### Abstract

Structural equation model trees (SEM Trees) allow for the construction of decision trees with structural equation models fit in each of the nodes. Based on covariate information, SEM Trees can be used to create distinct subgroups containing individuals with similar parameter estimates. Currently, the structural equation modeling component of SEM Trees is implemented in the R packages OpenMx and lavaan. We extend SEM Trees so that the models can be fit in Mplus, in the hopes that its efficiency and accessibility allow a broader group of researchers to fit a wider range of models. We discuss the Mplus Trees algorithm, its implementation, and its position among the growing number of tree-based methods in psychological research. We also provide several examples using publicly available data to illustrate how Mplus Trees can be implemented in practice with the R package MplusTrees.

Original language | English (US) |
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Journal | Structural Equation Modeling |

DOIs | |

State | Accepted/In press - Jan 1 2020 |

Externally published | Yes |

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### Keywords

- data mining
- decision trees
- machine learning
- Structural equation modeling

### ASJC Scopus subject areas

- Decision Sciences(all)
- Modeling and Simulation
- Sociology and Political Science
- Economics, Econometrics and Finance(all)

### Cite this

*Structural Equation Modeling*. https://doi.org/10.1080/10705511.2020.1726179