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Direct linkShi, Dexin; DiStefano, Christine; Zheng, Xiaying; Liu, Ren; Jiang, Zhehan – International Journal of Behavioral Development, 2021
This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N < 100). Among the robust ML…
Descriptors: Growth Models, Maximum Likelihood Statistics, Factor Analysis, Sample Size
