State-space modeling provides a flexible tool in time series analysis. A wide variety of psychological time series models can be reconciled into a simple linear model by rewriting them in a state-space form. Once a model is transformed into the state-space framework, the algorithm of the Kalman filter and Kalman smoother can be used to estimate the latent state variables. This article shows how simple state-space models can be cast as a standard mixed model, provided the transition matrix of the state equation has a simple form. This approach provides opportunities for modeling medium-size time series for researchers proficient in mixed models but less experienced in state-space modeling. In addition, integrating state-space components into a mixed model broadens the class of variance-covariance structures that may be employed to model serial correlation in longitudinal data. Some examples will be used to illustrate how to formulate state-space models in mixed model form. The merits and disadvantages of state-space and mixed effects modeling approach will be discussed.