Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/156180 
Erscheinungsjahr: 
2016
Schriftenreihe/Nr.: 
Working Papers in Economics and Statistics No. 2016-26
Verlag: 
University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon), Innsbruck
Zusammenfassung: 
In multinomial processing tree (MPT) models, individual differences between the participants in a study lead to heterogeneity of the model parameters. While subject covariates may explain these differences, it is often unknown in advance how the parameters depend on the available covariates, that is, which variables play a role at all, interact, or have a nonlinear influence, etc. Therefore, a new approach for capturing parameter heterogeneity in MPT models is proposed based on the machine learning method MOB for model-based recursive partitioning. This recursively partitions the covariate space, leading to an MPT tree with subgroups that are directly interpretable in terms of effects and interactions of the covariates. The pros and cons of MPT trees as a means of analyzing the effects of covariates in MPT model parameters are discussed based on a simulation experiment as well as on two empirical applications from memory research. Software that implements MPT trees is provided via the mpttree function in the psychotree package in R.
Schlagwörter: 
multinomial processing tree
model-based recursive partitioning
parameter heterogeneity
JEL: 
C14
C45
C52
C87
Dokumentart: 
Working Paper

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