The article discusses the importance of disaggregating between-person and within-person effects in longitudinal data analysis. Longitudinal models allow researchers to examine changes over time and distinguish between effects that occur within individuals and those that occur across individuals. However, this capability is not fully utilized in many social science applications. The review highlights the need for better methods to separate these effects and discusses the challenges in doing so. It emphasizes the importance of multilevel modeling and growth modeling in this context. The article also discusses the ecological fallacy, which occurs when aggregate-level findings are misapplied to individual-level conclusions. The review proposes a general framework for defining within-person and between-person effects and discusses the conditions under which traditional methods are valid. It also considers different scenarios where the time-varying covariate (TVC) is unrelated to time, has a fixed effect of time, or has both fixed and random effects of time. The article concludes that while traditional methods are useful, they have limitations and that more research is needed to develop better methods for disaggregating effects in longitudinal data.The article discusses the importance of disaggregating between-person and within-person effects in longitudinal data analysis. Longitudinal models allow researchers to examine changes over time and distinguish between effects that occur within individuals and those that occur across individuals. However, this capability is not fully utilized in many social science applications. The review highlights the need for better methods to separate these effects and discusses the challenges in doing so. It emphasizes the importance of multilevel modeling and growth modeling in this context. The article also discusses the ecological fallacy, which occurs when aggregate-level findings are misapplied to individual-level conclusions. The review proposes a general framework for defining within-person and between-person effects and discusses the conditions under which traditional methods are valid. It also considers different scenarios where the time-varying covariate (TVC) is unrelated to time, has a fixed effect of time, or has both fixed and random effects of time. The article concludes that while traditional methods are useful, they have limitations and that more research is needed to develop better methods for disaggregating effects in longitudinal data.