Single-Factor ANOVA w/ SPSS
Q560: Experimental Methods in Cognitive Science
Lecture 9
The Logic of ANOVA
F=
treatment effect + chance
chance
If there is no effect due to treatment:
F ≈ 1.00.
If there is a significant effect due to treatment:
F > 1.00.
The denominator of the F-ratio is also called the
error term (it measures only unsystematic
variance).
Partitioning of Variance/df
Total Variance
Between-treatments
variance:
Within-treatments
variance:
1)  Treatment effect
1)  Individual diffs
2)  Error or chance
(excluding indiv
differences)
2)  Error or chance
Between-subjects
variance:
1)  Individual diffs
Error variance:
1)  Error or chance
(excluding indiv
differences)
Oneway Example: Category Learning
Example: Learning categories by active or
passive exploration
•  Training/Testing phases
•  Does performance on test depend on how
you learned the categories?
•  Random
•  Generate
•  Yoked
Experiment 1
Condition
Exploration
Exemplar Sampling
Random
Passive
Uniform
Generate
Active
“Intelligent”
Yoked
Passive
“Intelligent”
480 Training trials (feedback), followed by 480 test trials (no feedback)
Step 1: State hypotheses
H0: µ1 = µ2 = µ3.
H1: At least one µ is different.
Oneway ANOVA with SPSS using category learning
data.... (data and syntax posted w/ today’s
lecture)
Remember: ANOVA Cribsheet is posted with today’s
lecture
Repeated Measures Example: Craik and
Lockhart’s Levels of Processing
An SPSS example with Craik and Lockhart’s (1972) Levels of
Processing Framework
Data and syntax posted on today’s lecture
[LOP.dat] [LOP.sps]