Overview
This lecture explains the difference between correlation and causation, common mistakes people make interpreting correlations, and how psychologists study and represent relationships between variables.
Correlation vs. Causation
- Correlation means two variables are related, but it does not mean one causes the other.
- Media headlines often misinterpret correlational findings as causal relationships.
- The example of pizza and cancer risk illustrates why correlation does not imply causation.
- Diets like the Mediterranean diet, not just pizza, are likely responsible for observed health benefits.
Third Variable Problem and Illusory Correlation
- A third variable may explain the link between two correlated variables (third variable problem).
- Example: Family violence could cause both youth violence and video game playing, not video games causing aggression.
- Illusory correlation is when people perceive a relationship between variables that does not exist, like candy causing hyperactivity.
- Sports superstitions are common examples of illusory correlations.
Conducting Correlational Research
- Psychologists use correlational research to explore how well one variable predicts another.
- Common relationships studied include GPA and attendance, money and happiness, or intelligence and income.
- It's difficult to determine causality from correlational data.
Correlation Coefficient and Interpretation
- The correlation coefficient (Pearson’s r) measures the strength and direction of a relationship, ranging from +1.0 to -1.0.
- An r of 0 means no relationship exists.
- Strong correlations are closer to ±1.0; weak correlations are closer to 0.
- Positive correlation: as one variable increases, so does the other (e.g., treadmill use and calories burned).
- Negative correlation: as one variable increases, the other decreases (e.g., alcohol use and judgment).
Graphing Correlations
- Correlations are represented with scatter plots.
- The slope shows direction; the tightness of data points shows strength.
- Dots close to the line of best fit indicate a stronger correlation; scattered dots indicate weaker correlation.
Key Terms & Definitions
- Correlation — a relationship or association between two variables.
- Causation — when one variable directly causes a change in another.
- Third Variable Problem — another variable explains the observed relationship between two variables.
- Illusory Correlation — believing a relationship exists when it does not.
- Correlation Coefficient (Pearson’s r) — a statistical measure of correlation strength and direction.
- Scatter Plot — a graph representing data points for correlated variables.
Action Items / Next Steps
- Brainstorm and list examples of positive and negative correlations.
- Practice identifying the strength and direction of correlations given correlation coefficients.
- Review definitions and ensure understanding of key terms before the next lesson.