Questions and Answers
What is the key difference between correlations and experiments?
Correlations do not reliably point to a cause-and-effect relationship, whereas experiments do.
What does a correlational study not tell us?
Why the co-variables are related.
Why should correlations be treated cautiously?
Outliers considerably distort the coefficient, and small samples are not generalizable.
When should greatest caution be taken with correlations?
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What are the choices for interpretation of correlations?
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What is a cause and effect explanation?
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What is a third variable explanation?
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What is a chance relationship?
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Study Notes
Key Differences Between Correlations and Experiments
- Correlations do not establish cause-and-effect; experiments can determine causal relationships.
Limitations of Correlational Studies
- Correlational studies fail to explain the reason behind the relationship between co-variables.
Cautions Regarding Correlations
- Outliers can skew correlation coefficients, making results less reliable.
- Smaller sample sizes may not accurately reflect broader populations.
When to Exercise Caution with Correlations
- Extreme caution is advised when the sample size is fewer than 100 participants.
Interpretation Options for Correlations
- Possible interpretations include:
- A direct cause-and-effect relationship.
- The influence of a third variable affecting both co-variables.
- A mere chance occurrence due to randomness.
Cause and Effect Explanation
- Researchers may assume a direct causal link between two variables, yet correlation alone does not validate this assumption.
Third Variable Explanation
- A third variable may influence the relationship between two measured variables, such as hard work leading to both better grades and higher income.
Chance Relationship
- Some correlations may arise purely by chance, particularly in larger sample sizes, leading to statistical anomalies.
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Description
Explore the key differences between correlational studies and experiments in psychology. This quiz will guide you through the limitations, cautions, and interpretation options of correlations, helping you understand the importance of establishing causal relationships in research.