### Abstract

Original language | English |
---|---|

Pages (from-to) | 139-154 |

Journal | Advances in Data Analysis and Classification |

Volume | 10 |

Issue number | 2 |

DOIs | |

Publication status | Published - 2016 |

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*Advances in Data Analysis and Classification*,

*10*(2), 139-154. https://doi.org/10.1007/s11634-016-0234-1

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*Advances in Data Analysis and Classification*, vol. 10, no. 2, pp. 139-154. https://doi.org/10.1007/s11634-016-0234-1

**Micro-macro multilevel latent class models with multiple discrete individual-level variables.** / Bennink, M.; Croon, M.A.; Kroon, B.; Vermunt, J.K.

Research output: Contribution to journal › Article › Scientific › peer-review

TY - JOUR

T1 - Micro-macro multilevel latent class models with multiple discrete individual-level variables

AU - Bennink, M.

AU - Croon, M.A.

AU - Kroon, B.

AU - Vermunt, J.K.

PY - 2016

Y1 - 2016

N2 - An existing micro-macro method for a single individual-level variable is extended to the multivariate situation by presenting two multilevel latent class models in which multiple discrete individual-level variables are used to explain a group-level outcome. As in the univariate case, the individual-level data are summarized at the group-level by constructing a discrete latent variable at the group level and this group-level latent variable is used as a predictor for the group-level outcome. In the first extension, that is referred to as the Direct model, the multiple individual-level variables are directly used as indicators for the group-level latent variable. In the second extension, referred to as the Indirect model, the multiple individual-level variables are used to construct an individual-level latent variable that is used as an indicator for the group-level latent variable. This implies that the individual-level variables are used indirectly at the group-level. The within- and between components of the (co)varn the individual-level variables are independent in the Direct model, but dependent in the Indirect model. Both models are discussed and illustrated with an empirical data example.

AB - An existing micro-macro method for a single individual-level variable is extended to the multivariate situation by presenting two multilevel latent class models in which multiple discrete individual-level variables are used to explain a group-level outcome. As in the univariate case, the individual-level data are summarized at the group-level by constructing a discrete latent variable at the group level and this group-level latent variable is used as a predictor for the group-level outcome. In the first extension, that is referred to as the Direct model, the multiple individual-level variables are directly used as indicators for the group-level latent variable. In the second extension, referred to as the Indirect model, the multiple individual-level variables are used to construct an individual-level latent variable that is used as an indicator for the group-level latent variable. This implies that the individual-level variables are used indirectly at the group-level. The within- and between components of the (co)varn the individual-level variables are independent in the Direct model, but dependent in the Indirect model. Both models are discussed and illustrated with an empirical data example.

U2 - 10.1007/s11634-016-0234-1

DO - 10.1007/s11634-016-0234-1

M3 - Article

VL - 10

SP - 139

EP - 154

JO - Advances in Data Analysis and Classification

JF - Advances in Data Analysis and Classification

SN - 1862-5347

IS - 2

ER -