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Lecture 2: Research Design and the Logic of Causation

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Katie Stuart- Lahman

SOCB05 – Lecture 2 – May 16 2013 Research Design and the Logic of Causation The Logic of Causation  Correlation does not equal causation  Not really interested in finding out if a specific thing will happen, but the factors that will make it more likely for it to happen. Causation  Scientific explanations rest on the idea that events and conditions have causes. Leaves room for outliers, or things that go against the general pattern.  Probabilistic in nature. o X is not always followed by Y, but the presence of X makes Y more likely. o A change in the independent variable, will lead to a change in the dependent variable. Variables  Independent o The cause o Impacts other variables o The variable that is depended on o X-value  Dependent o The effect/ explained variable o Being impacted upon o Depends on another variable o Y-value Variables vs Attributes  Attributes: Describe the intensity, magnitude, and nature of the variable. o The values that a variable takes. o How it is measures  A variable must have at least two attributes  If attributes do not vary, we cannot talk about a variable but a constant  Examples: o “People with lower levels of education are more likely to be more prejudiced”  Warning: Do not confuse attributes with variables  Low level of education is not a variable  More prejudice is not a variable o “In the U.S., Republicans are more likely to be in favour of the death penalty than Democrats”  I.V. : Political Affiliation  D.V. : Opinion towards death penalty.  NOTE: Variables are not inherently dependent or independent Conditions for Causation  Idiographic: Complete, in-depth understanding of a single case.  Nomothetic: An attempt to find independent variables that account for the variations in a given class/category of phenomenon. o More common in sociological research because sociologists aren’t really looking for individual cases. What do we need for a relationship to be causal?  Association/Correlation o When 2 variables are observed to be related. o It is never perfect. Outliers always exist. o Has two forms  Change in one variable is associated with change in a second variable  When an attribute of one variable is associated with an attribute of another variable.  Temporal Order o Cause has to be before the effect  Example: Buying a fancy car does not cause one to make enough money to afford one. o Can be problematic because sometimes we cannot tell what happens first.  Non-Spuriousness o Observed correlation between two variables that cannot be explained by a third variable. o Spuriousness:  The incorrect inference of a causal relationship between two variables where the relationship is in reality only accidental  Despite two variables being correlated and one preceding the other. It may be possible that neither is a cause of the other. Examples:  Length of hair and grades in SOCB05 o Association:  Length of hair is correlated with academic success in the class o Temporal Order  Starting the class with long hair precedes getting a good grade in the class. o BUT there is a spurious relationship because there is a third variable, which would be gender. Which may explain why people with longer hair do better. Intervening Relationship  Different from spuriousness  The original relation between the two variables is explained by a third variable that acts in between the two.  Example: Drinking doesn’t lead to pregnancy. But drinking does lead to higher chances of unprotected sex. Which leads to pregnancy. o Drinking doesn’t exactly cause pregnancy, but it can help it.. Social Science vs. Lay Science  Complete Causation? o SS: More likely to say that something is “one of the causes” rather than saying that something is the “only cause”.  Exceptional Cases? o SS: Likes exceptional cases, but it doesn’t change the pattern.  Majority of Cases? o SS: Talk about how people are more likely to do something. Units of Analysis  What
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