Evaluación de Valores Perdidos en Modelos de Crecimiento Latente

  1. Silvia Nieva 1
  2. J 1
  3. Marta Gràcia 2
  1. 1 Universidad Complutense de Madrid, Facultad de Psicología
  2. 2 Universidad de Barcelona, Facultad de Psicología
Revista:
Revista iberoamericana de diagnóstico y evaluación psicológica

ISSN: 1135-3848

Año de publicación: 2022

Título del ejemplar: Avances en Medición en Psicología

Volumen: 5

Número: 66

Páginas: 65-80

Tipo: Artículo

DOI: 10.21865/RIDEP66.5.05 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de diagnóstico y evaluación psicológica

Resumen

Los modelos de crecimiento latente han sido un avance para el estudio del cambio en investigación longitudinal. La presencia de datos incompletos durante el proceso de medición es un problema permanente en estos estudios. Se plantea una simulación para explorar el papel de distintos mecanismos de pérdida de datos (MCAR, MAR y MNAR) y en la recuperación de parámetros en los modelos de crecimiento latente. Se esperaba encontrar diferencias en la estimación, el ajuste y el sesgo en función de los diferentes escenarios y del tratamiento de datos perdidos. Se han generado muestras de diferentes tamaños, con valores perdidos bajo distintas condiciones. Algunos mecanismos muestran desajustes en la estimación al aplicar procedimientos de eliminación de sujetos. No obstante, otros tratamientos permiten compensarlo. Los cambios en la parametrización afectan al ajuste, al porcentaje de sesgo y aumentan la variabilidad entre réplicas, mostrándose diferencias entre los distintos métodos aplicados.

Referencias bibliográficas

  • Allison, P. D. (2001). Missing data. Sage Publications.
  • Ayilara, O. F., Zhang, L., Sajobi, T. T., Sawatzky, R., Bohm, E., & Lix, L. M. (2019). Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry. Health and Quality of Life Outcomes, 17(1), 1-9. https://doi.org/10.1186/s12955-019-1181-2
  • Baker, G. A. (1954). Factor analysis of relative growth. Growth, 18, 137-143.
  • Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48(1), 5-37. https://doi.org/10.1016/j.jsp.2009.10.001
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246. https://doi.org/10.1037/0033-2909.107.2.238
  • Bock, R. D. (1979). Univariate and multivariate analysis of variance with time-structured data. In J. R. Nesselroade & P. B. Baltes (Eds.), Longitudinal research in the study of behavior and development, 199-232. Academic Press.
  • Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. John Wiley & Sons.
  • Browne, W. J., & Draper, D. (2006). A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis, 1(3), 473-514. https://doi.org/10.1214/06-ba117
  • Byers‐Heinlein, K., Bergmann, C., & Savalei, V. (2021). Six solutions for more reliable infant research. Infant and Child Development, e2296. https://doi.org/10.1002/icd.2296
  • Carpenter, J. R., & Kenward, M. G. (2013). Multiple imputation and its application. John Wiley & Sons.
  • Cernat, A. (2021, 29 de Abril). Estimating and visualizing change in time using Latent Growth Models with R https://www.alexcernat.com/estimating-andvisualizing-change-in-time-using-latentgrowth-models-with-r/
  • Curran, P. J., Obeidat, K., & Losardo, D. (2010). Twelve frequently asked questions about growth curve modeling. Journal of Cognition and Development, 11(2), 121-136. https://doi.org/10.1080/15248371003699969
  • Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to latent variable. Psychology Press. Taylor & Francis.
  • Eekhout, I., de Boer, R. M., Twisk, J. W., de Vet, H. C., & Heymans, M. W. (2012). Missing data: A systematic review of how they are reported and handled. Epidemiology, 23(5), 729-732. https://doi.org/10.1097/ede.0b013e3182576cdb
  • Enders, C. K. (2003). Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data. Psychological Methods, 8(3), 322. https://doi.org/10.1037/1082-989x.8.3.322
  • Enders, C. K. (2010). Applied missing data analysis. Guilford press.
  • Fielding, S., Fayers, P. M., & Ramsay, C. R. (2009). Investigating the missing data mechanism in quality of life outcomes: A comparison of approaches. Health and Quality of Life Outcomes, 7(1), 1-10. https://doi.org/10.1186/1477-7525-7-57
  • Geiser, C. (2021). Longitudinal structural equation modeling with Mplus: A latent statetrait perspective. Guilford Press.
  • Gottman, J. M., & Rushe, R. H. (1993). The analysis of change: Issues, fallacies, and new ideas. Journal of Consulting and Clinical Psychology, 61(6), 907-910. https://doi.org/10.1037/0022-006x.61.6.907
  • Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60(1), 549-576. https://doi.org/10.1146/annurev.psych.58.110405.085530
  • Graham, J. W. (2012). Missing data: Analysis and design. Springer Science & Business Media.
  • Grimm, K. J, Ram, & N. Estabrook, R. (2017). Growth Modeling. Structural Equation and Multilevel Modeling Aproaches. The Guilford Press.
  • Hamilton, J., Gagne, P. E., & Hancock, G. R. (2003). The effect of sample size on latent growth models. Presentado en el Annual Meeting of the American Educational Research Association (Chicago, IL, April, 21-25, 2003)
  • Hancock, G. R., & Freeman, M. J. (2001). Power and sample size for the root mean square error of approximation test of not close fit in structural equation modeling. Educational and Psychological Measurement, 61(5), 741-758. https://doi.org/10.1177/00131640121971491
  • Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling: An overview and a meta-analysis. Sociological Methods & Research, 26(3), 329-367. https://doi.org/10.1177/0049124198026003003
  • Hooper, D., Coughlan, J., Mullen, M., & Hooper, D. (2008) Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Bussiness Research Methods, 6 (1). https://doi.org/10.21427/D7CF7R
  • IBM Corp. (2012). IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.
  • Jia, F., Moore, E. W. G., Kinai, R., Crowe, K. S., Schoemann, A. M., & Little, T. D. (2014). Planned missing data designs with small sample sizes: How small is too small? International Journal of Behavioral Development, 38(5), 435-452. https://doi.org/10.1177/0165025414531095
  • Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486-507. https://doi.org/10.1177/0049124114543236
  • Kim, M., Hsu, H. Y., Kwok, O. M., & Seo, S. (2018). The optimal starting model to search for the accurate growth trajectory in latent growth models. Frontiers in Psychology, 9, 349. https://doi.org/10.3389/fpsyg.2018.00349
  • Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
  • Lawes, M., Schultze, M., & Eid, M. (2020). Making the most of your research budget: Efficiency of a three-method measurement design with planned missing data. Assessment, 27(5), 903-920. https://doi.org/10.1177/1073191118798050
  • Lee, T., & Shi, D. (2021). A comparison of full information maximum likelihood and multiple imputation in structural equation modeling with missing data. Psychological Methods, 26(4), 466-485. https://doi.org/10.1037/met0000381
  • Little, T. D., & Rhemtulla, M. (2013). Planned missing data designs for developmental researchers. Child Development Perspectives, 7(4), 199-204. https://doi.org/10.1111/cdep.12043
  • Maydeu-Olivares, A. (2017). Maximum likelihood estimation of structural equation models for continuous data: Standard errors and goodness of fit. Structural Equation Modeling: A Multidisciplinary Journal, 24(3), 383-394. https://doi.org/10.1080/10705511.2016.1269606
  • McNeish, D. (2017). Missing data methods for arbitrary missingness with small samples. Journal of Applied Statistics, 44(1), 24-39. https://doi.org/10.1080/02664763.2016.1158246
  • McNeish, D., & Matta, T. (2018). Differentiating between mixed-effects and latent-curve approaches to growth modeling. Behavior Research Methods, 50(4), 1398-1414. https://doi.org/10.3758/s13428-017-0976-5
  • Miranda, H., Saracostti, M., Miranda-Zapata, E., & Lara, L. (2021). Análisis longitudinal del rendimiento escolar según el compromiso escolar y factores contextuales: El caso chileno. Revista Iberoamericana de Diagnóstico y Evaluación – e Avaliação Psicológica, 2(59), 163-175. https://doi.org/10.21865/ridep59.2.13
  • Rao, C. R. (1958). Some statistical methods for comparison of growth curves. Biometrics, 14(1), 1-17. https://doi.org/10.2307/2527726
  • Rast, P., & Hofer, S. M. (2014). Longitudinal design considerations to optimize power to detect variances and covariances among rates of change: simulation results based on actual longitudinal studies. Psychological Methods, 19(1), 133. https://doi.org/10.1037/a0034524
  • Rioux, C. & Little, T. D. (2020). Underused methods in developmental science to inform policy and practice. Child Development Perspectives. 14(2), 97-103. https://doi.org/10.1111/cdep.12364
  • Rioux, C., & Little, T. D. (2021a). Introduction to the special section on developmental approaches to prevention science. International Journal of Behavioral Development, 45(1), 1-2. https://doi.org/10.1177/0165025420943677
  • Rioux, C., & Little, T. D. (2021b). Missing data treatments in intervention studies: what was, what is, and what should be. International Journal of Behavioral Development, 45(1), 51-58. https://doi.org/10.1177/0165025419880609
  • Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
  • Rovine, M. J., & McDermott, P. A. (2018). Latent growth curve and repeated measures ANOVA contrasts: What the models are telling you. Multivariate behavioral research, 53(1), 90101. https://doi.org/10.1080/00273171.2017.1387511
  • Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581-592. https://doi.org/10.1093/biomet/63.3.581
  • RStudio Team (2020). RStudio: Integrated development for R. Rstudio, PBC, Boston, MAC. URL: http://www.rstudio.com
  • Shi, D., DiStefano, C., Zheng, X., Liu, R., & Jiang, Z. (2021). Fitting latent growth models with small sample sizes and non-normal missing data. International Journal of Behavioral Development, 45(2), 179-192. https://doi.org/10.1177/0165025420979365
  • Singer, J. D., & Willett, J. B. (2003). A framework for investigating change over time. En: J. D. Singer, J. B Willet. Applied longitudinal data analysis: Modeling change and event occurrence. (pp. 1-17). Oxford University Press.
  • Solano, A. C., & Casullo, M. M. (2005). Análisis del cambio en los estilos de liderazgo de un grupo de estudiantes militares. Revista Iberoamericana de Diagnóstico y Evaluación – e Avaliação Psicológica, 1(19), 105-120.
  • Steiger, J. H., & Lind, J. C. (1980). Statistically based tests for the number of common factors. Presentado en Annual Meeting of the Psychometric Society, Iowa City, IA.
  • Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual Differences, 42(5), 893-898. https://doi.org/10.1016/j.paid.2006.09.017
  • Tucker, L. R. (1958). Determination of parameters of a functional relation by factor analysis. Psychometrika, 23(1), 19-23. https://doi.org/10.1007/bf02288975
  • Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1-10. https://doi.org/10.1007/BF02291170
  • Vidal Arenas, J. (2015). La concepción del tiempo en Aristóteles. Byzantion nea hellás, (34), 323-340. https://doi.org/10.4067/s0718-84712015000100014
  • Whittaker, T. A., & Khojasteh, J. (2017). Detecting appropriate trajectories of growth in latent growth models: The performance of information-based criteria. The Journal of Experimental Education, 85(2), 215-230. https://doi.org/10.1080/00220973.2015.1123669
  • Wu, W., & Jia, F. (2021). Applying planned missingness designs to longitudinal panel studies in developmental science: An overview. New Directions for Child and Adolescent Development, 175, 35-63. https://doi.org/10.1002/cad.20391
Fuente de los datos: Dialnet