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Evaluating differential effects using regression interactions and regression mixture models

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Evaluating differential effects using regression interactions and regression mixture models. / Van Horn, M. Lee; Jaki, Thomas; Maysn, Katherine; Howe, George; Feaster, Daniel; Lamont, Andrea E.; George, Melissa; Kim, Minjung.

In: Educational and Psychological Measurement, Vol. 75, No. 4, 08.2015, p. 677-714.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Van Horn, ML, Jaki, T, Maysn, K, Howe, G, Feaster, D, Lamont, AE, George, M & Kim, M 2015, 'Evaluating differential effects using regression interactions and regression mixture models', Educational and Psychological Measurement, vol. 75, no. 4, pp. 677-714. https://doi.org/10.1177/0013164414554931

APA

Van Horn, M. L., Jaki, T., Maysn, K., Howe, G., Feaster, D., Lamont, A. E., George, M., & Kim, M. (2015). Evaluating differential effects using regression interactions and regression mixture models. Educational and Psychological Measurement, 75(4), 677-714. https://doi.org/10.1177/0013164414554931

Vancouver

Van Horn ML, Jaki T, Maysn K, Howe G, Feaster D, Lamont AE et al. Evaluating differential effects using regression interactions and regression mixture models. Educational and Psychological Measurement. 2015 Aug;75(4):677-714. https://doi.org/10.1177/0013164414554931

Author

Van Horn, M. Lee ; Jaki, Thomas ; Maysn, Katherine ; Howe, George ; Feaster, Daniel ; Lamont, Andrea E. ; George, Melissa ; Kim, Minjung. / Evaluating differential effects using regression interactions and regression mixture models. In: Educational and Psychological Measurement. 2015 ; Vol. 75, No. 4. pp. 677-714.

Bibtex

@article{26dca0d360db49ffb19637cfae1087c2,
title = "Evaluating differential effects using regression interactions and regression mixture models",
abstract = "Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The article aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design.",
keywords = "statistical interactions, differential effects, regression mixture models, finite mixture models",
author = "{Van Horn}, {M. Lee} and Thomas Jaki and Katherine Maysn and George Howe and Daniel Feaster and Lamont, {Andrea E.} and Melissa George and Minjung Kim",
year = "2015",
month = aug,
doi = "10.1177/0013164414554931",
language = "English",
volume = "75",
pages = "677--714",
journal = "Educational and Psychological Measurement",
issn = "0013-1644",
publisher = "SAGE Publications Inc.",
number = "4",

}

RIS

TY - JOUR

T1 - Evaluating differential effects using regression interactions and regression mixture models

AU - Van Horn, M. Lee

AU - Jaki, Thomas

AU - Maysn, Katherine

AU - Howe, George

AU - Feaster, Daniel

AU - Lamont, Andrea E.

AU - George, Melissa

AU - Kim, Minjung

PY - 2015/8

Y1 - 2015/8

N2 - Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The article aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design.

AB - Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The article aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design.

KW - statistical interactions

KW - differential effects

KW - regression mixture models

KW - finite mixture models

U2 - 10.1177/0013164414554931

DO - 10.1177/0013164414554931

M3 - Journal article

VL - 75

SP - 677

EP - 714

JO - Educational and Psychological Measurement

JF - Educational and Psychological Measurement

SN - 0013-1644

IS - 4

ER -