### Abstract

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

Pages (from-to) | 506-523 |

Journal | Structural Equation Modeling |

Volume | 24 |

Issue number | 4 |

DOIs | |

Publication status | Published - 6 Mar 2017 |

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*Structural Equation Modeling*,

*24*(4), 506-523. https://doi.org/10.1080/10705511.2017.1278604

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*Structural Equation Modeling*, vol. 24, no. 4, pp. 506-523. https://doi.org/10.1080/10705511.2017.1278604

**Mixture simultaneous factor analysis for capturing differences in latent variables between higher level units of multilevel data.** / De Roover, K.; Vermunt, J.K.; Timmerman, Marieke E.; Ceulemans, Eva.

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

TY - JOUR

T1 - Mixture simultaneous factor analysis for capturing differences in latent variables between higher level units of multilevel data

AU - De Roover, K.

AU - Vermunt, J.K.

AU - Timmerman, Marieke E.

AU - Ceulemans, Eva

PY - 2017/3/6

Y1 - 2017/3/6

N2 - Given multivariate data, many research questions pertain to the covariance structure: whether and how the variables (for example, personality measures) covary. Exploratory factor analysis (EFA) is often used to look for latent variables that may explain the covariances among variables; for example, the Big Five personality structure. In case of multilevel data, one may wonder whether or not the same covariance (factor) structure holds for each so-called ‘data block’ (containing data of one higher-level unit). For instance, is the Big Five personality structure found in each country or do cross-cultural differences exist? The well-known multigroup EFA framework falls short in answering such questions, especially for numerous groups/blocks. We introduce mixture simultaneous factor analysis (MSFA), performing a mixture model clustering of data blocks, based on their factor structure. A simulation study shows excellent results with respect to parameter recovery and an empirical example is included to illustrate the value of MSFA.

AB - Given multivariate data, many research questions pertain to the covariance structure: whether and how the variables (for example, personality measures) covary. Exploratory factor analysis (EFA) is often used to look for latent variables that may explain the covariances among variables; for example, the Big Five personality structure. In case of multilevel data, one may wonder whether or not the same covariance (factor) structure holds for each so-called ‘data block’ (containing data of one higher-level unit). For instance, is the Big Five personality structure found in each country or do cross-cultural differences exist? The well-known multigroup EFA framework falls short in answering such questions, especially for numerous groups/blocks. We introduce mixture simultaneous factor analysis (MSFA), performing a mixture model clustering of data blocks, based on their factor structure. A simulation study shows excellent results with respect to parameter recovery and an empirical example is included to illustrate the value of MSFA.

U2 - 10.1080/10705511.2017.1278604

DO - 10.1080/10705511.2017.1278604

M3 - Article

VL - 24

SP - 506

EP - 523

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

IS - 4

ER -