Pearson Correlation Effect Size Calculator
Names List (100 Names):
Here’s a comprehensive table summarizing all you need to know about Pearson Correlation Effect Size:
Pearson Correlation Effect Size
| Aspect | Description |
|---|---|
| Definition | A measure of the strength and direction of the linear relationship between two continuous variables2 |
| Range | -1 to +13 |
| Interpretation | -1: Perfect negative correlation 0: No linear correlation +1: Perfect positive correlation3 |
| Effect Size Categories | Small: 0.10 Medium: 0.30 Large: 0.503 |
| Squared (r²) | Small: 0.01 Medium: 0.09 Large: 0.254 |
| Interpretation of r² | Percentage of variance explained in the relationship1 |
| Calculation of r² | r² × 100 = % variance explained1 |
| Use in Research | Determines strength of relationship between variables Used in power analysis for sample size determination1 |
| Relationship to Other Measures | Can be converted to Cohen’s d for standardized mean difference5 |
| Advantages | Independent of sample size Allows for comparison across studies5 |
| Limitations | Only measures linear relationships Sensitive to outliers2 |
Additional Considerations
- Sample Size: Larger effect sizes require smaller sample sizes to detect significant relationships1.
- Clinical Importance: The percentage of variance explained (r²) helps in understanding the practical significance of a correlation1.
- Interpretation Guidelines: While the categories provide a general framework, interpretation should consider the specific context of the research2.
- Use in Meta-analysis: Pearson’s r is commonly used in meta-analyses to compare and combine results from multiple studies2.
- Reporting: When reporting Pearson’s r, it’s beneficial to include both the correlation coefficient and its squared value (r²) for a comprehensive understanding of the effect size1.
This table provides a concise overview of Pearson Correlation Effect Size, including its definition, interpretation, categories, and practical applications in research. It’s important to note that while these guidelines are widely used, the interpretation of effect sizes should always consider the specific context and field of study.