A systematic approach for identifying level-1 error covariance structures in latent growth modeling
| Autor: | Chih Kang Shen, Hang Rung Lin, Chiu Hui Wu, Ten Der Jane, Cherng G. Ding |
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| Rok vydání: | 2016 |
| Předmět: |
Social Psychology
Covariance function Latent growth modeling 05 social sciences 050401 social sciences methods 050301 education Covariance intersection Covariance computer.software_genre Education Estimation of covariance matrices 0504 sociology Developmental Neuroscience Sample size determination Developmental and Educational Psychology Econometrics Statistical inference Rational quadratic covariance function Data mining Life-span and Life-course Studies 0503 education computer Social Sciences (miscellaneous) Mathematics |
| Zdroj: | International Journal of Behavioral Development. 41:444-455 |
| ISSN: | 1464-0651 0165-0254 |
| DOI: | 10.1177/0165025416647800 |
| Popis: | It has been pointed out in the literature that misspecification of the level-1 error covariance structure in latent growth modeling (LGM) has detrimental impacts on the inferences about growth parameters. Since correct covariance structure is difficult to specify by theory, the identification needs to rely on a specification search, which, however, is not systematically addressed in the literature. In this study, we first discuss characteristics of various covariance structures and their nested relations, based on which we then propose a systematic approach to facilitate identifying a plausible covariance structure. A test for stationarity of an error process and the sequential chi-square difference test are conducted in the approach. Preliminary simulation results indicate that the approach performs well when sample size is large enough. The approach is illustrated with empirical data. We recommend that the approach be used in LGM empirical studies to improve the quality of the specification of the error covariance structure. |
| Databáze: | OpenAIRE |
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