Sensitivity of fi ve information criteria to discriminate covariance structures with missing data in repeated measures designs

  1. Pablo Livacic-Rojas 1
  2. Paula Fernández 2
  3. Guillermo Vallejo 2
  4. Ellián Tuero-Herrero 2
  5. Feliciano Ordóñez 2
  1. 1 Universidad de Santiago de Chile
    info

    Universidad de Santiago de Chile

    Santiago de Chile, Chile

    ROR https://ror.org/02ma57s91

  2. 2 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Revista:
Psicothema

ISSN: 0214-9915,  1886-144X

Año de publicación: 2020

Volumen: 32

Número: 3

Páginas: 399-409

Tipo: Artículo

DOI: 10.7334/PSICOTHEMA2020.63 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Psicothema

Objetivos de desarrollo sostenible

Resumen

Antecedentes: el presente trabajo analiza la efectividad de distintos criterios de información para seleccionar estructuras de covarianza extendiéndolo a diferentes mecanismos de pérdida de datos, la mantención y ajustes de las estructuras de medias y las matrices. Método: se utilizó el método Monte Carlo con 1.000 simulaciones, el software estadístico SAS 9.4 y un diseño de medidas parcialmente repetidas (p=2; q=5). Las variables manipuladas fueron: a) complejidad del modelo; b) tamaño muestral; c) emparejamiento de las matrices de covarianza y tamaño muestral; d) matrices de dispersión; e) forma de distribución de la variable; y f) mecanismo de no respuesta. Resultados: los resultados muestran que todos los criterios de información funcionan bien en el escenario 1 para distribuciones normales y no normales con homogeneidad y heterogeneidad de varianzas. Sin embargo, en los escenarios 2 y 3, todos son precisos con la matriz ARH, aunque, AIC, AICCR y HQICR lo hacen para TOEP y UN. Por otro lado, cuando la distribución no es normal, solo en el escenario 3 funcionan bien AIC y AICCR, matrices más heterogéneas y No Estructurada, con Casos Completo MAR y MCAR. Conclusiones: en consecuencia, para seleccionar la matriz correctamente se recomienda analizar la heterogeneidad, tamaño muestral y distribución de los datos.

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Información de financiación

Acknowledgements The authors would like express acknowledge the suggestions of revisors to improve this work has been funded by the Chilean National Fund for Scientific and Technological Development (FONDECYT. Ref.: 1170642) and the Spanish Ministry of Science, Innovation and Universities (Ref: PGC2018-101574-B-I00).

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