Background: In response to the rapid growth of the aging population and the decline of the nuclear family, varied levels of long-term care have developed. Yet, it has been difficult to recruit and retain the "pink collar" workers who care for the residents. Moreover, there has been little methodologically strong research to try and understand the complex factors influencing job satisfaction and intent to leave. Aim: The aims of this study were: 1) to examine the influence of relevant work-related and personal factors on job satisfaction among Certified Nursing Assistants (CNAs) working in nursing homes, 2) to examine the influence of relevant work-related and personal factors, and job satisfaction on intent to leave among CNAs working in nursing homes, and 3) to identify the latent groups of CNAs that have different relationships among the factors and intent to leave. Methods: A descriptive correlational design was used to conduct a secondary data analysis from two linked databases: the 2004 National Nursing Home Survey and the 2004 National Nursing Assistant Survey. Two-level logistic regression modeling and two-level mixture modeling were performed using Stata 10.0/IC and Mplus 5.0. Results: Job satisfaction was significantly associated with intent to leave. Supportive supervision was a significant predictor of both CNAs' job satisfaction and intent to leave. While personal factors (age, education, and job history) were related to intent to leave, but not to job satisfaction. The mixture models explored the latent classes for intent to leave. Distinct differences in the relationships among the factors and CNA intent to leave were found between two classes; one class reflected CNAs where supportive supervision was a significant effect on intent to leave while for the other group, there was no significant relationship. Conclusion: Findings corroborate the results from previous studies that showed supportive supervision or compensation significantly contribute to higher job satisfaction and less intent to leave among CNAs working in nursing homes. Yet this study found that all CNAs do not respond to the motivators in the same way. Multilevel mixture modeling is a promising analytical technique that would provide more useful data.