1. Explain the Measures of Relationship ?
Measures of relationship refer to statistical techniques used to determine the strength and direction of the association between two variables. There are different measures of relationship commonly used in research and statistics, including:
1. Pearson's correlation coefficient: This measure of relationship calculates the strength and direction of the linear relationship between two continuous variables. The value of the correlation coefficient ranges from -1 to +1, where +1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no relationship.
2. Spearman's rank correlation coefficient: This measure of relationship is used to assess the strength and direction of the relationship between two variables that are ordinal or ranked in nature. It measures the monotonic relationship between two variables, which means that it captures any type of consistent relationship between the variables, not just linear relationships.
3. Phi coefficient: This measure is used to assess the relationship between two binary (dichotomous) variables. It calculates the strength and direction of the association between the two variables using a formula similar to Pearson's correlation coefficient.
4. Point-biserial correlation: This measure is used to determine the relationship between a continuous variable and a binary (dichotomous) variable. It calculates the strength and direction of the relationship between the two variables.
5. Cramér's V: This measure is used to assess the relationship between two nominal variables. It calculates the strength and direction of the association between the variables based on the chi-square statistic.
Overall, measures of relationship help researchers determine the extent to which variables are related to each other and provide important information for hypothesis testing and understanding the patterns in the data.