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    Rights statement: The final, definitive version of this article has been published in the Journal, Educational and Psychological Measurement, 79 (2), 2019, © SAGE Publications Ltd, 2019 by SAGE Publications Ltd at the Educational and Psychological Measurement page: https://journals.sagepub.com/home/epm on SAGE Journals Online: http://online.sagepub.com/

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The effects of sample size on the estimation of regression mixture models

Research output: Contribution to journalJournal article

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The effects of sample size on the estimation of regression mixture models. / Jaki, Thomas Friedrich; Kim, Minjung; Lamont, Andrea E.; George, M.; Feaster, Daniel; Van Horn, M. Lee.

In: Educational and Psychological Measurement, Vol. 79, No. 2, 01.04.2019, p. 358-384.

Research output: Contribution to journalJournal article

Harvard

Jaki, TF, Kim, M, Lamont, AE, George, M, Feaster, D & Van Horn, ML 2019, 'The effects of sample size on the estimation of regression mixture models', Educational and Psychological Measurement, vol. 79, no. 2, pp. 358-384. https://doi.org/10.1177/0013164418791673

APA

Jaki, T. F., Kim, M., Lamont, A. E., George, M., Feaster, D., & Van Horn, M. L. (2019). The effects of sample size on the estimation of regression mixture models. Educational and Psychological Measurement, 79(2), 358-384. https://doi.org/10.1177/0013164418791673

Vancouver

Jaki TF, Kim M, Lamont AE, George M, Feaster D, Van Horn ML. The effects of sample size on the estimation of regression mixture models. Educational and Psychological Measurement. 2019 Apr 1;79(2):358-384. https://doi.org/10.1177/0013164418791673

Author

Jaki, Thomas Friedrich ; Kim, Minjung ; Lamont, Andrea E. ; George, M. ; Feaster, Daniel ; Van Horn, M. Lee. / The effects of sample size on the estimation of regression mixture models. In: Educational and Psychological Measurement. 2019 ; Vol. 79, No. 2. pp. 358-384.

Bibtex

@article{d81e8de8432147f1b6442a16a12c3ae1,
title = "The effects of sample size on the estimation of regression mixture models",
abstract = "Regression mixture models are a statistical approach used for estimating heterogeneity in effects. This study investigates the impact of sample size on regression mixture{\textquoteright}s ability to produce “stable” results. Monte Carlo simulations and analysis of resamples from an application data set were used to illustrate the types of problems that may occur with small samples in real data sets. The results suggest that (a) when class separation is low, very large sample sizes may be needed to obtain stable results; (b) it may often be necessary to consider a preponderance of evidence in latent class enumeration; (c) regression mixtures with ordinal outcomes result in even more instability; and (d) with small samples, it is possible to obtain spurious results without any clear indication of there being a problem.",
author = "Jaki, {Thomas Friedrich} and Minjung Kim and Lamont, {Andrea E.} and M. George and Daniel Feaster and {Van Horn}, {M. Lee}",
note = "The final, definitive version of this article has been published in the Journal, Educational and Psychological Measurement, 79 (2), 2019, {\textcopyright} SAGE Publications Ltd, 2019 by SAGE Publications Ltd at the Educational and Psychological Measurement page: https://journals.sagepub.com/home/epm on SAGE Journals Online: http://online.sagepub.com/",
year = "2019",
month = apr
day = "1",
doi = "10.1177/0013164418791673",
language = "English",
volume = "79",
pages = "358--384",
journal = "Educational and Psychological Measurement",
issn = "0013-1644",
publisher = "SAGE Publications Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - The effects of sample size on the estimation of regression mixture models

AU - Jaki, Thomas Friedrich

AU - Kim, Minjung

AU - Lamont, Andrea E.

AU - George, M.

AU - Feaster, Daniel

AU - Van Horn, M. Lee

N1 - The final, definitive version of this article has been published in the Journal, Educational and Psychological Measurement, 79 (2), 2019, © SAGE Publications Ltd, 2019 by SAGE Publications Ltd at the Educational and Psychological Measurement page: https://journals.sagepub.com/home/epm on SAGE Journals Online: http://online.sagepub.com/

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Regression mixture models are a statistical approach used for estimating heterogeneity in effects. This study investigates the impact of sample size on regression mixture’s ability to produce “stable” results. Monte Carlo simulations and analysis of resamples from an application data set were used to illustrate the types of problems that may occur with small samples in real data sets. The results suggest that (a) when class separation is low, very large sample sizes may be needed to obtain stable results; (b) it may often be necessary to consider a preponderance of evidence in latent class enumeration; (c) regression mixtures with ordinal outcomes result in even more instability; and (d) with small samples, it is possible to obtain spurious results without any clear indication of there being a problem.

AB - Regression mixture models are a statistical approach used for estimating heterogeneity in effects. This study investigates the impact of sample size on regression mixture’s ability to produce “stable” results. Monte Carlo simulations and analysis of resamples from an application data set were used to illustrate the types of problems that may occur with small samples in real data sets. The results suggest that (a) when class separation is low, very large sample sizes may be needed to obtain stable results; (b) it may often be necessary to consider a preponderance of evidence in latent class enumeration; (c) regression mixtures with ordinal outcomes result in even more instability; and (d) with small samples, it is possible to obtain spurious results without any clear indication of there being a problem.

U2 - 10.1177/0013164418791673

DO - 10.1177/0013164418791673

M3 - Journal article

VL - 79

SP - 358

EP - 384

JO - Educational and Psychological Measurement

JF - Educational and Psychological Measurement

SN - 0013-1644

IS - 2

ER -