### Abstract

A 2-part latent growth model can be used to analyze semicontinuous data to simultaneously study change in the probability that an individual engages in a behavior, and if engaged, change in the behavior. This article uses a Monte Carlo (MC) integration algorithm to study the interrelationships between the growth factors of 2 variables measured longitudinally where each variable can follow a 2-part latent growth model. A SAS macro implementing Mplus is developed to estimate the model to take into account the sampling uncertainty of this simulation-based computational approach. A sample of time-use data is used to show how maximum likelihood estimates can be obtained using a rectangular numerical integration method and an MC integration method.

Original language | English (US) |
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Pages (from-to) | 131-148 |

Number of pages | 18 |

Journal | Structural Equation Modeling |

Volume | 21 |

Issue number | 1 |

DOIs | |

State | Published - Jan 1 2014 |

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### Keywords

- longitudinal semicontinuous variables
- Monte Carlo integration
- multivariate two-part latent growth curve model

### ASJC Scopus subject areas

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

### Cite this

*Structural Equation Modeling*,

*21*(1), 131-148. https://doi.org/10.1080/10705511.2014.856699

**On Fitting a Multivariate Two-Part Latent Growth Model.** / Xu, Violet Shu; Blozis, Shelley A.; Vandewater, Elizabeth A.

Research output: Contribution to journal › Article

*Structural Equation Modeling*, vol. 21, no. 1, pp. 131-148. https://doi.org/10.1080/10705511.2014.856699

}

TY - JOUR

T1 - On Fitting a Multivariate Two-Part Latent Growth Model

AU - Xu, Violet Shu

AU - Blozis, Shelley A.

AU - Vandewater, Elizabeth A.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - A 2-part latent growth model can be used to analyze semicontinuous data to simultaneously study change in the probability that an individual engages in a behavior, and if engaged, change in the behavior. This article uses a Monte Carlo (MC) integration algorithm to study the interrelationships between the growth factors of 2 variables measured longitudinally where each variable can follow a 2-part latent growth model. A SAS macro implementing Mplus is developed to estimate the model to take into account the sampling uncertainty of this simulation-based computational approach. A sample of time-use data is used to show how maximum likelihood estimates can be obtained using a rectangular numerical integration method and an MC integration method.

AB - A 2-part latent growth model can be used to analyze semicontinuous data to simultaneously study change in the probability that an individual engages in a behavior, and if engaged, change in the behavior. This article uses a Monte Carlo (MC) integration algorithm to study the interrelationships between the growth factors of 2 variables measured longitudinally where each variable can follow a 2-part latent growth model. A SAS macro implementing Mplus is developed to estimate the model to take into account the sampling uncertainty of this simulation-based computational approach. A sample of time-use data is used to show how maximum likelihood estimates can be obtained using a rectangular numerical integration method and an MC integration method.

KW - longitudinal semicontinuous variables

KW - Monte Carlo integration

KW - multivariate two-part latent growth curve model

UR - http://www.scopus.com/inward/record.url?scp=84893652277&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893652277&partnerID=8YFLogxK

U2 - 10.1080/10705511.2014.856699

DO - 10.1080/10705511.2014.856699

M3 - Article

AN - SCOPUS:84893652277

VL - 21

SP - 131

EP - 148

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

IS - 1

ER -