Socioeconomic status (SES) is a key factor in health disparities among racial/ethnic minorities, women, and the elderly. However, studies on SES and health disparities have produced inconsistent results due to issues such as lack of precision in measures, difficulty in collecting individual SES data, and the dynamic nature of SES over a lifetime. Choosing the best SES measure depends on its relevance to the population and outcomes under study. Common SES measures, such as occupation, education, and income, have limitations in capturing the full impact of SES on health outcomes, especially for subgroups experiencing disparities. Contextual SES measures, which consider environmental and community factors, also have limitations in correlation with individual measures. Composite SES measures, like the Townsend Index and the Hollingshead Index, combine multiple indicators but may not fully capture SES effects. SES is dynamic and influences health over the life course, with early experiences potentially affecting later health outcomes. Analyzing SES data in health disparities research requires careful consideration of methodological issues, including nonresponse bias and the interaction between race/ethnicity and SES. Multilevel analyses combine compositional and contextual measures to better understand SES effects. Interpretation of SES analyses must account for potential biases and the complex interactions between SES, race, and health outcomes. Despite challenges, measuring SES remains crucial for understanding and reducing health disparities. Future research should focus on more detailed and context-specific SES measures to inform effective interventions.Socioeconomic status (SES) is a key factor in health disparities among racial/ethnic minorities, women, and the elderly. However, studies on SES and health disparities have produced inconsistent results due to issues such as lack of precision in measures, difficulty in collecting individual SES data, and the dynamic nature of SES over a lifetime. Choosing the best SES measure depends on its relevance to the population and outcomes under study. Common SES measures, such as occupation, education, and income, have limitations in capturing the full impact of SES on health outcomes, especially for subgroups experiencing disparities. Contextual SES measures, which consider environmental and community factors, also have limitations in correlation with individual measures. Composite SES measures, like the Townsend Index and the Hollingshead Index, combine multiple indicators but may not fully capture SES effects. SES is dynamic and influences health over the life course, with early experiences potentially affecting later health outcomes. Analyzing SES data in health disparities research requires careful consideration of methodological issues, including nonresponse bias and the interaction between race/ethnicity and SES. Multilevel analyses combine compositional and contextual measures to better understand SES effects. Interpretation of SES analyses must account for potential biases and the complex interactions between SES, race, and health outcomes. Despite challenges, measuring SES remains crucial for understanding and reducing health disparities. Future research should focus on more detailed and context-specific SES measures to inform effective interventions.