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A State Space Model of Daily Dynamics with Moderation Effects from Qualitative Text Data
Samuel D Aragones1, Emorie D Beck1, Emilio Ferrer1
1Department of Psychology, University of California, Davis, USA.
Multivariate Behavioral Research
|April 6, 2026
Summary
This study introduces a state space model to analyze daily affect dynamics, incorporating qualitative sentiment data. Results show sentiment predicts affect more strongly when modeled as a direct predictor rather than a moderator.
Area of Science:
- Psychology
- Computational Social Science
- Data Science
Background:
- Intensive longitudinal data are increasingly used to study psychological processes.
- Existing models often overlook factors influencing these dynamics.
- New methods are needed to integrate diverse data types for richer insights.
Purpose of the Study:
- To present a state space model for examining daily affect dynamics.
- To incorporate qualitative sentiment data as covariates moderating these dynamics.
- To compare the effects of sentiment as a moderator versus a direct predictor.
Main Methods:
- Developed a state space model for daily affect.
- Utilized natural language processing (NLP) to quantify sentiment from open-ended responses.
- Applied a vector autoregressive (VAR) model to assess moderation effects of sentiment on autoregressive and cross-lag parameters.
- Compared sentiment as a moderator versus a direct predictor of affect.
Main Results:
- Moderation effects of sentiment on dynamic parameters were small but robust.
- Sentiment showed strong effects when modeled as a direct predictor of affect.
- Qualitative sentiment data offer valuable insights into dynamic psychological processes.
Conclusions:
- State space models can effectively integrate qualitative data to understand psychological dynamics.
- NLP-derived sentiment is a valuable covariate for analyzing affect.
- Modeling choices significantly impact the interpretation of qualitative data's influence.
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