Non-Experimental designs
Psych 231: Research
Methods in Psychology
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Sometimes you just can’t perform a fully controlled
experiment
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Because of the issue of interest
Limited resources (not enough subjects, observations are too
costly, etc).
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Surveys
Correlational
Quasi-Experiments
Developmental designs
Small-N designs
This does NOT imply that they are bad designs
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Just remember the advantages and disadvantages of each
Non-Experimental designs
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Used to study changes in behavior that occur
as a function of age changes
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Age typically serves as a quasi-independent
variable
Three major types
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Cross-sectional
Longitudinal
Cohort-sequential
Developmental designs
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Cross-sectional design
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Groups are pre-defined on the basis of a preexisting variable
• Study groups of individuals of different ages at the
same time
• Use age to assign participants to group
• Age is subject variable treated as a between-subjects
variable
Age 4
Age 7
Age 11
Developmental designs
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Cross-sectional design
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Advantages:
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Can gather data about different groups (i.e., ages)
at the same time
Participants are not required to commit for an
extended period of time
Developmental designs
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Cross-sectional design
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Disavantages:
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Individuals are not followed over time
•
Cohort (or generation) effect: individuals of different
ages may be inherently different due to factors in the
environment
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Are 5 year old different from 15 year olds just because
of age, or can factors present in their environment
contribute to the differences?
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Imagine a 15yr old saying “back when I was 5 I
didn’t have a Wii, my own cell phone, or a
netbook”
Does not reveal development of any particular
individuals
Cannot infer causality due to lack of control
Developmental designs
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Longitudinal design
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Follow the same individual or group over time
•
Age is treated as a within-subjects variable
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Rather than comparing groups, the same individuals
are compared to themselves at different times
Changes in dependent variable likely to reflect
changes due to aging process
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Changes in performance are compared on an
individual basis and overall
time
Age 11
Age 15
Age 20
Developmental designs
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Example
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Wisconsin Longitudinal Study (WLS)
• Began in 1957 and is still on-going (50 years)
• 10,317 men and women who graduated from Wisconsin high schools
in 1957
• Originally studied plans for college after graduation
• Now it can be used as a test of aging and maturation
Longitudinal Designs
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Longitudinal design
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Advantages:
• Can see developmental changes clearly
• Can measure differences within individuals
• Avoid some cohort effects (participants are all from
same generation, so changes are more likely to be
due to aging)
Developmental designs
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Longitudinal design
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Disadvantages
• Can be very time-consuming
• Can have cross-generational effects:
• Conclusions based on members of one generation may
not apply to other generations
• Numerous threats to internal validity:
• Attrition/mortality
• History
• Practice effects
• Improved performance over multiple tests may be due to
practice taking the test
• Cannot determine causality
Developmental designs
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Cohort-sequential design
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Measure groups of participants as they age
• Example: measure a group of 5 year olds, then the
same group 10 years later, as well as another group
of 5 year olds
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Age is both between and within subjects
variable
• Combines elements of cross-sectional and longitudinal
designs
• Addresses some of the concerns raised by other designs
• For example, allows to evaluate the contribution of cohort
effects
Developmental designs
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Cohort-sequential design
Cross-sectional component
Time of measurement
1975
Cohort A
1970s
Cohort B
1980s
Cohort C
1990s
Age 5
1985
1995
Age 15
Age 25
Age 5
Age 15
Age 5
Longitudinal component
Developmental designs
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Cohort-sequential design
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Advantages:
• Get more information
• Can track developmental changes to individuals
• Can compare different ages at a single time
• Can measure generation effect
• Less time-consuming than longitudinal (maybe)
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Disadvantages:
• Still time-consuming
• Need lots of groups of participants
• Still cannot make causal claims
Developmental designs

Sometimes you just can’t perform a fully controlled
experiment


Because of the issue of interest
Limited resources (not enough subjects, observations are too
costly, etc).
•
•
•
•
•

Surveys
Correlational
Quasi-Experiments
Developmental designs
Small-N designs
This does NOT imply that they are bad designs

Just remember the advantages and disadvantages of each
Non-Experimental designs
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What are they?
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Historically, these were the typical kind of design
used until 1920’s when there was a shift to using
larger sample sizes
Even today, in some sub-areas, using small N
designs is common place
• (e.g., psychophysics, clinical settings, expertise, etc.)
Small N designs
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One or a few participants
Data are typically not analyzed statistically; rather rely
on visual interpretation of the data
Observations begin in the absence of treatment
(BASELINE)
Then treatment is implemented and changes in
frequency, magnitude, or intensity of behavior are
recorded
Small N designs
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Baseline experiments – the basic idea is to
show:
1. when the IV occurs, you get the effect
2. when the IV doesn’t occur, you don’t get the
effect (reversibility)
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Before introducing treatment (IV), baseline
needs to be stable
Measure level and trend
Small N designs
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Level – how frequent (how intense) is
behavior?
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Are all the data points high or low?
Trend – does behavior seem to increase (or
decrease)
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Are data points “flat” or on a slope?
Small N designs
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ABA design (baseline, treatment, baseline)
A
B
A
Steady state (baseline) | Transition steady state | Reversibility
– The reversibility is necessary, otherwise
something else may have caused the effect
other than the IV (e.g., history, maturation, etc.)
ABA design
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Advantages
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Focus on individual performance, not fooled by
group averaging effects
Focus is on big effects (small effects typically can’t
be seen without using large groups)
Avoid some ethical problems – e.g., with nontreatments
Allows to look at unusual (and rare) types of
subjects (e.g., case studies of amnesics, experts
vs. novices)
Often used to supplement large N studies, with
more observations on fewer subjects
Small N designs
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Disadvantages
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Effects may be small relative to variability of situation
so NEED more observation
Some effects are by definition between subjects
• Treatment leads to a lasting change, so you don’t get
reversals
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Difficult to determine how generalizable the effects
are
Small N designs
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Some researchers have argued that Small N
designs are the best way to go.
The goal of psychology is to describe behavior
of an individual
Looking at data collapsed over groups “looks” in
the wrong place
Need to look at the data at the level of the
individual
Small N designs