A State Space Model of Daily Dynamics with Moderation Effects from Qualitative Text Data

The last two decades have seen a dramatic increase in using intensive longitudinal data to capture psychological processes. Intensive longitudinal data allow researchers to study intraindividual change and variability. Multiple modeling approaches have been developed to examine these dynamics in a process as it unfolds over time. What is often not considered in these models are factors that can influence the dynamics of the given process. In this article, we describe a state space model to examine the dynamics of daily affect and combine it with covariates that moderate the parameters describing such dynamics. In our approach, the moderators represent sentiment values from open responses to a daily questionnaire. Unlike standard Likert-type measures, open text allows individuals to express their thoughts and feelings without numerical or wording restrictions. We apply natural language processing to quantify positive and negative sentiment associated with such written responses reported daily. The implemented model utilizes a functional relationship between the variability in dynamic parameters and a time-varying covariate. The target of the moderator covariates are the autoregressive and cross-lag parameters in a vector autoregressive model. We find that such moderation effects from the covariates are small, yet robust. However, when the covariates are specified as predictors of the process instead of the dynamic parameters, their effects are strong. Our analyses show that, overall, qualitative measures are valuable to help understand dynamic processes.