Correlational Designs
• Researchers often want to go beyond
simply describing their observations.
– Correlation allows for prediction
• Good when impractical or unethical to do
an experiment
– The effect of head injury on behavior.
• Relationships between pairs of scores from each
subject are known as simple correlations.
– The Pearson product-moment correlation coefficient
(r)
• The Pearson r can result in three situations
– positive correlation
– negative correlation
– no correlation
• The values of a correlation coefficient can
vary from –1 to +1.
– The – or + tells us whether the variables are
negatively or positively correlated
– the numerical value of r tells us the strength of
the association.
• Time spent studying and grades.
– devise operational definitions
• Time spent studying
• grades
• Notice both variables are measured
neither of the variables are manipulated
X axis = GPA
Y axis = Time spent studying (hours/week)
• Time spent drinking and grades
– Operational definitions
– X axis = GPA
– Y axis = Time spent drinking (hours/week)
• The main draw back of correlational studies
– correlation does not imply causation.
• Let’s say there is a correlation between exercise
and anxiety
– Do you think this would be positive or negative?
• Even if we had a perfect correlation of -1
between exercise and anxiety
– Does not mean exercise causes lower anxiety
• Two main issues
– Direction of causality
• people who exercise a lot, blow off steam, and thus have lower
anxiety
• people with low anxiety take more time to exercise
– Third variable problem
• Health
– Healthier people tend to have less anxiety and also tend to exercise a
lot.
– Unhealthy people tend to have greater anxieties and also don’t
exercise much.
• Is drinking red wine in moderation good for your health?
Multiple correlation
• Sometimes we wish to see whether there is a
relationship among a number of measured behaviors.
• Inter correlations among three or more behaviors can be
computed with a statistic known as multiple correlation.
– represented as R.
• R is conceptually similar to r and can be used to add to
information gained from simple correlations.
Causal Modeling
• Causal Modeling
• As we now know, correlational studies cannot
show causality
– Experiments can
– However in some cases it may be unfeasible to run
an experiment.
• Because of this researchers have sought
alternate methods of reducing the ambiguity of
correlational findings.
– We discuss one causal modeling technique
• Cross-Lagged Panel Design
– T.V. watching and size of vocabulary at ages
3 and 8.
• There was an initial correlation that implied that
these two variables were weakly positively
correlated.
– So, does watching T.V. increase your
vocabulary, or does having a better
vocabulary increase T.V. watching?
• Quasi-Experimental Designs
– used when subjects cannot be randomly
assigned to receive different treatments
• We will discuss four types
– Ex post facto designs
– Longitudinal designs
– Cross sectional designs
– Pretest postest designs
Quasi Experimental Designs
• Ex Post Facto Studies = After the fact
– In ex post facto studies the researcher relies
on changes that occurred before the study to
make up the groups.
• Gender
– Males – spatial
– Females - verbal
• Cohen, Glass, and Singer (1973)
– Effect of environmental noise on reading ability, and auditory
discrimination in children.
• large apartment building in New York City.
– built over a highway
• Low floors noisy
• Higher floors quiet
– found that children from the lower floors had poorer reading
skills, and performed worse on auditory discriminations.
– First this is a very nice study.
– But in the interest of showing that ex post facto studies, do have
threats to internal validity
• can you think of any other reasons for these differences?
• ex post-facto design of vehicle safety
• looked at the number of deaths related to different types
of cars.
– safe
• volvo 240, 740
• Plymoth voyager
• Mercedes
– Unsafe
• Corvette
• small trucks
• Ford Escort.
• The headline of the report asks “Which Vehicles are the
Safest?”
– Safety a legitimate conclusion?
Pros and cons of ex-post facto designs
• low in internal validity
– there is always a chance that some other difference
between groups was the cause of the effect
• Better external validity
– allow us to understand complex behaviors that occur
in real life
– realistic data that can be applied in practical ways
• LONGITUDINAL DESIGNS
– The cross-lag panel examined how variables are
related over time
– longitudinal designs are interested in how time effects
data
• In a longitudinal design the same subjects are
studied across time to see if there behavior
changes in a systematic way.
• This is particular important for psychologists
studying human growth and development
• Lewis Terman
• Terman study
– 1,528 California children who were considered to be highly
intelligent.
• IQ’s at least 135.
• studied these children throughout childhood, adolescence and into
adulthood (1925, 1947, 1959).
– provided a rich description of the lives of highly intelligent
individuals.
– It disconfirmed many negative stereotypes of high intelligence.
• well adjusted both socially and emotionally.
• The data have now been archived and have been used
by other researchers.
– study social and health practice factors associated with age of
death.
• There are some problems with
Longitudinal designs.
– very time consuming
– retention of subjects
• Cross-Sectional Designs.
– approximates results from a longitudinal study.
– subjects of different ages are compared at a single point in time.
• Suppose you are interested in examining how the ability
to learn a computer application changes as people grow
older.
• Using the cross sectional design you might study people
that are
– 20, 30, 40, 50, 60, and 70 years old.
– give the participants the same computer learning task, and you
could compare the groups on their performance.
Pros and cons of cross sectional
• The cross sectional design is much more common than
a longitudinal design because it is less expensive and
immediately yields useful results
• There are some disadvantages to the cross-sectional
design however.
– Cohort effects
• a group of individuals born at about the same time, exposed to the
same events in society, and influenced by the same demographic
trends.
– These differences in cohorts can represent different cultural
climates, educational systems, and child rearing practices.
• The single group pretest-posttest design.
• This is an often used technique that measures the level of some
variable before and after some event.
• Governor of Connecticut (1955)
– crack down on speeding
– stiffer penalties
• 1st offense 30 day suspension of drivers license
• 2nd offense = 60 days
• 3rd offense = indefinite suspension
– It was opposed by many but went through
– fatalities decreased to from 324 to284 in 1956,
– The Governor was quoted as saying with the saving of 40 lives we can
say that the program was definitely worth while.
• Do you think that the program actually
decreased deaths?
– What if we looked across time to see the
pattern of ups and downs
– called an Interrupted time -series design