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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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| 出版年: | Int J Behav Dev |
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| 主要な著者: | , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2021
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| 主題: | |
| オンライン･アクセス: | 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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