1Class 5Causality in Social ScienceClass Outline• Nomothetic Causality• Probabilistic Causality• Necessary and Sufficient Causes• Causal Diagrams• Spurious Causation• Causal MechanismsNomothetic Explanation• Description• Explanation– Idiographic - Seeks to fully understand the causes of what happened in a single instance.– Nomothetic - Seeks to explain a class of situations or events rather than a single one.2Causality in Probability• Classical notion of causality (in natural sciences) is deterministic. – If X happens, then Y happens.• Causality in social science is probabilistic.– If X happens, then Y will happen with probability p.– Applies to aggregated patterns of behavior, not to individual cases.– Exceptions do not disprove a causal relationship.– It is expected that population vary in response to the same stimuli.– As the number of cases increases, causality holds true on average.• Example: smoking and lung cancer. Implications of Probabilistic Causality• Importance of multiple observations (sample size N)– The law of large numbers: as sample size increases, the sample mean approaches the population mean.• Importance of replications– Replications: Repetitions of a study using the same research methods to answer the same research question– Meta-analysis: Using statistical techniques to synthesize past study results.Example of Meta-AnalysisSource: Hedges, Larry and Amy Nowell. 1995. “Sex Differences in Mental Test Scores, Variability, and Numbers of High-Scoring Individuals.” Science. Vol. 269(7), pp. 41-5.3Variability in Response to the Same Stimuli• Variability is the essence of social science.– Population variability (i.e., variability across different individuals) – Contextual variability (i.e., variability across time and space)• The same stimuli often lead to different responses.Correlation or Regression as a Statistical Tool to Discover Causality• Correlation describes the linear probabilistic relationship between two variables. • Regression describes the linear or non-linear probabilistic relationship between two or more variables.• Correlation ≠ Causation. Examples of Correlation30 35 40 45 50Turn Circle (ft.)2,000 3,000 4,000 5,000Weight (lbs.)correlation = 0.857410 20 30 40Mileage (m pg)2,000 3,000 4,0 00 5,000Weight (lbs.)correlation = -0.807230 35 40 45 50Turn Circle (ft.)1.0 2.0 3.0 4. 0 5.0Headroom (in.)correlation = 0.4245Strong, positive Strong, negativeModerate, positive4Criteria for Nomothetic Causality• A statistical correlation between the two variables.• The cause takes place before the effect.• There is no third variable that can explain away the observed correlation as spurious.Necessary and Sufficient Causes• Necessary cause represents a condition that must be present for the effect to follow.• Sufficient cause represents a condition that if present, guarantees the effect in question.• Causes that are necessary and sufficient are the most satisfying outcome in research.Causal Diagram: Two VariablesFamily background Educational attainmentIndependent variable Dependent variableExamples:Water pollution Cholera5Causal Diagram: Intervening VariablesAgeJob satisfactionJob autonomyIndependent variable Dependent variableIntervening variableExample:Causal Diagram: ConfoundingEducationEarning powerAbilityIndependent variable Dependent variableConfounding variableExample:Spurious CausationShoe sizeMath skillsAgewhen in fact the correlation is induced only by a common causeShoe sizeMath skillsWe observe correlation:Shoe size Math skillsand we claim causation:Note: Spurious causation is a special case of confounding.6Path Analysis ExampleAge Job satisfactionAutonomyIncomeBased on Bryman, A. and D. Cramer (1990). Quantitative data analysis for social scientists, pp. 246-251. -0.080.280.580.570.470.22Causal MechanismsSexSuccess in mathematical and scientific occupations?New York Times articleThe Chronicle articleWhy do fewer women make it to the top of mathematics and science?Some Possible MechanismsSex(F=1, M=0)Success in math and scienceBrain sizeProp. of grey matterCareer choiceWorking hoursEnthusiasm-??--+-+-+Direct effect ?7Reasons Have ReasonsSex(F=1, M=0)Success in math and scienceParental/teacher influenceCareer
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