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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.

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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.

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Natural language processingidiographic analysisstate space modelstime series

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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.