Title: Non-Experimental designs: Correlational
1Non-Experimental designs Correlational
Quasi-Experiments
- Psych 231 Research Methods in Psychology
2Announcements
- Lab attendance is critical this week because
group projects are being administered - Attendance will be taken.
- Dont forget Quiz 9 (chapters 12 13) due on
Monday Nov. 5
3Non-Experimental designs
- Sometimes you just cant 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
4Correlational 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
- For our example, which variable is explanatory
(predictor) and which is response (outcome)? - It depends on your theory of the causal
relationship between the variables
5Scatterplot
study time
exam score
X Y
6 6
1 2
5 6
3 4
3 2
6Scatterplot
- Response Explanatory variables
- For descriptive case, it doesnt matter which
variable goes where - Correlational analysis
- For predictive cases, put the response variable
on the Y axis - Regression analysis
7Form
8Direction
9Strength
Common statistic computed for correlation
Pearsons r
r -1.0 perfect negative corr.
10Correlational 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
11Correlational designs
- Disadvantages
- Do not make casual claims
- Third variable problem
- Temporal precedence
- Coincidence (random co-occurence)
- r0.52 correlation between the
- number of republicans in US senate
- and number of sunspots
- From Fun with correlations
- Correlational results are often misinterpreted
12Misunderstood 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.
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)
13Non-Experimental designs
- Sometimes you just cant 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
14Quasi-experiments
- What are they?
- Almost true experiments, but with an inherent
confounding variable - General types
- An event occurs that the experimenter doesnt
manipulate - Something not under the experimenters control
- (e.g., flashbulb memories for traumatic events)
- Interested in subject variables
- high vs. low IQ, males vs. females
- Time is used as a variable
15Quasi-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?
16Quasi-experiments
- Nonequivalent control group designs
- with pretest and posttest (most common)
- (think back to the second control lecture)
- But remember that the results may be
compromised because of the nonequivalent control
group (review threats to internal validity)
17Quasi-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
18Non-Experimental designs
- Sometimes you just cant 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
19Developmental 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
20Developmental 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
21Developmental 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
22Developmental designs
- 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
didnt 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
23Developmental 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
24Longitudinal 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
25Developmental 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)
26Developmental 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
27Developmental 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
28Developmental designs
Time of measurement
1975
1985
1995
Cohort A
1970s
Cohort B
1980s
Cohort C
1990s
29Developmental 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
30Small N designs
- What are they?
- Historically, these were the typical kind of
design used until 1920s 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.)
31Small 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
32Small N designs
- Baseline experiments the basic idea is to show
- when the IV occurs, you get the effect
- when the IV doesnt occur, you dont get the
effect (reversibility) - Before introducing treatment (IV), baseline needs
to be stable - Measure level and trend
33Small 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?
34ABA 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.)
35Small N designs
- Advantages
- Focus on individual performance, not fooled by
group averaging effects - Focus is on big effects (small effects typically
cant 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
36Small 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 dont
get reversals - Difficult to determine how generalizable the
effects are
37Small 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