Uncovering Asymmetric Temporal Dynamics using Threshold Dynamics Parameters

Statistical models to analyze longitudinal data often include parameters that capture temporal dependencies. These dynamics parameters are typically thought to operate independently of the time series value. Here, we argue that this leads to overlooking important information on psychological processes. We propose the DYNamics of ASymmetric TIme series (DYNASTI) approach, allowing dynamics parameters to differ above and below the time series mean. Through extensive simulations, we show that DYNASTI implementations of two commonly-used time series models (DSEM and RI-CLPM) adequately recover symmetric and asymmetric temporal dynamics. Importantly, we also show that assuming symmetric dynamics (as in the vast majority of the literature) when processes are in fact asymmetric leads to incorrect conclusions about these dynamics. We further illustrate how DYNASTI implementations can lead to new insights in three empirical examples. We believe asymmetric dynamics are widespread and hope, by providing open and easy-to-apply code, to aid researchers in uncovering them.