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Chapter 6
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Establishing causation
It appears that lung cancer is associated with smoking.
How do we know that both of these variables are not being affected by an unobserved third (lurking) variable?
What if there is a genetic predisposition that causes people to both get lung cancer and become addicted to smoking, but the smoking itself doesn’t CAUSE lung cancer?
1) The association is strong.
2) The association is consistent.
3) Higher doses are associated with stronger responses.
4) Alleged cause precedes the effect.
5) The alleged cause is plausible.
THERE IS NO SUBSTITUTE FOR AN EXPERIMENT!!!
We can evaluate the association using the following criteria:
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64% of American’s answered “Yes” . 38% replied “No”. The other 8% were undecided.
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Cause: An explanation for some characteristic, attitude, or behavior of groups, individuals, or other entities
Causal effect: The finding that change in one variable leads to change in another variable, other things being equal.
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3 required 1.Association: Empirical (observed)
correlation between independent and dependent variables (must vary together)
2. Time Order: Independent variable
comes before dependent variable
3. Nonspuriousness: Relationship between independent and dependent variable not due to third variable
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These two strengthen the causal argument
4. Mechanism: Process that creates a connection between variation in an independent variable and variation in dependent variable
5. Context: Scientific explanation that includes a sequence of events that lead to particular outcome for a specific individual
• Can not be used to explain general ideas, places, events, or populations
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Correlation tells us two variables are related
Types of relationship reflected in correlation:
X causes Y or Y causes X (causal relationship)
X and Y are caused by a third variable Z (spurious relationship)
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‘‘The correlation between workers’ education levels and wages is strongly positive”
Does this mean education “causes” higher wages?We don’t know for sure !
Correlation tells us two variables are related BUT does not tell us why
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Possibility 1 Education improves skills & skilled workers get better paying jobsEducation causes wages to
Possibility 2Individuals are born with quality A, which is relevant for success in education and on the jobQuality A (NOT education) causes wages to
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Kids’ TV Habits Tied to Lower IQ Scores
IQ scores and TV timer = -.54
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Eating Pizza ‘Cuts Cancer Risk’
Pizza consumption and cancer rate
r = .-59
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Reading Fights Cavities
Number of cavities in elementary school children & their
vocabulary sizer = -.67
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Stop Global Warming: Become a Pirate
Average global temperature and number of pirates
r = -.93
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A strong relationship between two variables does not always mean that changes in one variable causes changes in the other.
The relationship between two variables is often influenced by other variables which are lurking in the background.
There are two relationships which can be mistaken for causation:1. Common response2. Confounding
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Common response• Possibility that a change in a lurking
variable is causing changes in both explanatory variable and response variable
Confounding• Possibility that either the change in
explanatory variable is causing changes in the response variable
OR• That change in a lurking variable is causing
changes in the response variable.
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Both X and Y respond to changes in some unobserved variable, Z.
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The effect of X on Y is indistinguishable from the effects of other explanatory variables on Y.
Example of confounding: The “placebo effect”
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When controlled experiments are performed.
When can we imply When can we imply causation?causation?
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Strongest for demonstrating causality
Asch Experiment https://www.youtube.com/watch?v=F17JGDZDVUs
Quasi-experimental designs Looks like experimental design but lacks -- random assignment
Attraction and Scary Bridge https://www.youtube.com/watch?v=YLXFmQEF
mn0
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Most powerful design for testing causal hypotheses
Experiments establish:AssociationTime orderNon-spuriousness
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Two comparison groups to establish associationExperimental Group:
Treatment or experimental manipulation
Control group: No treatment
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Variation must be collected before assessment to establish time order
Post-test: Measurement of the DV in both groups after the experimental group has received treatment
Pre-test: Measurement of the DV prior to experimental intervention True experiment doesn’t need a pre-test Random assignment assumes groups will
initially be similar
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Random assignment (randomization):Of subjects into experimental and control groups
Establishes non-spuriousnessNot random samplingRandomization has no effect on generalizability
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Assignment of subject pairs into experimental and control groupsBased on similarity (e.g., gender, age)
Individuals (in pairs) randomly assigned to each group
Can only be done on a few characteristicsMay not distribute characteristics between the two groups
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Establish time order & association
May be better at establishing context
Cannot establish non-spuriousness
Comparison groups not randomly assigned
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Confidence in cause and effect relationship
Key question in any experiment is:
“Could there be an alternative cause, or causes, that explains the observations and results?”
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Generalization: Whether results from small sample group, in a laboratory, can be extended to make predictions about entire population
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Threats to validity in experiments
True experiments have high internal but low external validity
Quasi-experiments have higher external but lower internal validity
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Experimental and Control groups are not comparableSelection bias: subjects in experimental
and control groups are initially different
Mortality/Differential attrition: groups become different because subjects are more likely to drop out of one of the groups for some reason
Instrument decay: Measurement instrument wears out or researchers get tired or bored, producing different results for cases later in the research than earlier
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Natural developments in subjects, independent treatment, account for some or all of change between pre- and post-test scores
Generally, eliminated by use of control group
Changes same for both groups.
Testing: Pre-test can influence post-test scores
Maturation: Changes may be caused by aging of subjects
Regression to the mean: When subjects are selected based on extreme scores
In future testing: Regress back to average
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Things happen outside experiment may change subjects’ scores
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Control and experimental groups affect one another
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Demoralization:
The control group may feel left out and perform worse than expected
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Compensatory Rivalry (The John Henry Effect):
When groups know being compared
May increase efforts to be more competitive
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Expectancies of Experimental Staff:Staff actions and attitudes change the behavior of subjects (i.e., a self-fulfilling prophecy)
Resolved by double-blind designs Neither the subject nor the staff
knows who’s getting the treatment and who’s not
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Placebo Effect: Subjects change because of expectations of change, not because of treatment itself
Hawthorne Effect: Participation in study may change behavior simply because subjects feel special for being in the study
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More artificial experimental arrangementsGreater problem of sample generalizability
Subjects are not randomly drawn from population
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Field experiments: Conduct experiments in natural settings Increases ability to generalize.
Random assignment is critical