Causal inference A cause must always be relative to another cause Wrong: –Eating an avocado before taking a test causes better performance on the test. Right: –Eating an avocado before taking a test causes better performance on the test compared to not eating an avocado before taking the test. –Eating an avocado before taking a test causes better performance on the test compared to eating a banana before taking the test.

Randomized experiment Experimental condition (avocado) Control condition (no avocado) = on all variables other than the avocado Random assignment of individuals into condition

Quasi-experiment Ms. Jones’ class (avocado) Mr. Smith’s class (no avocado)  on all variables other than the avocado Random assignment of pre-existing groups into condition

Observational study Students who chose an avocado for breakfast Students who chose an anchovy and mushroom omelet for breakfast  on all variables other than the avocado Self-selection into conditions: Third variable problem!

Continuum of causal inference Least confidence Greatest confidence Case study Observational study Quasi- experiment Randomized experiment Ideal experiment

Which are valid causal statements (according to Rubin)? The moon causes the tides (compared to no moon) Gender causes differences in spatial abilities (being male compared to being female) Gender causes differences in the severity of punishment for misbehaving on the playground (being male compared to being female) Eating apples causes a decrease in requests for medical attention (compared to not eating apples)

Independent variables (IVs): manipulated variables Dependent variables (DVs): measured variables

Internal validity: The confidence with which we can say that the manipulated change in the IV caused the observed change in the DV

Confounding variable: Something that changes systematically with levels of the IV

Are these confounds? It’s raining all day while the man is jaywalking The man jaywalks in a suit at noon and the work clothes at 5pm The suit has a bright red tie

Control variables: held constant for all runs

Random variables: allowed to vary between runs

External validity: How well a causal relationship can be generalized across people, settings, and times

External validity Do control variables increase or decrease external validity? Do random variables increase or decrease external validity?

Threats to internal validity: Confounding variables History Maturation Selection Mortality Testing Statistical regression

Classical Test Theory Score on a test = True score + Error

Classical Test Theory Lowest possible score Highest possible score

Internal vs. External Validity Does the IV cause the change in the DV? Can we generalize to other people, settings, and times? Confounds threaten this: Too many control variables threaten this: