Can we trust our numbers? Quantification of measurement reliability for intensive longitudinal data

I will discuss the new statistical approach to analyzing intensive longitudinal data (ILD), which is a growing research area in many research domains, such as psychology or medicine sciences. With the progress of technology, ILD, which records psychological or biological Information in real-time, is easily obtained by wearable devices or smartphones. However, we still know little about how to approach this huge amount of data, especially the statistical properties of the various model. The main goals of this project are that we want to use the state-space model with Bayesian statistical approaches for ILD. Specifically, we would extend the model to apply to kinds of measurement outcomes (binary, Gaussian, and bounded) and discuss the reliability and agreement properties. In addition, a user-friendly software/package will be built to fit the models.