Given the temporal density of the data we collect (viz. EMA and passive sensing smartphone metrics), we often employ statistical methods to enhance inference of repeated assessment within people over time. These methods include Multilevel Modeling (MLM; e.g., Fulford, Johnson, Llabre, et al., 2010), Structural Equation Modeling (SEM; e.g., Fulford, Piskulic, et al., 2018), and other robust approaches to analyzing longitudinal data (e.g., Generalized Estimating Equations; Fulford, Treadway, & Woolley, 2018).
We are also excited to employ new and evolving statistical methods in our work. Current projects involve the use of Exploratory Structural Equation Modeling (ESEM) for survey development, multilevel vector autoregressive network analyses for time series data, and machine learning approaches to improve inference of clinical classification.