The article by C. Spearman, titled "The Proof and Measurement of Association Between Two Variables," addresses the challenges and methods for scientifically evaluating and measuring the correlation between two variables. The author emphasizes the importance of precise measurement and statistical analysis in psychological research, highlighting that many experiments lack rigorous methods and interpretation. The article is divided into two main parts: 1. **Elementary Correlation and "Accidental Deviations"**: - **Requirements of a Good Method**: The author outlines the need for a method that can quantitatively express the relationship between variables, provide meaningful interpretations, and minimize errors. - **Standard Methods**: The "Product Moments" method by Bravais and Pearson is discussed, along with its application in measuring correlations between quantitatively measurable variables. - **Comparison by Rank**: This method is introduced as an alternative when direct quantitative measurement is not feasible, offering a way to compare variables based on their relative positions. - **Auxiliary Methods**: Various auxiliary methods are presented for specific scenarios, including the method of proportional changes, class averages, and rank differences. 2. **Correction of "Systematic Deviations"**: - **Systematic Deviations Generally**: The article discusses the importance of correcting for systematic deviations, which can bias the results. These deviations can arise from various sources, such as self-suggestion or inherent biases in the experimental design. - **"Attenuation" by Errors**: The author explains how errors can "attenuate" the apparent correlation, making it appear weaker than it actually is. This effect is distinct from accidental deviations, which tend to balance out over a large number of observations. Spearman's work aims to provide a rigorous and systematic approach to measuring and interpreting correlations, emphasizing the need for careful experimental design and statistical analysis to ensure the validity and reliability of psychological research.The article by C. Spearman, titled "The Proof and Measurement of Association Between Two Variables," addresses the challenges and methods for scientifically evaluating and measuring the correlation between two variables. The author emphasizes the importance of precise measurement and statistical analysis in psychological research, highlighting that many experiments lack rigorous methods and interpretation. The article is divided into two main parts: 1. **Elementary Correlation and "Accidental Deviations"**: - **Requirements of a Good Method**: The author outlines the need for a method that can quantitatively express the relationship between variables, provide meaningful interpretations, and minimize errors. - **Standard Methods**: The "Product Moments" method by Bravais and Pearson is discussed, along with its application in measuring correlations between quantitatively measurable variables. - **Comparison by Rank**: This method is introduced as an alternative when direct quantitative measurement is not feasible, offering a way to compare variables based on their relative positions. - **Auxiliary Methods**: Various auxiliary methods are presented for specific scenarios, including the method of proportional changes, class averages, and rank differences. 2. **Correction of "Systematic Deviations"**: - **Systematic Deviations Generally**: The article discusses the importance of correcting for systematic deviations, which can bias the results. These deviations can arise from various sources, such as self-suggestion or inherent biases in the experimental design. - **"Attenuation" by Errors**: The author explains how errors can "attenuate" the apparent correlation, making it appear weaker than it actually is. This effect is distinct from accidental deviations, which tend to balance out over a large number of observations. Spearman's work aims to provide a rigorous and systematic approach to measuring and interpreting correlations, emphasizing the need for careful experimental design and statistical analysis to ensure the validity and reliability of psychological research.