surveys non experimental

Non-Experimental designs:
Surveys & Correlational
Psych 231: Research
Methods in Psychology

Exam 2 results
 Mean = 74.14
 Median = 77
 Max = 94
 Min = 50
 Most common
errors
 Between vs. within designs
 Independent vs. dependent vars
 Scales of measurement
 Confounds vs. extraneous
variables
 Main effects vs. interactions

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
• Developmental designs
• Small-N designs
 This does NOT imply that they are bad designs
 Just remember the advantages and disadvantages of each

Stages of survey research
 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

Importance of sample size
 Confidence intervals
• An estimate of the mean or percentage of the population,
based on the sample data
• “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.
• Which would you be more likely to believe
• “We asked 10 people …”
• “We asked 1000 people …”
 Sampling error - how is the sample different
from the population?
Often focus on this part
But this part is important too

Importance of sample size
 Sampling error - how is the sample different
from the population?
 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)?

10 Stages of survey research
 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)?

Good and poor questions
Good
Poor
Was the FDC negligent by ignoring the
warnings about Vioxx during testing
and approving it for sale?
a) Yes
b) No
c) Unsure
Do you favor eliminating the wasteful
excess in the public school budget?
a) Yes
b) No
c) Unsure
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
Problem: emotionally
charged words

Good
Poor
Should senior citizens be given more
money for recreation centers and
food assistance programs?
a) Yes
b) No
c) Unsure
Should senior citizens be given
more money for recreation centers?
a) Yes
b) No
c) Unsure
Should senior citizens be given
more money for food assistance
programs?
a) Yes
b) No
c) Unsure
Problem: asks two
different questions

Good
Poor
Are you against same sex marriage
and in favor of a constitutional
amendment to ban it?
a) Yes
b) No
c) Unsure
What is your view on same sex
marriage?
a) I think marriage is a matter of
personal choice
b) I’m against it but don’t want a
constitutional amendment
c) I want a constitutional
amendment banning it
Problem: Biased in
more than one direction
Problem: Asks two
questions

Survey 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
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
What is the best thing
about ISU?

 Decide what kind of rating scales
• Rating:
e.g., Likert scale
Survey Questions: Close-ended
PSY 231 is an important course in the major.
1 2 3 4 5
Strongly Agree Neutral Disagree Strongly
Agree 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.

 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

10 Stages of survey research
 Stage 7) Pre-testing the survey instrument
 Fix what doesn’t seem to be working
 Stage 8) Selecting and training interviewers
 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

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
 We call this relationship a correlation.
 3 properties: form, direction, strength

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

 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.

 Example 1: Suppose that you notice that the
more you study for an exam, the better your
score typically is
 Explanatory variables (Predictor variables)
 Response variables (Outcome variables)
 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
 At a descriptive level this suggests that there is a
relationship between study time and test performance.

Scatterplot
Hours
study
X
Exam
perf.
Y
6 6
1 2
5 6
3 4
3 2
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

Scatterplot
Y
X
1
2
3
4
5
6
1 2 3 4 5 6
Response (outcome) variable
Explanatory (predictor) variable
 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

 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)
• 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

 Disadvantages:
 Don’t make casual claims
• Third variable problem
• Temporal precedence
• Coincidence (random co-occurence)
 Correlational results are often misinterpreted

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 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.”
Example from Owen Emlen (2006)

Quasi-experiments
 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)
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)

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
O O O T O O O & O O O _ O O O
• Interrupted time series with removed treatment
• If treatment effect is reversible

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

 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

 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

 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

 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
Age 11
time
Age 20
Age 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

 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)

 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

 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

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

 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

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)
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.)

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

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