Abstract: Increasingly, psychologists of all kinds and developmental researchers encounter data where several individuals were measured on multiple variables over numerous occasions. Many current methods combine these data, assuming everyone is a randomly equivalent. An extreme alternative is to assume no one is randomly equivalent. This presentation argues for a method as a compromise. The goal is to find people that are undergoing similar change processes over time. Data were simulated under various conditions to explore what factors influenced the ability to correctly estimate the change process and find people with the same process. It was found that sample size had the greatest influence on parameter estimation and the dimension of the change process had the greatest impact on correctly grouping people together, likely due to the distinctiveness of their patterns of change. The method of state space mixture modeling was then applied to an archival data source reflecting cognitive growth in the National Longitudinal Survey of Youth Children data. This analysis suggested that the genetic effects operating between people on their cognitive development may be quite different from their within-person effects. State space mixture modeling offers one of the best-performing methods for simultaneously drawing conclusions about individual change processes while also analyzing multiple people.