Modeling psychological time series with multilevel hidden Markov models: A tutorial

Time series (or intensive longitudinal) data are now widely used in psychological science. These data allow researchers to study the dynamics of human functioning at an unprecedented level of granularity. Capturing these dynamics requires appropriate statistical models. One powerful class of models for such data is the hidden Markov model (HMM), which captures processes that switch between a number of latent states over time, each associated with a different probability distribution. Unlike the currently predominant linear models, such as the vector autoregressive model, HMMs can capture more complex behaviors. They can characterize multiple equilibrium states (for instance, manic and depressed states as seen in bipolar disorder) and quantify how the system transitions between them. HMMs can also fit common empirical data patterns such as highly skewed or multimodal distributions. Despite their effectiveness in modeling within-person dynamics, HMMs are not widely used. We see three reasons: (1) limited familiarity among researchers, (2) only recent availability of software for multilevel HMMs, and (3) the relative complexity of estimating, analyzing, and reporting HMMs. Here, we address these issues by providing a gentle introduction to HMMs for researchers working with time series data in psychology, including a fully reproducible tutorial that walks the reader through each of the steps of analyzing psychological time series data using the R-package mHMMbayes. We hope our tutorial helps researchers working with time series data with adding (multilevel) HMMs into their methodological toolbox. (PsycInfo Database Record (c) 2026 APA, all rights reserved).