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Fitting Latent Growth Models with Small Sample Sizes and Non-normal Missing Data

This study investigates the performance of robust ML estimators when fitting and evaluating small sample latent growth models (LGM) 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 <...

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Bibliografski detalji
Izdano u:Int J Behav Dev
Glavni autori: Shi, Dexin, DiStefano, Christine, Zheng, Xiaying, Liu, Ren, Jiang, Zhehan
Format: Artigo
Jezik:Inglês
Izdano: 2021
Teme:
Online pristup:https://ncbi.nlm.nih.gov/pmc/articles/PMC7928428/
https://ncbi.nlm.nih.gov/pubmed/33664535
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1177/0165025420979365
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