Transcript Correlational and Differential Research Graziano and Raulin Research Methods: Chapter 7 This multimedia product and its contents are protected under copyright law.

Correlational and
Differential Research
Graziano and Raulin
Research Methods: Chapter 7
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Copyright © Allyn & Bacon (2007)
Correlational Research


Quantifies the strength of the relationship
between two or more variables
Value of correlational research
– Correlations can be used for prediction
– Evidence consistent or inconsistent with a theory


Cannot prove a theory, but could negate a theory
Note: Correlations CANNOT establish
causation
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Correlation and Causation

If A and B are
correlated, then …
– A could cause B
– B could cause A
– Another variable
could cause both A
and B

Must be cautious in
drawing conclusions
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Differential Research
Methods


Compare two or more preexisting groups
Similar to both correlational and
experimental research
– Same form as experimental research
– Conceptually similar to correlational research
(variables measured, but not manipulated)

Cross-sectional design in developmental
research is differential research
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Cross-Sectional and
Longitudinal Research

Cross-sectional designs are faster
– Can test many age groups simultaneously

But cohort effects can be a problem
– Defined as “shared life experiences of people of
a given age that lead them to behave similarly to
others their age and different from people of
other ages”

Longitudinal designs are essentially timeseries designs
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Artifacts and
Confounding Variables

Confounding occurs when two variables vary
together
– Need to have them vary independently, usually
by holding all but one variable constant
– Failing to provide this control could result in
artifactual findings
– Procedures standardized for this reason

Comparing groups is reasonable ONLY IF
we standardized the measurement
procedures
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Correlational versus
Differential

Both involve the measurement, but not
manipulation, of variables
– Therefore, neither is able to establish causation

Differential is higher constraint because
– The researcher can select the comparison
group(s) to control at least some of the potential
confounding variables, thus providing stronger
evidence for a theory
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When to Use Each Method

Correlational Method
– When we are interested in knowing the strength
of a relationship for predictive purposes
– Often included to help interpret the primary
findings of a study

Differential Research
– When the manipulation of an independent
variable is impractical, impossible, or unethical
– Then we rely on comparing preexisting groups
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Doing Correlational
Research

Steps in conducting correlational research
–
–
–
–
–

Developing the problem statement
Measuring the Variables
Obtaining the Sample
Analyzing the Data
Interpreting the Results
Correlational research is often embedded in
larger studies
Copyright © Allyn & Bacon (2007)
Developing a Problem
Statement


“What is the relationship between variable X
and variable Y?”
Often want to correlate available
demographic variables with the dependent
measures or intercorrelate the dependent
measures in higher-constraint research
– Useful in detecting confounding variables
– Provides hypotheses for later research
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Measuring the Variables


Need to use reliable and valid measures
Need to control
– Experimenter expectancy

Researchers tending to see what they expect to see
– Experimenter reactivity

Researchers unconsciously influencing participants
– Measurement reactivity

Participants responding differently because they know they
are being observed
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Controlling these effects

Experimenter expectancy
– Use objective measures whenever possible

Experimenter reactivity
– Minimize experimenter contact

Measurement reactivity
– Use filler items to distract participants
– Use unobtrusive measures when possible
– Separate the measurements in time
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Sampling Considerations

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Want the sample to be representative
Is the observed relationship the same in
each subpopulation?
– If we suspect such differences, we should compute the
correlation in each subpopulation
– Moderator Variable: a variable that seems to modify
the relationship between other variables

e.g., sex: males and females showing different patterns of
relationship between variables
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Analyzing the Data

Correlations range from -1.00 to +1.00
– Size indicates strength of the relationship
– Sign indicates direction of the relationship

Many types of correlations
– Pearson product-moment correlation
– Spearman rank-order correlation
– Advanced techniques (multiple correlation,
canonical correlation, partial correlation, path
analysis)
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Interpreting the Data

Note size and sign of correlation
– Indicates strength and direction of relationship

Is the correlation significantly different from
zero (i.e., evidence for a relationship)?
– Is the p value < alpha?

Coefficient of Determination
– r2 indicates the proportion of variance accounted
for
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Doing Differential
Research






Developing the problem statement
Measuring the variables
Selecting appropriate control groups
Obtaining the sample
Analyzing the data
Interpreting the results
Copyright © Allyn & Bacon (2007)
Developing a Problem
Statement


“Does Group A differ from Group B?”
Developing good problem statements
– Select theoretically interesting groups to
compare
– Compare them on theoretically interesting
variables
– Best to compare groups that differ on only a
single variable if possible
– Several comparisons are best
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Measuring the Variables


Dependent variable is usually continuous,
but could be categorical
Independent variable is categorical or is a
continuous variable converted to categories
– Unlike experimental research, the independent
variable is measured, rather than manipulated

Need operational definitions for
– Dependent Variable
– Independent variables
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Selecting Control Groups

Select control groups to avoid confounding
– A variable can confound results only if
a) it affects the scores on the dependent variable
b) the groups differ on this variable

Ideal control group is identical to
experimental group on all variables except
the variable that defines the groups
– Rarely possible, so multiple comparisons groups
are typical
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Sampling of Participants


Like all research, we want representative
sampling to permit generalization
Many factors can bias sampling
– Where we have access to participants
– How we go about identifying participants
– Even factors like time of day that we sample

Participants who drop out of the study can
limit generalizability
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Analyzing the Data


Same procedures as those used to
analyze experimental research
Type of analysis depends on the
number of groups and the level of
measurement
– Score data: t-test or ANOVA
– Ordinal data: Mann-Whitney U-test
– Nominal data: Chi square
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Interpreting the Results


Reject null hypothesis (of no group
difference) if the p < alpha
Difficult to draw a strong conclusion from
differential research
– Sampling considerations
– Unlikely that all potential confounding variables
will have been adequately controlled
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Blanchard et al. (2001)
18
16
So cial A n h e d o n ia Sco res
14
12
10
Bas eline
Follow - up
8
6
4
2
0
Sc hiz ophr enic s
Depr es s ed
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Contr ols
Limitations of these
Methods

Problems in determining causation
– A correlation does not imply causality

If A and B are correlated, then
– A could cause B
– B could cause A
– Some other variable could cause both

Confounding variables
– Without experimental control, it is virtually
impossible to avoid confounding variables
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Summary
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Both correlational and differential
research involve measuring the
relationship between variables
Drawing causal inferences is risky
Selecting appropriate control groups in
the differential research design can
control some, but typically not all,
potential confounding variables
Copyright © Allyn & Bacon (2007)