Chapter 7: Control Techniques

October 23, 2012

Roadmap

• Discuss Reflection Assignment #2– Due Nov. 1 via BlackBoard

• Coming Up: Exam #3 next Tuesday, Oct. 30• Quick review• Chapter 7

Factorial Designs

• When to use• Main and interaction effects• Effect patterns in data displays

OverviewControl Techniques

• Control at the beginning of experiment– Random assignment– Matching

• Control during the experiment– Counterbalancing– Controlling for participant effects– Controlling for experimenter effects

Create equivalent experimental groups

Treat groups the same during the experiment

Methods for Matching Participants

• Holding variables constant• Building the extraneous variable into the

design• Yoked control• Equating participants

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

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?

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

Intensive tutoring program

Study packets

Inte

rven

tion

A

B

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

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

CONTROL DURING THE EXPERIMENTwww.xkcd.com

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?

• Control during the experiment– Counterbalancing within-participants

designs

– Controlling for participant effects– Controlling for experimenter effects

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

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

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

Counterbalancing Techniques

• Randomized counterbalancing• Intrasubject counterbalancing

• Complete counterbalancing• Incomplete counterbalancing

individual

group

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

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

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

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

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

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!

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

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*

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

Control of Experimenter Effects

• Experimenter Expectancy Errors– Experimenter’s expectations about the study

influence participant responses

Control techniques:• Blind technique• Partial blind technique• Automation

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)