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A Bayesian nonparametric approach to dynamic item-response modeling: An application to the GUSTO cohort study.

Andrea Cremaschi1, Maria De Iorio1,2,3,4, Yap Seng Chong1,2

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Summary

This study introduces a Bayesian model for analyzing longitudinal psychometric questionnaire data from mothers and children. The model reveals three distinct mother-child groups and highlights maternal reporting bias.

Keywords:
Dirichlet processclusteringcohort studyitem-response theoryquestionnaire data

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Area of Science:

  • Statistics and Biostatistics
  • Psychometrics
  • Longitudinal Data Analysis

Background:

  • Item-response theory (IRT) is commonly used for questionnaire data analysis, focusing on respondent profiles and item characteristics.
  • Existing IRT models are often cross-sectional and do not fully capture temporal dependencies or joint analysis of related groups.
  • Analysis of psychometric data from mothers and their children over time presents unique statistical challenges.

Purpose of the Study:

  • To develop a novel Bayesian semiparametric model for analyzing longitudinal psychometric data from mothers and children.
  • To extend current IRT models by incorporating temporal dependence, joint modeling of related subjects, and subject clustering.
  • To identify distinct latent response profiles within mother-child dyads from the GUSTO cohort study.

Main Methods:

  • Proposed a Bayesian semiparametric model to analyze longitudinal questionnaire data.
  • Incorporated temporal dependence for repeated measures within subjects.
  • Jointly modeled responses from mothers and children, accounting for their relationship and allowing for information sharing.
  • Implemented latent profile analysis for subject clustering based on response patterns.

Main Results:

  • The model successfully identified three distinct clusters of mother-child pairs based on their latent response profiles.
  • A significant maternal reporting bias was detected, influencing the observed clustering structure of mother-child dyads.
  • The proposed model effectively handles complex dependencies in longitudinal, multi-group questionnaire data.

Conclusions:

  • The developed Bayesian model provides a robust framework for analyzing complex longitudinal psychometric data.
  • The findings underscore the importance of accounting for temporal dynamics, inter-subject relationships, and reporting biases in such analyses.
  • The study offers valuable insights into maternal reporting effects and identifies distinct mother-child response patterns in the GUSTO cohort.