Home > Research > Publications & Outputs > Using multilevel regression mixture models to i...

Electronic data

  • Multilevel Regression Mixtures_Resubmit

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Structural Equation Modeling on 28/08/2015, available online: http://wwww.tandfonline.com 10.1080/10705511.2015.1035437

    Accepted author manuscript, 666 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects

Research output: Contribution to journalJournal articlepeer-review

Published

Standard

Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects. / Van Horn, M. Lee; Feng, Y.; Kim, Minjung; Lamont, Andrea E.; Feaster, Daniel; Jaki, Thomas.

In: Structural Equation Modeling, Vol. 23, No. 2, 2016, p. 259-269.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Van Horn, ML, Feng, Y, Kim, M, Lamont, AE, Feaster, D & Jaki, T 2016, 'Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects', Structural Equation Modeling, vol. 23, no. 2, pp. 259-269. https://doi.org/10.1080/10705511.2015.1035437

APA

Van Horn, M. L., Feng, Y., Kim, M., Lamont, A. E., Feaster, D., & Jaki, T. (2016). Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects. Structural Equation Modeling, 23(2), 259-269. https://doi.org/10.1080/10705511.2015.1035437

Vancouver

Van Horn ML, Feng Y, Kim M, Lamont AE, Feaster D, Jaki T. Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects. Structural Equation Modeling. 2016;23(2):259-269. https://doi.org/10.1080/10705511.2015.1035437

Author

Van Horn, M. Lee ; Feng, Y. ; Kim, Minjung ; Lamont, Andrea E. ; Feaster, Daniel ; Jaki, Thomas. / Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects. In: Structural Equation Modeling. 2016 ; Vol. 23, No. 2. pp. 259-269.

Bibtex

@article{c557592e62cf4faaa5e4f16b88b20c6f,
title = "Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects",
abstract = "This article proposes a novel exploratory approach for assessing how the effects of Level-2 predictors differ across Level-1 units. Multilevel regression mixture models are used to identify latent classes at Level 1 that differ in the effect of 1 or more Level-2 predictors. Monte Carlo simulations are used to demonstrate the approach with different sample sizes and to demonstrate the consequences of constraining 1 of the random effects to 0. An application of the method to evaluate heterogeneity in the effects of classroom practices on students is used to show the types of research questions that can be answered with this method and the issues faced when estimating multilevel regression mixtures. ",
keywords = "heterogeneity in contextual effects, multilevel regression mixtures, regression mixture modeling",
author = "{Van Horn}, {M. Lee} and Y. Feng and Minjung Kim and Lamont, {Andrea E.} and Daniel Feaster and Thomas Jaki",
note = "This is an Accepted Manuscript of an article published by Taylor & Francis in Structural Equation Modeling on 28/08/2015, available online: http://wwww.tandfonline.com 10.1080/10705511.2015.1035437",
year = "2016",
doi = "10.1080/10705511.2015.1035437",
language = "English",
volume = "23",
pages = "259--269",
journal = "Structural Equation Modeling",
issn = "1070-5511",
publisher = "Psychology Press Ltd",
number = "2",

}

RIS

TY - JOUR

T1 - Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects

AU - Van Horn, M. Lee

AU - Feng, Y.

AU - Kim, Minjung

AU - Lamont, Andrea E.

AU - Feaster, Daniel

AU - Jaki, Thomas

N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Structural Equation Modeling on 28/08/2015, available online: http://wwww.tandfonline.com 10.1080/10705511.2015.1035437

PY - 2016

Y1 - 2016

N2 - This article proposes a novel exploratory approach for assessing how the effects of Level-2 predictors differ across Level-1 units. Multilevel regression mixture models are used to identify latent classes at Level 1 that differ in the effect of 1 or more Level-2 predictors. Monte Carlo simulations are used to demonstrate the approach with different sample sizes and to demonstrate the consequences of constraining 1 of the random effects to 0. An application of the method to evaluate heterogeneity in the effects of classroom practices on students is used to show the types of research questions that can be answered with this method and the issues faced when estimating multilevel regression mixtures.

AB - This article proposes a novel exploratory approach for assessing how the effects of Level-2 predictors differ across Level-1 units. Multilevel regression mixture models are used to identify latent classes at Level 1 that differ in the effect of 1 or more Level-2 predictors. Monte Carlo simulations are used to demonstrate the approach with different sample sizes and to demonstrate the consequences of constraining 1 of the random effects to 0. An application of the method to evaluate heterogeneity in the effects of classroom practices on students is used to show the types of research questions that can be answered with this method and the issues faced when estimating multilevel regression mixtures.

KW - heterogeneity in contextual effects

KW - multilevel regression mixtures

KW - regression mixture modeling

U2 - 10.1080/10705511.2015.1035437

DO - 10.1080/10705511.2015.1035437

M3 - Journal article

VL - 23

SP - 259

EP - 269

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

IS - 2

ER -