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Non-Experimental designs:
Surveys & Correlational
Psych 231: Research
Methods in Psychology
Quiz 8 is due on Oct. 29th at midnight
 Running your group projects in labs this
week.


Please be there, your participation is
important!
Reminders
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
Mean = 74.1
Median = 74.5
Max = 96
Min = 40

Most common
errors

Between vs. within designs


Exam 2 results
Confounds vs. extraneous
random variables
Main effects vs. interactions

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 studies
• 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

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 studies
• Quasi-Experiments
Finish up
Start
• 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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Stage 1) Identify the focus of the study and select
your research method
Stage 2) Determining the research schedule and
budget
Stage 3) Establishing an information base
Stage 4) Identify the sampling frame
Stage 5) Determining the sample method and
sampling size

Review Probability and Non-Probability methods
• Voluntary response method

Importance of sample size
11 Stages of survey research


Sampling error - how is the sample different
from the population?
Confidence intervals
• “John Doe has 55% of the vote, with a margin of error ± 3%”
• Margin of error (that “± 3%” part)
• The larger your sample size, the smaller your margin
of error will be.

Response rate
• What proportion of the sample actually responded to
the survey?
• Hidden costs here - what can you do to increase
response rates
• Non-response error (bias)
• Is there something special about the data that you’re
missing (From the people who didn’t respond)?
Importance of sample size

Stage 6) Designing the survey instrument

Question construction: How the questions are
written is very important
• Clearly identify the research objectives
• Do your questions really target those research
objectives (think Internal and External Validity)?
• Take care wording of the questions
• Keep it simple, don’t ask two things at once, avoid
loaded or biased questions, etc.
• How should questions be answered (question type)?
11 Stages of survey research
Poor
Was the FDC negligent by ignoring the
warnings about Vioxx during testing
and approving it for sale?
Yes
Problem: a)emotionally
b) No
charged
c) words
Unsure
Do you favor eliminating the wasteful
excess in the public school budget?
a)
b)
c)
Yes
No
Unsure
Good
If the FDC knew that Vioxx caused
serious side effects during testing,
what should it have done?
a)Ban it from ever being sold
b)Require more testing before
approving it
c)Unsure
Do you favor reducing the public school
budget?
a)Yes
b)No
c)Unsure
Good and poor questions
Poor
Should senior citizens be given more
money for recreation centers and
food assistance programs?
a) Yes
b) No
Problem:
asks
c) Unsure
two
different questions
Good
Should senior citizens be given
more money for recreation centers?
a)
b)
c)
Yes
No
Unsure
Should senior citizens be given
more money for food assistance
programs?
a)
b)
c)
Yes
No
Unsure
Good and poor questions
Poor
Are you against same sex marriage
and in favor of a constitutional
amendment to ban it?
Good
What is your view on same sex
marriage?
a)
a)
b)
c)
Yes
No
Unsure
Problem: Biased in
more than one direction
b)
c)
I think marriage is a matter of
personal choice
I’m against it but don’t want a
constitutional amendment
I want a constitutional
amendment banning it
Problem: Asks two
questions
Good and poor questions

Question types

Open-ended (fill in the blank, short answer)
• Can get a lot of information, but
• Coding is time intensive and potentially
ambiguous

Close-ended (pick best answer, pick all that apply)
• Easier to code
• Same response alternatives for everyone
• Take care with your labels
• Decide what kind of scale
• Decide number/label of response
alternatives
Survey Questions
What is the best thing
about ISU?
What is the best thing about
ISU? (choose one)

1. Location

2. Academics

3. Dorm food

4. People who sell
things between
Milner and the
Bone

Decide what kind of rating scales
• Rating:
e.g., Likert scale
PSY 231 is an important course in the major.
1
Strongly
Agree
2
Agree
3
Neutral
4
Disagree
5
Strongly
Disagree
• Semantic differential:
Rate how you feel about PSY 231 on these dimensions
Important _____:
_____:
_____: _____:
_____: Unimportant
Boring
_____:
_____: _____:
_____: Interesting
_____:
• Nonverbal scale for children:
Point to the face that shows how you feel about the toy.
Survey Questions: Close-ended

Decide number/label of response alternatives
• Use odd number (mid point and equal # of responses above
and below the mid point)
• Questions should be uni-dimensional (each concerned with
only one thing)
• Labels should be clear
Survey Questions: Close-ended
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Stage 7) Pre-testing the survey instrument
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Stage 8) Selecting and training interviewers

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Fix what doesn’t seem to be working
For telephone and in-person surveys
Need to avoid interviewer bias
Stage 9) Implementing the survey
Stage 10) Coding and entering the data
Stage 11) Analyzing the data and preparing a
final report
11 Stages of survey research

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

Looking for a co-occurrence relationship
between two (or more) variables

Used for
• Descriptive research
• do behaviors co-occur?
• Predictive research
• is one behavior predictive of another?
• Reliability and Validity
• Does your measure correlate with others (and itself)?
• Evaluating theories
• Look for co-occurrence posited by the theory.
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

At a descriptive level this suggests that there is a
relationship between study time and test performance.

For our example, which variable is explanatory and which is
response? And why?

It depends on your theory of the causal relationship between
the variables


Explanatory variables (Predictor variables)
Response variables (Outcome variables)
Correlational designs
Y
6
Hours
study
Exam
perf.
X
Y
5
6
1
6
2
4
5
6
2
3
4
1
3
2
Scatterplot

3
1
2
3
4
For this example, we
have a linear
relationship, it is
positive, and fairly
strong
5
6 X


For descriptive case,
it doesn’t matter
which variable goes
where
 Correlational
analysis
For predictive cases,
put the response
variable on the Y axis
 Regression
analysis
Y
6
Response (outcome) variable
5
4
3
2
1
1
2
3
4
5
6 X
Explanatory (predictor) variable
Scatterplot

Looking for a co-occurrence relationship
between two (or more) variables

We call this relationship a correlation.
 3 properties: form, direction, strength
Y6
For this example, we
have a linear
relationship, it is
positive, and fairly
strong

5
4
3
2
1
1
2
3
4
5
6
Correlational designs
X
Linear
Y
Non-linear
Y
X
Y
Y
X
Form
X
X
Negative
Positive
Y
• X & Y vary in the same
direction
Direction
Y
X
• X & Y vary in opposite
directions
X
r = -1.0
“perfect negative corr.”
-1.0
r = 0.0
“no relationship”
r = 1.0
“perfect positive corr.”
0.0
The farther from zero, the stronger the relationship
Strength
+1.0
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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
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 students
Correlational designs
Zagorsky (2011)

Disadvantages:

Don’tt make casual claims
• Third variable problem
• Temporal precedence
• Coincidence (random co-occurence)

Correlational results are often misinterpreted
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 worse
grades than [each and every child] who sits in
the front.”
Better: “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.”
Misunderstood Correlational designs
Example from Owen Emlen (2006)

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
What are they?



Almost “true” experiments, but with an inherent
confounding variable
General types
1) An event occurs that the experimenter doesn’t
manipulate
•
Something not under the experimenter’s control
•
(e.g., flashbulb memories for traumatic events)
2) Interested in subject variables
–
high vs. low IQ, males vs. females
3) Time is used as a variable
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
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?
Quasi-experiments

Nonequivalent control group designs

with pretest and posttest (most common)
(think back to the second control lecture)
Independent
Non-Random Dependent
Variable
Variable
Assignment
Measure
Experimental
group
Dependent
Variable
Measure
participants
Measure
Control
group
Measure
– But remember that the results may be compromised
because of the nonequivalent control group (review threats
to internal validity)
Quasi-experiments

Interrupted & Non-interrupted time series
designs

Observe a single group multiple times prior to and after a
treatment
Obs Obs Obs Obs Treatment Obs Obs Obs Obs
• Look for an instantaneous, permanent change
• Interrupted – when treatment was not introduced by
researcher, for example some historical event

Variations of basic time series design
• Addition of a nonequivalent no-treatment control group time series
OOOTOOO
& OOO_OOO
• Interrupted time series with removed treatment
• If treatment effect is reversible
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

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

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 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

Cross-sectional design

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
Developmental designs

Cross-sectional design

Disavantages:
•
Individuals are not followed over time
•
Cohort (or generation) effect: individuals of different
ages may be inherently different due to factors in the
environment
•
•
•
Are 5 year old different from 15 year olds just because
of age, or can factors present in their environment
contribute to the differences?
•
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

Longitudinal design

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
time
Age 11
Age 15
Age 20
Developmental 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 Designs

Longitudinal design

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

Longitudinal design

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

Cohort-sequential design

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
Developmental designs

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

Cohort-sequential design

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
Developmental 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?
Small N designs

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

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 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

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
Small N designs