Chapter 8
Experimental Design:
Dependent Groups and
Mixed Groups Designs
Dependent Groups Designs
Matched Designs
 Within-Participants Designs

– More than one IV – WP Factorial Design
Repeated Measures/With-Participants
Design

Each participant is his or her own control.
– Increased statistical power (the likelihood of detecting an effect
if one is present).
– More economical since fewer subjects are needed

Often the DV is assessed at multiple time points such as
before and after a “treatment”
– Repeated Measures

Repeating treatment conditions enables the participants
to identify what is being manipulated.
– Demand Characteristics
 Concern over demand characteristics prevents more widespread use of
repeated measures designs
Repeated Measures/WithinParticipants Design

The potential for increased economy and statistical
power is weighed against the potential threats to
internal validity
– Confounds - conditions that vary systematically with
changes in the level of the independent variable
 When present in an experiment, it is impossible to
tell whether changes in the dependent variable
resulted from the different levels of the
independent variable or from the different levels of
the confounded variable.
– History
– Maturation
– Testing
History
Anything that happens between the
pretest and posttest that is not part of the
experimental situation
 The longer the interval between pretest
and posttest, the greater the potential for
history effect
 It is important to know that this is not
specifically time passage, but events that
occur during that time

– Equipment issues
– Life events
To Minimize History Effects
Shorten the interval between the pretest
and posttest, so things will not happen
 Control the environment the pretest and
posttest as much as possible

Maturation

Internal processes that occur as a function
of the passage of time
– Growth and aging processes
– Motivational effects such as practice and
fatigue

Especially a problem in:
– Longitudinal research
– Educational research
– Therapy research
To Minimize Maturation Effects
Minimize the interval of time between
pretest and posttest
 Keep all experimental conditions identical
during pretest and posttest

Testing

Taking a test once affects scores on the
second test
– Taking a pretest affects scores on a posttest
 Stroop
To Minimize Testing Effects
Use alternate forms, if available
 Lengthen the interval between pretest and
posttest

Determining the Levels of the
Factor
There should be enough levels to represent the
range of values of the treatment variable.
 There should be enough levels to show the
exact nature of the relationship being tested.
 A true control condition is one in which the
treatment variable is absent.
 Certain problems arise when manipulating the IV
– Carry Over Effects

Carry Over Effects

Because each participant experiences
multiple treatment combinations,
experiencing the early treatments can
affect responses to subsequent treatments
– Also called “transfer effects”
 Order Effects
 Differential Order Effects
Dealing with Order Effects

Randomly determine the order of treatments
– Difficult to do unless you have a large number of
participants.

Completely counterbalanced approach
– Requires that each condition occurs equally
often, and precedes and follows all other
conditions the same number of times.

Incomplete counterbalancing
– Requires that each condition occurs equally
often.
Counterbalancing
Participant
1
2
3
4
5
6
Order of
LA MA
MA HA
HA LA
LA HA
MA LA
HA MA
Conditions
HA
LA
MA
MA
HA
LA
Differential Order Effects

When some levels of the IV may
irreversibly influence the DV
– Certain orders may permanently change the
individuals
 Example – A teaching technique

Cannot be controlled by counterbalancing
– Need to do a Between-Subjects Design
Repeated Measures Designs
ADVANTAGES
 Require fewer subjects

Take less time to complete

More powerful than between
subjects designs because each
participant is compared with
him/herself
– Error variance is reduced
DISADVANTAGES
 Problems posed by history,
testing, and maturation
 Problems posed by multiple
treatment interference effects
– Differential Order Effects
Example – THC, Alcohol &
Driving
Many studies have shown that both THC
and alcohol impair driving ability.
 No studies have compared the two drugs,
nor have any studies examined subjective
experiences of the drugs while driving
 How do we do this study with a within
subjects design?

Starting with the Title
Data Analysis - Partitioning the Variance
The treatment effect is estimated from
differences within subjects rather than
between subjects.
 Between subjects variance reflects
differences between subjects, NOT due to
the treatment.

Analysis of Variance Summary Table
Between subjects variance is listed first
and then removed from further
consideration
 Within subjects variance is partitioned into
the variance due to the treatment and
error variance (variance due to chance
factors).
 The F ratio compares the variance due to
the treatment (numerator) to the error
variance (denominator).

Interpretation of the Results of ANOVA
Calculated F is compared to critical F:
If calculated f is equal to or greater than
critical F:
 F is significant


There is a significant difference among the
means of the different treatment levels
Get it?
Data Analysis
Results
Discussion
Describes the outcome of the research in
words
– Briefly summarize what you found
 Integrates the outcome of the study with
previous research findings
– Here is how you data fit in to the larger
literature base
 Draws conclusions
– Based on theory or practical application
 May present suggestions for future research
– Limitations

Discussion – Explaining the
effect
Discussion – Implications &
Meaning
Discussion – Addressing a
limitation
Discussion – A summary of the
results that happens to be a
conclusion
Discussion – Addressing a
limitation
Example – 2X2 WS Factorial
Question/Problem
Increase in the coadministration of alcohol
and caffeine (energy drinks) in college
students
 Little research on the interactive effects.
 How does caffeine influence the effects of
alcohol on information processing tasks?

Methods
Procedures & Results
Mixed Designs
Have at least two independent variables
 At least one variable is a between subjects
factor.
 At least one variable is a within subjects
factor.

Reasons for Using Mixed
Designs
Repeated measures factors are desirable
because they require fewer participants
and can take less time.
 Some variables are manipulated between
participants to reduce or prevent fatigue,
interference effects, or demand
characteristics.
 Sometimes participant variables are
studied
(e.g., gender, handedness, etc.)

Example - Our THC and STM
Study
Problem – The effects of THC Intoxication on the
ability to do occupational tasks requiring STM
 Research Hypothesis – THC intoxication will impair
STM
 IV – Three smoked THC doses
– 0%, 5%, 10%
 DV – Span test for words at different time
intervals
– 15min, 1hr and 3hrs

Descriptives
Testing the WP Factor
Testing Levels of WP Factor
Testing the BS Factor
Post Hoc Test on the BS Factor
Matched groups designs
Provides the added power of dependent
groups designs and eliminates the
problem of carryover effects.
 Matching variables – participants are
matched, or made equivalent, on variables
that correlate with the DV.
 By using appropriate matching variables
the error variance due to individual
differences is reduced and power is
increased.
