Correlation AND EXPERIMENTAL DESIGN Chapter 6

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? The association is strong. The association is consistent. Higher doses are associated with stronger responses. Alleged cause precedes the effect. The alleged cause is plausible. THERE IS NO SUBSTITUTE FOR AN EXPERIMENT!!! We can evaluate the association using the following criteria:

64% of American’s answered “Yes” . 38% replied “No”. In a Gallup poll, surveyors asked, “Do you believe correlation implies causation?’” 64% of American’s answered “Yes” . 38% replied “No”. The other 8% were undecided.

Causal Explanation 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.

Five Criteria for Identifying a Causal Effect 3 required 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

Criteria for Identifying a Causal Effect 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

Correlation vs Causation Topic 5, Lecture 3 Correlation vs Causation 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)

Correlation vs. Causation Example ‘‘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

Correlation vs. Causation Possibility 1 Education improves skills & skilled workers get better paying jobs Education causes wages to  Possibility 2 Individuals are born with quality A, which is relevant for success in education and on the job Quality A (NOT education) causes wages to 

Kids’ TV Habits Tied to Lower IQ Scores IQ scores and TV time r = -.54

Eating Pizza ‘Cuts Cancer Risk’ Pizza consumption and cancer rate

Reading Fights Cavities Number of cavities in elementary school children & their vocabulary size r = -.67

Stop Global Warming: Become a Pirate Average global temperature and number of pirates r = -.93

4. Implying causation where only correlation exists

There are two relationships which can be mistaken for causation: 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: Common response Confounding

Common response Confounding 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.

1. Common Response: Both X and Y respond to changes in some unobserved variable, Z.

2. Confounding The effect of X on Y is indistinguishable from the effects of other explanatory variables on Y. Example of confounding: The “placebo effect”

When can we imply causation? When controlled experiments are performed. Unless Data Have Been Gathered By Experimental Means and Confounding Variables Have Been Eliminated, Correlation Never Implies Causation

Strongest for demonstrating causality EXPERIMENTS 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=YLXFmQEFmn0

Most powerful design for testing causal hypotheses Experiments Most powerful design for testing causal hypotheses Experiments establish: Association Time order Non-spuriousness

Experimental design

True Experiments Two comparison groups to establish association Experimental Group: Treatment or experimental manipulation Control group: No treatment

Time order 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

Non-spuriousness Random assignment (randomization): Of subjects into experimental and control groups Establishes non-spuriousness Not random sampling Randomization has no effect on generalizability

Matching Can only be done on a few characteristics 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

Quasi-Experimental Designs Establish time order & association May be better at establishing context Cannot establish non-spuriousness Comparison groups not randomly assigned

Internal validity in experiments 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?”

Generalization: External validity Whether results from small sample group, in a laboratory, can be extended to make predictions about entire population

Problems with experiments Threats to validity in experiments True experiments have high internal but low external validity Quasi-experiments have higher external but lower internal validity

Non-comparable groups 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

Endogenous change 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

Things happen outside experiment may change subjects’ scores The History effect Things happen outside experiment may change subjects’ scores

Control and experimental groups affect one another Contamination Control and experimental groups affect one another

Demoralization: Contamination The control group may feel left out and perform worse than expected

Contamination Compensatory Rivalry (The John Henry Effect): When groups know being compared May increase efforts to be more competitive

Treatment Misidentification 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

Treatment Misidentification 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

generalizability of experiments More artificial experimental arrangements Greater problem of sample generalizability Subjects are not randomly drawn from population

generalizability of experiments Field experiments: Conduct experiments in natural settings Increases ability to generalize. Random assignment is critical