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Modelado de efectos de interacción latente dentro de los niveles en modelos vectoriales autorregresivos de varios

Jana Holtmann1, Kenneth Koslowski2

  • 1Wilhelm-Wundt Institute for Psychology, Leipzig University, Neumarkt 9-19, 04109, Leipzig, Germany. jana.holtmann@uni-leipzig.de.

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Resumen
Este resumen es generado por máquina.

Este estudio introduce modelos avanzados de series temporales latentes de varios niveles para capturar cómo la dinámica interna de la persona cambia con el tiempo, teniendo en cuenta los moderadores que varían en el tiempo. Estos modelos ofrecen una comprensión más matizada de los procesos psicológicos complejos.

Palabras clave:
Modelado dinámico de ecuaciones estructuralesDatos longitudinales intensivosInteracción latenteLa moderaciónAnálisis de las series temporales

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Área de la Ciencia:

  • Ciencias Psicológicas
  • Psicología cuantitativa
  • Análisis longitudinal de los datos

Sus antecedentes:

  • Los modelos de series temporales de varios niveles (latentes) se utilizan cada vez más para la dinámica interna.
  • Los modelos actuales a menudo pasan por alto los moderadores que varían en el tiempo e influyen en las relaciones longitudinales.
  • Esto limita la comprensión de cómo los procesos dinámicos dentro de la persona se ven afectados por factores cambiantes.

Objetivo del estudio:

  • Ampliar los modelos de series temporales latentes de varios niveles mediante la incorporación de efectos de interacción latente a nivel interno.
  • Proporcionar un tutorial para la aplicación de estos modelos mejorados utilizando la estimación bayesiana.
  • Investigar la dinámica temporal del afecto negativo, la rumia y la atención consciente.

Principales métodos:

  • Extensión de los modelos de series temporales latentes de varios niveles para incluir efectos de interacción latente.
  • Estimación bayesiana a través de las técnicas de cadena de Markov Monte Carlo (MCMC).
  • Estudios de simulación para evaluar el rendimiento y la complejidad del modelo.

Principales resultados:

  • Se ha demostrado la incorporación exitosa de efectos de interacción latente en análisis dinámicos interpersonales.
  • Proporcionó ejemplos empíricos usando afecto negativo, rumiación y atención consciente.
  • Recomendaciones sobre la complejidad del modelo y los requisitos de tamaño de la muestra basadas en simulaciones.

Conclusiones:

  • Los modelos mejorados proporcionan un enfoque más amplio para estudiar la dinámica interna dependiente del tiempo.
  • Los investigadores aplicados pueden utilizar estos modelos para explorar relaciones longitudinales matizadas.
  • Los tamaños de muestra adecuados (por ejemplo, 100 puntos de tiempo para 100 personas) son cruciales para los modelos complejos de efectos aleatorios.