The document discusses various control techniques that can be used in experiments to minimize extraneous influences, including random assignment, counterbalancing, and controlling for participant and experimenter effects. It provides examples of how to match participants, counterbalance conditions, and implement blind studies to control for demand characteristics and experimenter biases. The control techniques are important for creating equivalent groups and treating groups the same during experiments.

1. Chapter 7: Control Techniques
October 23, 2012

2. Roadmap
• Discuss Reflection Assignment #2
– Due Nov. 1 via BlackBoard
• Coming Up: Exam #3 next Tuesday, Oct. 30
• Quick review
• Chapter 7

3. Factorial Designs
• When to use
• Main and interaction effects
• Effect patterns in data displays

4. Overview
Control Techniques
• 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

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

6. 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

7. 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?

8. Example: Effect of a study skills intervention
on 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

9. 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

10. 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

11. www.xkcd.com
CONTROL DURING THE EXPERIMENT

12. Control During the Experiment
• Must treat the different groups in the same
way during the experiment, except for
administration of the IV
• Why is this important?

13. • Control during the experiment
– Counterbalancing within-participants designs
– Controlling for participant effects
– Controlling for experimenter effects

14. Counterbalancing
• Used to control for sequencing effects in a
repeated measures (aka within-subjects) design
• Sequencing effects occur when participants
participate in more than one condition
• Two types of sequencing effects
– Order effect
– Carryover effect

15. Counterbalancing
• Order effect
– “Arises from the order in which treatment
conditions are administered to participants”
– Treatment/experiment exposure can influence
performance on subsequent tasks and measures
– Most common:
• Practice effect
• fatigue

16. Counterbalancing
• Carryover effect
– Performance in one condition is affected by the
condition that precedes it
– Example: Participant receives active drug before
the placebo, and the residual effects are still
present during placebo condition
• One strategy: “wash-out” period

17. Counterbalancing Techniques
• Randomized counterbalancing individual
• Intrasubject counterbalancing
• Complete counterbalancing group
• Incomplete counterbalancing

18. Types of Counterbalancing
• Randomized counterbalancing
– Sequence order is randomly determined for each
individual
– Just like random assignment to conditions
– You do not decide the sequence, must use a random process to decide
order

19. Types of Counterbalancing
• Intrasubject Counterbalancing
– When each participant receives all levels of the IV
more than one time
– Have participants take conditions first in one
order, then again in the reverse order
– Disadvantage: Participant burden is increased
• Must complete each condition more than once

20. Types of Counterbalancing
• Complete and Incomplete counterbalancing
– Group counterbalancing
– Determine possible sequences
– Randomly assign to sequence such that sequences
are distributed across groups rather than
individuals

21. Participant Effects
• Demand characteristics
– Cues in the experiment that might influence
participant behavior
• Positive self-presentation
– Motivation for participants to present themselves
in a positive light

22. Control of Participant Effects
• Deception
– Giving participants a bogus rationale for the
experiment
• Can range from minor deceit to more
elaborate schemes
Classic example: Milgram studies

23. Control of Participant Interpretation
• Previously discussed methods provide good
control for demand characteristics of study
• But how do we know what participants’
perceptions of our study are?
– Ask them!

24. Control of Participant Interpretation
• Retrospective Verbal Reports: after experiment
– Disadvantage: Participants might forget
perceptions by the end of the study
• Concurrent Verbal Reports: during experiment
– Solomon’s Sacrifice Groups
– Concurrent probing
– Think-aloud technique

25. Control of Experimenter Effects
• Experimenter effects
– The biasing influence that can be exerted by the
experimenter
• Data Recording errors--control
– Be careful
– Multiple observers and data recorders
– Keep experimenter blind to participants’ conditions
– Electronic or mechanical data recording*

26. Control of Experimenter Effects
• Experimenter Attribute Errors
– Some experimenters, because of their attributes,
produce more of an effect than other
experimenters
• Control technique:
– Experimenters should run all conditions
– Experimenters same on characteristics that might
affect DV

27. Control of Experimenter Effects
• Experimenter Expectancy Errors
– Experimenter’s expectations about the study
influence participant responses
Control techniques:
• Blind technique
• Partial blind technique
• Automation

28. Ideal:
Control Participant AND Experimenter Effects
• Double-Blind Placebo Method
– Participant and experimenter blind to condition
– “Devise manipulations that appear essentially
identical to research participants in all conditions”
– Example: Compare drug to identical sugar pill
(placebo)