1. Factorial Designs
• So far: basic designs (one IV, one DV)
• Now: more than one IV (still one DV)

2. 2 x 3 Factorial Design
Independent Variable A
A1 A2 A3
A1 B1 A2 B1 A3 B1 B1
B1 Cell mean Marginal
mean
IV B A1 B2 A2 B2 A3 B2 B2
B2 Marginal
mean
A1 A2 A3
Marginal Marginal Marginal
mean mean mean

3. 2 types of effects
• Main Effect - The influence of one
Independent variable in a factorial design
• Interaction Effect - joint influence of two or
more IVs on the DV
– The effect of one IV depends on the level of
another IV.

4. Example
• Study examining gender (M-F) and
intervention to improve test-taking skills
• 3 IV levels
– control (no intervention)
– reading material (instructional booklet)
– personalized tutoring

5. Intervention
Control Booklet Tutoring
A1 B1 A2 B1 A3 B1
Male
A1 B2 A2 B2 A3 B2
Female

6. What would a main effect of gender
look like?
100
90
80
70
60
Male
50
Female
40
30
20
10
0
Control Reading Tutoring

7. What would a main effect of
intervention look like?
120
100
80
Male
60
Female
40
20
0
Control Reading Tutoring

8. What would an interaction look like?
120
100
80
Male
60
Female
40
20
0
Control Reading Tutoring

9. What should we interpret?
• If one main effect - report it
• 2 main effects – report both
• BUT if there’s an interaction…
– Only interpret/report the interaction
– Because the effect of test-taking intervention
depends on gender

10. Interaction
120
100
80
Male
60
Female
40
20
0
Control Reading Tutoring

11. Combining Between and Within
Participant Designs
• Factorial design based on a mixed model
• -or- mixed model design
• IVs can be either between-groups
(e.g., gender) or within-groups (a.k.a.
repeated)

12. Advantages of Factorial Designs
• Can test more than 1 hypothesis at a time
• Able to deal with extraneous variables
– Build into design and test outright
• Increases precision b/c it evaluates more variables at
once
• Allows researcher to understand interactive effects
of variables

13. Disadvantages of Factorial Designs
• Gets messy with more than 2 IVs
• Requires more participants (N per cell)
• More difficult to simultaneously manipulate
all IVs when you have more of them

14. Choosing an Experimental Design
• Depends on…
• Research question
• Nature of variables you are investigating
• We have discussed design building blocks
• Page 255: guiding questions

15. Chapter 7
Control Techniques in Experimental
Research

16. Overview
• Control at the beginning of experiment
– Random assignment Create equivalent
– Matching experimental groups
• Control during the experiment
– Counterbalancing Treat groups
the same
– Controlling for participant effects during the
– Controlling for experimenter effects experiment

17. Random Assignment
• Not to be confused with random sampling!
• In reality, random sampling is rarely used in
experimental research
• Generalize on the basis of multiple studies
• With different kinds of samples/settings

18. Random Assignment
• a.k.a randomization
– Most important of all control methods
– Only technique for controlling both known
and unknown extraneous variables

19. Random Assignment
• Quiz time:
• How does randomization eliminate systematic
bias in experiments (produce control)?
– All variables distributed in approximately the same
manner in all groups
– Influence of extraneous variables is held constant

20. Random Assignment
• Sample Size
– It is possible for random assignment to fail
– rare with a large enough sample size (N > 30)

21. Random Assignment
• Ways of achieving randomization
– Table of random numbers
Text pp. 203-207
– Randomizer.org
– Draw out of a hat
– Be creative – flip a coin/lottery/etc
• www.Randomizer.org

22. Matching
• Equate participants on one or more selected
variables
• Matching Variable: The extraneous variable
used in matching
• Useful when random assignment is not
possible

23. Methods for Matching Participants
• Holding variables constant
• Building the extraneous variable into the
design
• Yoked control
• Equating participants

24. Matching by Holding Variables
Constant
• Hold extraneous variable constant for all
groups in the experiment
• All participants in each treatment group will
have same degree or type of extraneous
variable
• Requires selection criteria for participant
sample

25. Build Extraneous Variable into the
Research Design
• Especially useful if you are interested in:
– Differences produced by the levels of the
extraneous variable
– Interaction between levels of IV and levels of
extraneous variable
• Sound familiar?
– What kind of research design would this be?

26. Example: Effect of a study skills intervention
on college grades in a Quantitative Methods
course…
Intensive tutoring program Study packets (usual)
But the literature suggests that learning style may affect how students
respond to different study skills training methods.
Learning style is a potential confounding extraneous variable….but we can
build it in to the design!
Learning Style
Visual Auditory Kinesthetic
Intervention
Intensive
tutoring
program
Study packets

27. Matching by Yoked Control
• Match participants on the basis of the
sequence of administering an event
• Each control participant is “yoked” to an
experimental participant
• Controls for the possible influence of
participant-controlled events
• Example: Sklar & Anisan (1979)
– stress and immune response

28. Matching by Equating Participants
Precision control
• Match each participant in experimental group
with a participant in control group on
variable(s) of concern
• Example: Scholtz (1973) compared defense
styles in suicide attempt vs. no attempt

29. Matching by Equating Participants
• Precision Control Advantage
– Groups are equated on matching variables
• Precision Control Disadvantages
– How do you know which variables are critical?
– Difficulty of finding matched participants increases
exponentially as number of matching variables increases
– Matching limits generalizability of results
– Some variables are difficult to match
• Example: prior psychotherapy
– Matching can only be as accurate as the available
measurement of the matching variable