psych 231 Research Methods in Psychology
2Announcements
- Reminder your group project experiment method
section is due in labs this week - Remember to download, print and READ the class
exp articles
3Methods of Controlling Variability
- Comparison
- Production
- Constancy/Randomization
4Methods 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
5Methods 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
6Methods 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
7Experimental 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
8Poorly 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)
9Single 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
101 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
111 factor - 2 levels
12Single variable one Factor
anxiety
80
60
13Single 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
14Single variable one Factor
- Disadvantages
- True shape of the function is hard to see
- interpolation and extrapolation are not a good
idea
15Interpolation
What happens within of the ranges that you test?
test performance
low
moderate
anxiety
16Extrapolation
What happens outside of the ranges that you test?
test performance
low
moderate
anxiety
17Poorly 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
18Poorly 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
19Poorly designed experiments
- Example 2 Does a relaxation program decrease the
urge to smoke? - Pretest desire level give relaxation program
posttest desire to smoke
20Poorly 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
211 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)
221 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
231 factor - 3 levels
241 Factor - multilevel experiments
60
251 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
26Relationship between Anxiety and Performance
271 Factor - multilevel experiments
- Disadvantages
- Needs more resources (participants and/or
stimuli) - Requires more complex statistical analysis
(analysis of variance and pair-wise comparisons)
28Pair-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
29Next time
- Adding a wrinkle between-groups versus
within-groups factors - Read chapter 11