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Non-Experimental designs:Correlational and Quasi-experiments
Psych 231: Research Methods in Psychology
Announcements
Lab attendance is critical this week because group projects are being administered Attendance will be taken.
Don’t forget Quiz 8 (chapters 9& 10) due Tonight
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
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
For this example, we have a linear relationship, it is positive, and fairly strong
Form
Non-linearLinear
Y
X
Y
X
Y
X
Y
X
Direction
Positive
• X & Y vary in the same direction
Y
X
Negative
• X & Y vary in opposite directions
Y
X
Strength
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
Correlational designs
Advantages: Does not require manipulation of variable
• Sometimes the variables of interest cannot be manipulated Allows for simple observations of variables in
naturalistic settings (increasing external validity) Can look at a lot of variables at once
Example 2: The Freshman 15 (CBS story) (Vidette story)• Is it true that the average freshman gains 15 pounds?
• 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 studentsFor a nice review see Brown (2008)
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
Correlational designs
Correlational results are often misinterpreted
Correlation is not causation blog posts:Internet’s favorite phraseWhy we keep saying it
Misunderstood Correlational designs
Example 3: 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.”
Example from Owen Emlen (2006)Other examples:Psych you mind | PsyBlog
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
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
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
Quasi-experiments
Nonequivalent control group designs with pretest and posttest (most common)
(think back to the second control lecture)
participants
Experimentalgroup
Controlgroup
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)
Quasi-experiments
Program evaluation– Research on programs that is implemented to achieve
some positive effect on a group of individuals.– 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?
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
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
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
Cross-sectional design
Developmental designs
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 20Age 15
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
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)
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
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
Developmental designs
Cohort-sequential designTime of measurement
1975 1985 1995
Cohort A
Cohort B
Cohort CCro
ss-s
ectio
nal c
ompo
nent
1970s
1980s
1990s
Age 5 Age 15 Age 25
Age 5 Age 15
Age 5
Longitudinal component
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
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, expertise, etc.)
Small N designs
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
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)
Before introducing treatment (IV), baseline needs to be stable
Measure level and trend
Small N designs
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?
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.)
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
Small N designs
Disadvantages 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
Difficult to determine how generalizable the effects are
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