This document discusses non-experimental research designs, including correlational, quasi-experimental, developmental, and small-N designs. Correlational designs examine relationships between variables without manipulation. Quasi-experiments approximate true experiments but lack full control or random assignment. Developmental designs study age-related changes using cross-sectional, longitudinal, or cohort-sequential methods. Small-N designs involve in-depth analysis of a small number of cases. While not as rigorous as experiments, these non-experimental designs are still valuable when manipulation is not possible or ethical.

1. Non-Experimental designs:
Correlational & Quasi-experimental
Psych 231: Research
Methods in Psychology

2. Non-Experimental designs:
Correlational & Quasi-experimental
Psych 231: Research
Methods in Psychology

3. Announcements
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4. Non-Experimental 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

5.  For this example, we
have a linear
relationship, it is
positive, and fairly
strong
Correlational designs
 Looking for a co-occurrence relationship between two
(or more) variables
 Example 1: Suppose that you notice that the more you
study for an exam, the better your score typically is.
 This suggests that there is a relationship between
study time and test performance.
 We call this relationship a correlation.
 3 properties: form, direction, strength
Y
X
1
2
3
4
5
6
1 2 3 4 5 6
Hours
study
X
Exam
perf.
Y
6 6
1 2
5 6
3 4
3 2

6. Form
Non-linear
Linear
Y
X
Y
X
Y
X
Y
X

7. Direction
Positive
• X & Y vary in the same
direction
Y
X
Negative
• X & Y vary in opposite
directions
Y
X

8. Strength: Pearson’s Correlation Coefficient r
r = 1.0
“perfect positive corr.”
r = -1.0
“perfect negative corr.”
r = 0.0
“no relationship”
-1.0 0.0 +1.0
The farther from zero, the stronger the relationship

9. Correlational designs
 Advantages:
 Doesn’t require manipulation of variable
• Sometimes the variables of interest can’t be manipulated
 Allows for simple observations of variables in
naturalistic settings (increasing external validity)
 Can look at a lot of variables at once
Want some examples?

10.  Disadvantages:
 Do not make casual claims
• Third variable problem
• Temporal precedence
• Coincidence (random co-occurence)
• r=0.52 correlation between the
number of republicans in US senate
and number of sunspots
• From Fun with correlations
• See also Spurious correlations
Correlational designs
 Correlational results are often misinterpreted
Correlation is not causation blog posts:
Internet’s favorite phrase
Why we keep saying it
Minute physics (~4 mins)

11. Misunderstood Correlational designs
 Example 2: Suppose that you notice that kids
who sit in the front of class typically get higher
grades.
 This suggests that there is a relationship between
where you sit in class and grades.
Daily Gazzett
Children who sit in the
back of the classroom
receive lower grades
than those who sit in
the front.
Possibly implied: “[All] Children who sit in the
back of the classroom [always] receive lower
grades than those [each and every child] who sit
in the front.”
Incorrect interpretation: Sitting in the back of the
classroom causes lower grades.
Better way to say it: “Researchers X and Y found
that children who sat in the back of the
classroom were more likely to receive lower
grades than those who sat in the front.”
Other examples:
Psych you mind | PsyBlog

12. Non-Experimental 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

13. Quasi-experiments
 What are they?
 Almost “true” experiments, but with an inherent
confounding variable
 General types
• An event occurs that the experimenter doesn’t
manipulate or have control over
• Flashbulb memories for traumatic events
• Program already being implemented in some schools
• Interested in subject variables
• high vs. low IQ, males vs. females
• Time is used as a variable
• age
Relatively accessible article: Harris et al
(2006). The use and interpretation of Quasi-
Experimental studies in medical informatics

14. Quasi-experimental designs
Example: The Freshman 15 (CBS story) (Vidette story)
• Is it true that the average freshman gains 15
pounds? (Wikipedia)
• Recent research says ‘no’ – closer to 2.5 – 3 lbs
• Looked at lots of variables, sex, smoking, drinking,
etc.
• Also compared to similar aged, non college students
• College student isn’t as important as becoming
a young adult
For a nice reviews see, Zagorsky & Smith (2011) & Brown
(2008)
Note: the original study was Hovell, Mewborn, Randle, &
Fowler-Johnson (1985) (note: they reported the avg gain as 8.8 lbs)

15. Quasi-experiments
 Nonequivalent control group designs
 with pretest and posttest (most common)
(think back to the second control lecture)
participants
Experimental
group
Control
group
Measure
Measure
Non-Random
Assignment
Independent
Variable
Dependent
Variable
Measure
Measure
Dependent
Variable
– But remember that the results may be compromised
because of the nonequivalent control group (review threats
to internal validity)

16. Quasi-experiments
 Advantages
 Allows applied research when experiments not
possible
 Threats to internal validity can be assessed
(sometimes)
 Disadvantages
 Threats to internal validity may exist
 Designs are more complex than traditional
experiments
 Statistical analysis can be difficult
• Most statistical analyses assume randomness

17. Quasi-experiments
 Program evaluation
– Systematic research on programs that is conducted to
evaluate their effectiveness and efficiency.
– e.g., does abstinence from sex program work in schools
– Steps in program evaluation
– Needs assessment - is there a problem?
– Program theory assessment - does program address the
needs?
– Process evaluation - does it reach the target population? Is it
being run correctly?
– Outcome evaluation - are the intended outcomes being
realized?
– Efficiency assessment- was it “worth” it? The the benefits
worth the costs?

18. Developmental designs
 Used to study changes in behavior that occur
as a function of age changes
 Age typically serves as a quasi-independent
variable
 Three major types
 Cross-sectional
 Longitudinal
 Cohort-sequential
Video lecture (~10 mins)

19. Developmental designs
 Cross-sectional design
 Groups are pre-defined on the basis of a pre-
existing 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

20.  Cross-sectional design
Developmental designs
 Advantages:
• 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

21.  Cross-sectional design
Developmental designs

22.  Longitudinal design
Developmental designs
 Follow the same individual or group over time
• Age is treated as a within-subjects variable
• 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
• Changes in performance are compared on an
individual basis and overall
Age 11
time
Age 20
Age 15

23. Longitudinal Designs
 Example
 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
• Data collected in:
• 1957, 1964, 1975, 1992, 2004, 2011

24.  Longitudinal design
Developmental designs
 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)

25.  Longitudinal design
Developmental designs
 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
 Baby boomers
 Generation X
 Mellennials
 Generation Z

26. Developmental designs
 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
 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
 Cohort-sequential design

27. Developmental designs
 Cohort-sequential design
Time of measurement
1975 1985 1995
Cohort A
Cohort B
Cohort C
Cross-sectional
component
1970s
1980s
1990s
Age 5 Age 15 Age 25
Age 5 Age 15
Age 5
Longitudinal component

28. Developmental designs
 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)
 Disadvantages:
• Still time-consuming
• Need lots of groups of participants
• Still cannot make causal claims
 Cohort-sequential design

29. Non-Experimental 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

30. Small N designs
 What are they?
 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, animal studies,
expertise, etc.)

31. Small N designs
 In contrast to Large N-designs (comparing aggregated
performance of large groups of participants)
 One or a few participants
 Data are typically not analyzed statistically; rather rely
on visual interpretation of the data

32. Small N designs
 Observations begin in the absence of treatment
(BASELINE)
 Then treatment is implemented and changes in
frequency, magnitude, or intensity of behavior are
recorded
Steady state (baseline)
= observation
Treatment
introduced

33. Small N designs
 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)
Steady state (baseline)
Transition
steady state
= observation
Treatment
introduced
Reversibility
Treatment
removed

34. Small N designs
 Before introducing treatment (IV), baseline needs
to be stable
 Measure level and trend
 Level – how frequent (how intense) is behavior?
• Are all the data points high or low?
 Trend – does behavior seem to increase (or decrease)
• Are data points “flat” or on a slope?
Unstable Stable

35. ABA design
 ABA design (baseline, treatment, baseline)
– The reversibility is necessary, otherwise
something else may have caused the effect
other than the IV (e.g., history, maturation, etc.)
Steady state (baseline)Transition steady state Reversibility
 There are other designs as well (e.g., ABAB see
figure13.6 in your textbook)

36. Small N designs
 Advantages
 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 non-
treatments
 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

37. Small N designs
 Disadvantages
 Difficult to determine how generalizable the effects
are
 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

38. Small N designs
 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