Fitting Structural Equation Model Trees and Latent Growth Curve Mixture Models in Longitudinal Designs: The Influence of Model Misspecification
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- Satoshi Usami
- University of Tsukuba and University of Reading
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- Timothy Hayes
- University of Southern California
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- John McArdle
- University of Southern California
書誌事項
- 公開日
- 2017-01-31
- 資源種別
- journal article
- DOI
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- 10.1080/10705511.2016.1266267
- 公開者
- Informa UK Limited
この論文をさがす
説明
When conducting longitudinal research, the investigation of between-individual differences in patterns of within-individual change can provide important insights. In this article, we use simulation methods to investigate the performance of a model-based exploratory data mining technique—structural equation model trees (SEM trees; Brandmaier, Oertzen, McArdle, & Lindenberger, 2013)—as a tool for detecting population heterogeneity. We use a latent-change score model as a data generation model and manipulate the precision of the information provided by a covariate about the true latent profile as well as other factors, including sample size, under the possible influences of model misspecifications. Simulation results show that, compared with latent growth curve mixture models, SEM trees might be very sensitive to model misspecification in estimating the number of classes. This can be attributed to the lower statistical power in identifying classes, resulting from smaller differences of parameters prescribed ...
収録刊行物
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- Structural Equation Modeling: A Multidisciplinary Journal
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Structural Equation Modeling: A Multidisciplinary Journal 24 (4), 585-598, 2017-01-31
Informa UK Limited
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詳細情報 詳細情報について
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- CRID
- 1360848659152201216
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- ISSN
- 15328007
- 10705511
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- 資料種別
- journal article
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- データソース種別
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- Crossref
- KAKEN
- OpenAIRE
