1. Control Techniques ♣ Chapter 9 Introduction  Randomization 
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Introduction 
Randomization 
Matching 
Counterbalancing 
Control of Participant Effects 
Control of Experimenter Effects 
Likelihood of Achieving Control

2. 9.0 Introduction
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Goal of experimentation ― identify the causal effect of the IV
Must have internal validity to do this
Internal validity requires control of confounding variables
Ways of achieving control
Design of the experiment
Statistical adjustments
Incorporate control techniques into the research design

3. 9.1 Randomization
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Randomization is a statistical control technique to equate groups of participants
This is the most important and basic control technique
Random selection —selecting people at random from a defined population
Insures that the sample selected is representative of the population
representative: sample P have the same characteristics
as the people in the population
Studies seldom if ever do this because of expense, etc

4. 9.1 Randomization
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Random assignment —randomly assigning participants to treatment groups
Provides maximum insurance that groups are equal
Equates groups because every person has an equal chance of being assigned to each group
Accomplishes this by randomly distributing the extraneous variables over the treatment groups
Fig Tab 9.1

5. 9.1 Randomization -3(end) Random Assignment Exercise
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Random Assignment Exercise
Randomly assign 40 children to four different drug conditions using the table of random numbers in appendix D Exhibit
Logon to and use the randomizer in this site to randomly assign 40 children to the four drug conditions

6. 9.2 Matching
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Uses of any of a variety of techniques to equate participants in the treatment groups on specific variables
Advantages of matching
Controls for the variables on which participants are matched
Increases the sensitivity of the experiment

7. 9.2 Matching -2(end) Matching by Holding Variables Constant 
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Matching by Holding Variables Constant 
Matching by Building the Extraneous Variable into the Research Design 
Matching by Yoked Control 
Matching by Equating Participants 

8. 9.2.1 Holding Variables Constant
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Matches on the variable held constant
Fig 9.2
Disadvantages
Restricts the population size
Restricts generalization to the type of participants in the study
9.2

9. 9.2.2 Building EV into Research Design
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Should be used only when you are interested in the effect of the effect of the extraneous variable
Fig 9.3
9.2

10. 9.2.3 Matching by Yoked Control
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Controls the temporal relationship between an event and a response
Brady (1958)
Emotional stress → Ulcer
Stress: Press a lever every 20-sec to avoid shock
Control: receive the same temporal sequence of shock
9.2

11. 9.2.4 Matching by Equating Participants
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Precision control —match case by case Fig 9.4
Disadvantages
Identifying the variables on which to match
Matching increases as the number of variables on which to match increases
Some variables difficult to match
Frequency distribution control —match on the overall distribution of the selected variables Fig 9.5
Disadvantage
Combination of variables may be mismatched
(e.g.) Age-IQ: (E) Old-high IQ, Young-low IQ
(C) Old-low IQ, Young-high IQ
9.2

12. 9.3 Counterbalancing Used to control sequencing effects
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Used to control sequencing effects
Type of Sequencing effects Fig 9.6
Order effect Tab 9.2
arising from the order in which the treatment conditions are administered to P
Carry-over effect Tab 9.3
occurs when performance in one treatment condition affects performance in another treatment condition
Counterbalancing procedures
Intrasubject Counterbalancing: The ABBA technique 
Intragroup Counterbalancing 

13. 9.3 Counterbalancing - intrasubject
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Intrasubject or ABBA technique — counterbalances on a case-by-case basis
Controls only for linear sequencing effects Tab 9.4
Nonlinear order effects can be controlled if you use the ABBA plus BAAB counterbalancing Tab 9.5
Can’t control nonlinear carry-over effects
9.3 ◄

14. 9.3 Counterbalancing – Intragroup -1
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Intragroup Counterbalancing
(e.g.) ABC ACB BAC BCA CAB CBA
Incomplete Counterbalancing (Latin square)
Participant Sequence
A B D C
B C A D
C D B A
D A C B
9.3 ◄

15. 9.3 Counterbalancing - Intragroup -2
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Intragroup Counterbalancing
Incomplete Counterbalancing (Latin square)
A B E C D D C E B A
B C A D E E D A C B
C D B E A A E B D C
D E C A B B A C E D
E A D B C C B D A E
9.3 ◄

16. 9.3 Counterbalancing - Intragroup -3(end)
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Intragroup Counterbalancing
Incomplete Counterbalancing (Latin square)
1,2,n,3,(n-1),4,(n-2),5,…
2,3,n+1,4,…
(e.g.) A B F C E D ( p. 282, 283 )
B C A D F E
C D B E A F
D E C F B A
E F D A C B
F A E B D C
9.3 ◄

17. 9.4 Control of Participant Effects -1
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Double Blind Placebo Model
Participants, Experimenter
Blind: the treatment condition administered to P
Deception ― giving the P a bogus rationale for the experiment
provide hypothesis : unrelated or orthogonal; false but plausible

18. 9.4 Control of Participant Effects -2(end)
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Perceptual Control, or Control of Participant Interpretation
Retrospective verbal report
postexperimental inquiry
Concurrent verbal report
sacrifice groups: stopped at a different point
concurrent probing: at the end of each trial
think-aloud technique

19. 9.5 Control of Experimenter Effects -1
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Control of Recording Errors
Aware of the necessity: ensure the accuracy
Kept blind
Mechanical or electronic device
Control of Experimenter Attribute Errors
Interact with treatment effect? Tab 9.6
Minimize: Control Attributes that correlate with DV

20. 9.5 Control of Experimenter Effects -2(end)
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Control of Experimenter Expectancy Error
The Blind Technique
The Partial Blind Technique
Automation