Fitting Latent Growth Models with Small Sample Sizes and Non-normal Missing Data.
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ABSTRACT:
SUBMITTER: Shi D
PROVIDER: S-EPMC7928428 | biostudies-literature | 2021 Mar
REPOSITORIES: biostudies-literature
Publications
Fitting Latent Growth Models with Small Sample Sizes and Non-normal Missing Data.
Shi Dexin D DiStefano Christine C Zheng Xiaying X Liu Ren R Jiang Zhehan Z
International journal of behavioral development 20210107 2
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., <i>N</i> < 100). Among the robust ML estimators, "MLR" was the optimal choice, as it was found to be robust to both non-normality and missing data while also yielding more accurate standard error estimates ...[more]
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