What analysis method is appropriate for comparing various job roles within the compensation system?

The appropriate analysis method for comparing various job roles within the compensation system is ANOVA (Analysis of Variance). ANOVA is particularly useful when assessing the differences in means across multiple groups—in this case, different job roles. It allows organizations to determine if there are statistically significant differences in compensation levels among various job positions, which can help inform equitable pay structures and identify discrepancies that need to be addressed.

In the context of compensation analysis, ANOVA enables HR professionals to evaluate the average salaries or benefit packages of multiple roles simultaneously, rather than comparing them two at a time. This holistic approach can provide clearer insights into how roles stack against one another in terms of compensation, making it beneficial for developing fair and competitive pay practices.

Other statistical methods like a t-test would be limited to comparing only two groups at a time, which is not suitable for this scenario where multiple job roles are involved. Pearson's correlation analysis focuses on the relationship between two continuous variables, rather than comparing means. Regression analysis is used to assess the impact of one or more independent variables on a dependent variable but does not specifically address the comparison between different job roles in a compensation context as directly as ANOVA does.