Set correlation (SC) is a multivariate generalization of multiple correlation and is a realization of the general multivariate linear model. It is used for analyzing multivariate data and supplements four methods for analyzing two-way contingency tables. SC assesses specific hypotheses about associations between variables and provides measures of association, significance tests, and power analysis. It is particularly useful for contingency tables and can be applied to any form of multivariate data. SC is a general method for studying relationships between two sets of variables, X and Y, and can be used in various forms. It includes measures of association, such as multivariate R², which is a generalization of multiple R². SC also provides significance tests and power analysis, and computer programs are available for its implementation. In the analysis of contingency tables, SC uses partialling (residualization) to assess specific hypotheses. For example, in the analysis of a 3×4 contingency table from a survey on abortion, SC was used to examine the relationship between religion and abortion response. The results showed a significant overall association between religion and abortion response, with the majority religions showing a higher rate of "No" responses compared to "Yes" responses. SC also allows for different coding methods, such as dummy coding, effects coding, and contrast coding, which can be used to test specific hypotheses. For instance, effects coding was used to examine the individual effects of religious groups on abortion responses. The results showed that each religious group had a significant effect on the abortion response, with Catholics and Protestants showing a tendency to respond "Yes" less than the equally-weighted combination of the other groups, while Jews showed a tendency to respond "Yes" more. SC provides a unified framework for analyzing relationships among phenomena, unconstrained by the level of measurement. It offers measures of association, hypothesis testing, and power analysis, and is particularly useful for contingency tables. SC is a multivariate generalization of multiple correlation and can be used to analyze higher-order contingency tables, including those involving additional variables such as region or education level. The results of SC analysis are equivalent to those obtained using other methods such as Pearson chi-square analysis, MANOVA, and correspondence analysis. SC provides a more accurate test statistic than the Pillai-Bartlett chi-square procedure, although for large samples, the tests are virtually interchangeable. SC is robust and can be used for contingency tables, even when the assumption of multivariate normality is not met. The Rao F test is preferred for its accuracy and robustness in power estimation.Set correlation (SC) is a multivariate generalization of multiple correlation and is a realization of the general multivariate linear model. It is used for analyzing multivariate data and supplements four methods for analyzing two-way contingency tables. SC assesses specific hypotheses about associations between variables and provides measures of association, significance tests, and power analysis. It is particularly useful for contingency tables and can be applied to any form of multivariate data. SC is a general method for studying relationships between two sets of variables, X and Y, and can be used in various forms. It includes measures of association, such as multivariate R², which is a generalization of multiple R². SC also provides significance tests and power analysis, and computer programs are available for its implementation. In the analysis of contingency tables, SC uses partialling (residualization) to assess specific hypotheses. For example, in the analysis of a 3×4 contingency table from a survey on abortion, SC was used to examine the relationship between religion and abortion response. The results showed a significant overall association between religion and abortion response, with the majority religions showing a higher rate of "No" responses compared to "Yes" responses. SC also allows for different coding methods, such as dummy coding, effects coding, and contrast coding, which can be used to test specific hypotheses. For instance, effects coding was used to examine the individual effects of religious groups on abortion responses. The results showed that each religious group had a significant effect on the abortion response, with Catholics and Protestants showing a tendency to respond "Yes" less than the equally-weighted combination of the other groups, while Jews showed a tendency to respond "Yes" more. SC provides a unified framework for analyzing relationships among phenomena, unconstrained by the level of measurement. It offers measures of association, hypothesis testing, and power analysis, and is particularly useful for contingency tables. SC is a multivariate generalization of multiple correlation and can be used to analyze higher-order contingency tables, including those involving additional variables such as region or education level. The results of SC analysis are equivalent to those obtained using other methods such as Pearson chi-square analysis, MANOVA, and correspondence analysis. SC provides a more accurate test statistic than the Pillai-Bartlett chi-square procedure, although for large samples, the tests are virtually interchangeable. SC is robust and can be used for contingency tables, even when the assumption of multivariate normality is not met. The Rao F test is preferred for its accuracy and robustness in power estimation.