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dc.contributor.authorBRANDMAIER, Andreas M.
dc.contributor.authorPRINDLE, John J.
dc.contributor.authorMCARDLE, John J.
dc.contributor.authorLINDENBERGER, Ulman 
dc.date.accessioned2019-03-01T14:53:17Z
dc.date.available2019-03-01T14:53:17Z
dc.date.issued2016
dc.identifier.citationPsychological methods, 2016, Vol. 21, No. 4, pp. 566-582
dc.identifier.issn1082-989X
dc.identifier.issn1939-1463en
dc.identifier.urihttp://hdl.handle.net/1814/61450
dc.description.abstractStructural equation model (SEM) trees, a combination of SEMs and decision trees, have been proposed as a data-analytic tool for theory-guided exploration of empirical data. With respect to a hypothesized model of multivariate outcomes, such trees recursively find subgroups with similar patterns of observed data. SEM trees allow for the automatic selection of variables that predict differences across individuals in specific theoretical models, for instance, differences in latent factor profiles or developmental trajectories. However, SEM trees are unstable when small variations in the data can result in different trees. As a remedy, SEM forests, which are ensembles of SEM trees based on resamplings of the original dataset, provide increased stability. Because large forests are less suitable for visual inspection and interpretation, aggregate measures provide researchers with hints on how to improve their models: (a) variable importance is based on random permutations of the out-of-bag (OOB) samples of the individual trees and quantifies, for each variable, the average reduction of uncertainty about the model-predicted distribution
dc.description.abstractand (b) case proximity enables researchers to perform clustering and outlier detection. We provide an overview of SEM forests and illustrate their utility in the context of cross-sectional factor models of intelligence and episodic memory. We discuss benefits and limitations, and provide advice on how and when to use SEM trees and forests in future research.
dc.language.isoen
dc.publisherAmer Psychological Assoc
dc.relation.ispartofPsychological methods
dc.subjectSEM forest
dc.subjectModel-based tree
dc.subjectRecursive partitioning
dc.subjectVariable importance
dc.subjectCase proximity
dc.subjectEpisodic Memoryen
dc.subjectVariable Importanceen
dc.subjectLongitudinal Dataen
dc.subjectLife-Styleen
dc.subjectClassificationen
dc.subjectValidationen
dc.subjectSelectionen
dc.subjectAdultsen
dc.subjectTreesen
dc.subjectAgeen
dc.titleTheory-guided exploration with structural equation model forests
dc.typeArticle
dc.identifier.doi10.1037/met0000090
dc.identifier.volume21
dc.identifier.startpage566
dc.identifier.endpage582
eui.subscribe.skiptrue
dc.identifier.issue4


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