Which measure indicates the magnitude of a relationship between two variables?

Which measure indicates the magnitude of a relationship between two variables?

• A. Reliability
• B. Effect size
• C. Standard deviation
• D. Factor analysis

Answer: (B)

The measure that indicates the magnitude of a relationship between two variables is effect size. Effect size quantifies the strength of the relationship or the impact of an intervention, providing a way to assess not just whether a relationship exists but also how significant that relationship is. Effect size can be expressed in different forms, such as Cohen's d, which is commonly used for comparing means, or Pearson's r, which measures correlation. These statistics provide valuable context for interpreting results in psychological research, helping researchers understand the practical significance of their findings beyond mere statistical significance. Reliability, on the other hand, refers to the consistency of a measure, while standard deviation measures the dispersion of a dataset, indicating how much individual scores differ from the mean. Factor analysis is a statistical technique used to identify underlying relationships between variables but does not directly convey the magnitude of those relationships. Each of these concepts plays a different role in research methodology and data analysis.

The measure that indicates the magnitude of a relationship between two variables is effect size. Effect size quantifies the strength of the relationship or the impact of an intervention, providing a way to assess not just whether a relationship exists but also how significant that relationship is.

Effect size can be expressed in different forms, such as Cohen's d, which is commonly used for comparing means, or Pearson's r, which measures correlation. These statistics provide valuable context for interpreting results in psychological research, helping researchers understand the practical significance of their findings beyond mere statistical significance.

Reliability, on the other hand, refers to the consistency of a measure, while standard deviation measures the dispersion of a dataset, indicating how much individual scores differ from the mean. Factor analysis is a statistical technique used to identify underlying relationships between variables but does not directly convey the magnitude of those relationships. Each of these concepts plays a different role in research methodology and data analysis.