Traditionally, researchers have used time series and multilevel models to analyze intensive longitudinal data. However, these models do not directly address traits and states which conceptualize the stability and variability implicit in longitudinal research, and they do not explicitly take into account measurement error. An alternative to overcome these drawbacks is to consider structural equation models (state-trait SEMs) for longitudinal data that represent traits and states as latent variables. Most of these models are encompassed in the latent state-trait (LST) theory. These state-trait SEMs can be problematic when the number of measurement occasions increases. As they require the data to be in wide format, these models quickly become overparameterized and lead to nonconvergence issues. For these reasons, multilevel versions of state-trait SEMs have been proposed, which require the data in long format. To study how suitable state-trait SEMs are for intensive longitudinal data, we carried out a simulation study. We compared the traditional single level to the multilevel version of three state-trait SEMs. The selected models were the multistate-singletrait (MSST) model, the common and unique trait-state (CUTS) model, and the trait-state-occasion (TSO) model. Furthermore, we also included an empirical application. Our results indicated that the TSO model performed best in both the simulated and the empirical data. To conclude, we highlight the usefulness of state-trait SEMs to study the psychometric properties of the questionnaires used in intensive longitudinal data. Yet, these models still have multiple limitations, some of which might be overcome by extending them to more general frameworks. Translational Abstract Collecting data from individuals multiple times a day for several days is becoming more popular among psychological researchers. This kind of data, known as intensive longitudinal data, allows studying how people change over short periods of time. In the present study, we explored how to study traits and states in intensive longitudinal data. Traits conceptualize the stability of variables, whereas states conceptualize their variability. For this, we conducted a simulation study in which we studied three state-trait structural equation models (state-trait SEMs). State-trait SEMs are measurement models for longitudinal data that represent traits and states as latent variables and include measurement error terms. Our results showed that the trait-state-occasion (TSO) model outperforms the other two. We concluded that the TSO model is an additional tool to consider when analyzing intensive longitudinal data.