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

Revue:
Psicothema

ISSN: 0214-9915,  1886-144X

Année de publication: 2020

Volumen: 32

Número: 3

Pages: 399-409

Type: Article

DOI: 10.7334/PSICOTHEMA2020.63 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: Psicothema

Objectifs de Développement Durable

Résumé

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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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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Références bibliographiques

  • Aberson, C.L. (2019). Applied Power Analysis for the Behavioral Sciences. Routledge.
  • Blanca, M. J., Arnau, J., López-Montiel, D., Bono, R., & Bendayan, R. (2013). Skewness and kurtosis in real data samples. Methodology:
  • European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84. https://doi.org/10.1027/1614-2241/a000057.
  • Bono, R., Blanca, M. J., Arnau, J., & Gómez-Benito, J. (2017). Non-normal distributions commonly used in health, education, and social sciences: A systematic review. Frontiers in Psychology-Quantitative Psychology and Measurement, 8, Article 1602, 1-6. https://doi.org/10.3389/ fpsyg.2017.01602
  • Bono, R., Arnau, J., Alarcón, R., & Blanca, M. J. (2020). Bias, precision and accuracy of skewness and kurtosis estimators for frequently used continuous distributions. Symmetry, 12(1), 19. https://doi.org/10.3390/ sym12010019
  • Dong & Peng (2013). Principled missing data methods for researchers. SpringerPlus, 2, 222. https://doi.org/10.1186/2193-1801-2-222
  • Fernández, P., Vallejo G., & Livacic-Rojas P. (2010). Robustez de cinco estadísticos univariados para analizar diseños Split Plot en condiciones adversas [Robustness of fi ve univariate statistics to analyze Split Plot designs in adverse conditions]. Revista Latinoamericana de Psicología, 42(2), 289-309.
  • Fernández, P., Vallejo, G., Livacic-Rojas, P., & Tuero-Herrero, E. (2018). The (Ir)Responsibility of (Under)Estimating Missing Data. Frontiers in Psychology. 9, 556-568. https://doi.org/10.3389/fpsyg.2018.00556
  • Fleishman, A.I. (1978). A method for simulating non-normal distributions. Psychometrika, 43, 521-532. https://doi.org/10.1007/BF02293811
  • Funatogawa, I., & Funatogawa, T. (2019). Longitudinal Data Analysis. Autoregresive Linear Mixed Effects Models. Springer.
  • Garson, G.D. (2020). Multilevel Modeling: Applications in STATA®, IBM® SPSS®, SAS®, R, & HLM™. Sage Publications.
  • Grissom, R., & Kim, J. (2012). Effect size for research: Univariate and multivariate applications. 2nd edition. Routledge.
  • Grund, S., Lüdtke, O., & Robitzsch, A. (2019). Missing data in Multilevel Research. In S.E. Humphrey & J.M. LeBreton (Editors-in-Chief), The Handbook of Multilevel Theory, Measurement and Analysis (pp. 365- 386). American Psychological Association.
  • Heck, R., & Thomas, S.L. (2020). An Introduction to Multilevel Modeling Techniques. MLM and SEM Approaches Using Mplus. Routledge.
  • Leke, C. A., & Marwala, T. (2019). Deep Learning and Missing Data in Engineering Systems (Studies in Big Data Book 48). Springer.
  • Little, R.J.A., & Rubin D.B. (2020). Statistical Analysis with Missing Data (3rd edition). Wiley-Blackwell.
  • Livacic-Rojas P., Vallejo G., & Fernández, P. (2013). Covariance structures selection and Type I Error rates in Split Plot designs. Methodology. European Journal of Research Methods for the Behavioural and Social Sciences, 9(4), 129-136. https://doi.org/10.1027/1614-2241/a000058.
  • Livacic-Rojas P., Vallejo G., & Fernández, P. (2017). Power of modifi ed Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs. Methodology. European Journal of Research Methods for the Behavioural and Social Sciences, 13(1), 9-22. https://doi.org/10.1027/1614-2241/ a000124
  • Liu, X. (2016). Methods and Applications of Longitudinal Data Analysis. Academic Press.
  • Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105, 156-166. https://doi.org/10.1037/ 0033-2909.105.1.156
  • Molenberghs, G., Fitzmaurice, G., Kenward, M., Tsiatis, A., & Verbeke, G. (2015). Handbook of Missing Data Methodology. CRC Press.
  • Raghunathan, T. (2016). Missing Data Analysis in Practice. CRC Press.
  • Raghunathan, T., Berglund, P., & Solenberger, P. (2018). Multiple Imputation in Practice. CRC Press. Ripley, B. D. (1987). Statistical Inference for Spatial Processes. Cambrigde University Press.
  • Sullivan, T.R., Yelland L.N., Lee K.L., Ryan, P., & Salter, A.B. (2017). Treatment of missing data follow-up studies of randomized controlled trials: A systematic review of the literature. Clinical Trials, 14(4), 387- 395. https://doi.org/10.1177/1740774517703319
  • Vale, C. D., & Maurelli, V. A. (1983). Simulating multivariate non normal distributions. Psychometrika, 48, 465-471. https://doi.org/10.1007/ BF02293687.
  • Vallejo, G., Fernández, M. P., Livacic-Rojas, P. E., & Tuero-Herrero, E. (2011a). Selecting the best unbalanced repeated measures model. Behavior Research Methods, 43, 18-36. https://doi.org/10.3758/ s13428-010-0040-1
  • Vallejo, G., Fernández, P., Livacic-Rojas, P., & Tuero-Herrero, E.(2011b). Comparison of modern methods for analyzing repeated measures data with missing values. Multivariate Behavioral Research, 46, 900-937. https://doi.org/10.1080/00273171.2011.625320
  • Vallejo G., Tuero-Herrero E., Núñez J.C., & Rosário P. (2014). Performance evaluation of recent information criteria for selecting multilevel models in Behavioral and Social Sciences. International Journal of Clinical and Health Psychology, 14, 48-57. https://doi.org/10.1016/S1697- 2600(14)70036-5
  • Vallejo, G., Ato, M., Fernández, M.P., Livacic-Rojas, P.E., & Tuero-Herrero, E. (2016). Power analysis to detect treatment effect in longitudinal studies with heterogeneous errors and incomplete data. Psicothema, 28(3), 330-339. https://doi.org/10.7334/psicothema2016.92
  • Vallejo, G., Fernández, M. P., & Livacic-Rojas, P. E. (2018). Data analysis of incomplete repeated measures using a multivariate extension of the Brown-Forsythe procedure. Psicothema, 30(4), 434-441. https://doi. org/10.7334/psicothema2018
  • Vallejo, G., Fernández, M. P., & Livacic-Rojas, P. E. (2019). Sample size estimation for heterogeneous growth curve models with attrition. Behavior Research Methods, 51(3), 1216-1243. https://doi.org/10.3758/ s13428-018-1059-y
  • Wilcox, R. (2017). Introduction to Robust Estimation & Hypothesis Testing (4rd ed.). Elsevier.
  • Yoo, J.E. (2013). Multiple Imputation with Structural Equation Modeling. Using auxiliary variables when data are missing. Academic Publishing.
  • Zhang, Z., & Yuan, K. (2018). Practical Statistical Power Analysis. Using Web Power and R. Isdsa Press.
  • Zhou, X.H., Zhou, Ch., Liu, D., & Ding, X. (2014). Applied Missing Data Analysis in the Health Sciences. Wiley & Sons.
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