Correlation vs. Causation - Winthropfaculty.winthrop.edu/solomonj/FALL 2013/SOCL 516/V2 USE...
Transcript of Correlation vs. Causation - Winthropfaculty.winthrop.edu/solomonj/FALL 2013/SOCL 516/V2 USE...
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?
For instance, 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:
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.
3 required 1. Association: Empirical (observed) correlation
between the independent and dependent variables (they must vary together)
2. Time Order: Independent variable comes before the dependent variable
3. Nonspuriousness: Relationship between independent and dependent variable must not be due to third variable
These two strengthen the causal argument
4. Mechanism: The process that creates a connection between the variation in an independent variable and the variation in the dependent variable
5. Context: Focus of idiographic causal explanation; a scientific explanation that includes a sequence of events that lead to a particular outcome for a specific individual• Can not be used to explain general ideas, places,
events, or populations
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 1Education 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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Reading Fights Cavities
Number of cavities in elementary school children & their vocabulary size
r = -.67
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
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.
The effect of X on Y is indistinguishable from the effects of other explanatory variables on Y.
Example of confounding:The “placebo effect”
Asch Experimenthttps://www.youtube.com/watch?v=F17JGDZDVUs
Strongest for demonstrating causality
Quasi-experimental designs
Problems of validity associated
Most powerful design for testing causal hypotheses
Experiments establish:AssociationTime orderNon-spuriousness
Two comparison groups to establish associationExperimental Group:
Group of subjects that receives treatment or experimental manipulation
Control group: Comparison group that receives no treatment
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-testRandom assignment assumes groups will initially be similar
Random assignment (randomization):Of subjects into experimental and control groups
Establishes non-spuriousnessNot same as random samplingRandomization has no effect on generalizability
Assignment of subject pairs into experimental and control groups
Based on similarity (e.g., gender, age)
Individuals (in pairs) randomly assigned to each group
Can only be done on a few characteristics
May not distribute characteristics between the two groups
Establish time order & associationMay be better at establishing contextCannot establish non-spuriousness
Comparison groups not randomly assignedIndividuals must be able to choose whether to be in experimental or control groups
Confidence that can be placed in cause and effect relationship in a study
The key question in any experiment is:
“Could there be an alternative cause, or causes, that explain my observations and results?”
Generalization:Whether results from a small sample group, in a laboratory, can be extended to make predictions about the entire population
Threats to validity in experiments
True experiments have high internal but low external validity
Quasi-experiments have higher external but lower internal validity
Experimental and Control groups are not comparable
Selection 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
Natural developments in subjects, independent of experimental treatment, account for some or all of change between pre- and post-test scores Generally, eliminated by use of a control group because changes will be the same for both groups.
Testing: Pre-test can influence post-test scores
Maturation: Changes may be caused by the aging of subjects
Regression to the mean: When subjects are selected based on extreme scores, on future testing they tend to regress back to the average
Compensatory Rivalry (The John Henry Effect):
When groups know they’re being compared, They may increase their efforts to be more competitive
Subjects experience an unintended treatment during the experiment.
To compensate, measures are taken throughout the experiment to assess whether the treatment is being delivered as planned.
Expectancies of Experimental Staff:Staff actions and attitudes change the behavior of subjects (i.e., a self-fulfilling prophecy)
Resolved by double-blind designsNeither the subject nor the staff knows who’s getting the treatment and who’s not
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
The more artificial the experimental arrangements
The greater the problem of sample generalizability
Subjects are not randomly drawn from the population