Transcript Design Approaches to Causal Inference

Design Approaches to Causal
Inference
Statistical mediation analysis answers the following question, “How
does a researcher use measures of the hypothetical intervening
process to increase the amount of information from a research
study?”
Another question is, “What is the best next study or studies to
conduct after a statistical mediation analysis to further test
mediation theory.”
Five general approaches: (1) double randomization, (2) blockage, (3)
enhancement, (4) purification, (5) pattern matching for multiple
variables, subgroups, settings, time, and alternative manipulations
(Mark, 1986).
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(1) Double Randomization
If the problem with the b path is that M is not randomly
assigned, then how about randomizing both X in the X
to M relation and randomizing M in the M to Y
relation.
Say X was randomized and there was a significant
effect of X on M in Study 1. In Study 2, an experiment
was set up so that M was randomized to levels defined
by how X changed M in Study 1. If there was a
significant relation of M to Y in Study 2, then there is
more evidence for mediation.
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Wood et al. (1974) Overview
Study of self-fulfilling prophecy in interviews cited in
Spencer et al., (2005).
Race (X) predicts quality of interview (M) and quality
of interview predicts performance (Y).
Confederate—Person assisting with the experiment.
The confederates are used to manipulate factors.
Confederate applicants were used in Study 1 for the
X to M relation and confederate interviewers were
used in Study 2 for the M to Y relation.
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Wood et al., (1974)
Study 1. White participants interviewed either Black or
White confederate applicants (X). The dependent
variable M, was interview quality and participants
with Black confederate applicants gave poorer
quality interviews (M).
Study 2. Confederates gave either an interview (M)
like White applicants were interviewed in Study 1 or
like Black applicants in Study 1. This manipulation
had a significant effect on applicant performance (Y).
So randomization was used for the X to M relation and
the M to Y relation.
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Prevention Example
(MacKinnon et al., 2002)
Norms increase exercise which decreases depression.
Study 1, X to M: Similar to existing prevention
studies, participants either receive a social norm
manipulation to increase exercise or not (X) and
exercise is measured (M).
Study 2, M to Y: Participants are randomly assigned to
conduct an amount of exercise (M) obtained in the
program group or the control from Study 1 and
depression is measured (Y).
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Double Randomization
Problems
Most problems center around the randomization of the
mediator so that it corresponds to the change in the
mediator in the X to M study.
Study 2 is a mediation model with a manipulation (X)
that should change M in the same way as X changed
M in Study 1. So Study 2 data is analyzed with
statistical mediation analysis with the same problems
of interpretation.
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(2) Blockage Designs
The goal of blockage designs is to test a mediation
relation with a manipulation that blocks the mediator
from operating.
For example, lets say that an exercise program appears
to reduce depression by increasing endorphin levels-the hypothesized mediator. A blockage manipulation
would administer a drug to prevent endorphin
production so that persons receiving the exercise
program would no longer experience reduced
depression if the endorphin level is the mediator.
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(3) Enhancement Designs
The goal of enhancement designs is to deliver
interventions that enhance the effects of a
hypothesized mediator.
For example, lets say that an addiction treatment
program reduces remission by improving social
support. An enhancement design would increase social
support even more to demonstrate a larger effect on
remission. Social support may be increased by more
exposure to a therapist, additional contact with friends
and family etc.
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(4) Purification Designs
The goal of purification designs is to reduce a
manipulation to its critical ingredients.
For example, in drug prevention research, it appears
that changes in norms, beliefs about positive
consequences of drugs, and intentions to avoid drugs
appear to be important mediators of drug prevention
programs. A purification design would retain only
those program components that address these
mediators to test whether the purer program changes
drug use.
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(5) Pattern Matching
The goal of pattern matching is to specify patterns of
results based on mediation theory. Different types of
studies and information are used to assess whether the
pattern of results is consistent with mediation theory.
Multiple variables: a mediation relation is observed for
one variable but not another. For example, change in
beliefs about positive consequences of alcohol use is a
mediator for alcohol use but not for tobacco use.
Changes in beliefs about positive consequences is a
statistical mediator but changes in beliefs about
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negative consequences is not.
More Pattern Matching
Examples
Moderators: For example, prevention program effects
are most effective for persons low on the mediator at
baseline.
Setting: An intervention to change norms to change
behavior should be more successful in a setting where
more norm change may occur.
Different Manipulations: A different manipulation that
should change the same theoretical mediator should
lead to the same results.
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Goals of CAPS Presentation
• Describe many mediating variable examples.
• Describe reasons for mediation analysis--it can help
improve prevention programs and reduce their cost.
It is also useful for testing theories.
• Describe the latest methods to assess mediation.
• Describe limitations of mediation analysis.
• Describe experimental as well as non-experimental
designs to investigate mediating variables.
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Summary of Workshop
Described methods for multilevel, categorical,
longitudinal, and multiple mediator data. Moderators
and potential designs to assess mediation were
discussed.
New methods have more power and are more accurate
than older methods, e.g., distribution of the product
methods.
Mediation can be investigated in the analysis of any
design that includes mediating variable measures.
Mediation analysis provides a way to extract more
information from a research study, e.g., action theory
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and conceptual theory. Can improve programs.