Literaturnachweis - Detailanzeige
| Autor/inn/en | Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne |
|---|---|
| Titel | Nonlinear Structured Growth Mixture Models in M"plus" and OpenMx |
| Quelle | In: Multivariate Behavioral Research, 45 (2010) 6, S. 887-909
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| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 0027-3171 |
| Schlagwörter | Forschungsbericht; Models; Computer Software; Programming; Statistical Analysis; Longitudinal Studies; Reading; Child Development; Early Childhood Longitudinal Survey |
| Abstract | Growth mixture models (GMMs; B. O. Muthen & Muthen, 2000; B. O. Muthen & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models because of their common use, flexibility in modeling many types of change patterns, the availability of statistical programs to fit such models, and the ease of programming. In this article, we present additional ways of modeling nonlinear change patterns with GMMs. Specifically, we show how LCMs that follow specific nonlinear functions can be extended to examine the presence of multiple latent classes using the M"plus" and OpenMx computer programs. These models are fit to longitudinal reading data from the Early Childhood Longitudinal Study-Kindergarten Cohort to illustrate their use. (Contains 2 figures, 2 tables, and 1 footnote.) (As Provided). |
| Anmerkungen | Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals |
| Begutachtung | Peer reviewed |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2017/4/10 |
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