Incorporating Machine Learning Into Factor Mixture Modeling: Identification of Covariate Interactions to Explain Population Heterogeneity
Factor mixture modeling (FMM) has been widely adopted in health and behavioral sciences to examine unobserved population heterogeneity. Covariates are often included in FMM as predictors of the latent class membership via multinomial logistic regression to help understand the formation and character...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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PsychOpen GOLD/ Leibniz Institute for Psychology
2023-09-01
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| Series: | Methodology |
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| Online Access: | https://doi.org/10.5964/meth.9487 |
| _version_ | 1827355637587116032 |
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| author | Yan Wang Tonghui Xu Jiabin Shen |
| author_facet | Yan Wang Tonghui Xu Jiabin Shen |
| author_sort | Yan Wang |
| collection | DOAJ |
| description | Factor mixture modeling (FMM) has been widely adopted in health and behavioral sciences to examine unobserved population heterogeneity. Covariates are often included in FMM as predictors of the latent class membership via multinomial logistic regression to help understand the formation and characterization of population heterogeneity. However, interaction effects among covariates have received considerably less attention, which might be attributable to the fact that interaction effects cannot be identified in a straightforward fashion. This study demonstrated the utility of structural equation model or SEM trees as an exploratory method to automatically search for covariate interactions that might explain heterogeneity in FMM. That is, following FMM analyses, SEM trees are conducted to identify covariate interactions. Next, latent class membership is regressed on the covariate interactions as well as all main effects of covariates. This approach was demonstrated using the Traumatic Brain Injury Model System National Database. |
| first_indexed | 2024-03-08T04:46:09Z |
| format | Article |
| id | doaj.art-c4eee871604843f59eaed764d5892b9f |
| institution | Directory Open Access Journal |
| issn | 1614-2241 |
| language | English |
| last_indexed | 2024-03-08T04:46:09Z |
| publishDate | 2023-09-01 |
| publisher | PsychOpen GOLD/ Leibniz Institute for Psychology |
| record_format | Article |
| series | Methodology |
| spelling | doaj.art-c4eee871604843f59eaed764d5892b9f2024-02-08T10:51:09ZengPsychOpen GOLD/ Leibniz Institute for PsychologyMethodology1614-22412023-09-0119330332210.5964/meth.9487meth.9487Incorporating Machine Learning Into Factor Mixture Modeling: Identification of Covariate Interactions to Explain Population HeterogeneityYan Wang0https://orcid.org/0000-0003-2237-8816Tonghui Xu1Jiabin Shen2https://orcid.org/0000-0001-6625-5215Department of Psychology, University of Massachusetts Lowell, Lowell, MA, USASchool of Education, University of Massachusetts Lowell, Lowell, MA, USADepartment of Psychology, University of Massachusetts Lowell, Lowell, MA, USAFactor mixture modeling (FMM) has been widely adopted in health and behavioral sciences to examine unobserved population heterogeneity. Covariates are often included in FMM as predictors of the latent class membership via multinomial logistic regression to help understand the formation and characterization of population heterogeneity. However, interaction effects among covariates have received considerably less attention, which might be attributable to the fact that interaction effects cannot be identified in a straightforward fashion. This study demonstrated the utility of structural equation model or SEM trees as an exploratory method to automatically search for covariate interactions that might explain heterogeneity in FMM. That is, following FMM analyses, SEM trees are conducted to identify covariate interactions. Next, latent class membership is regressed on the covariate interactions as well as all main effects of covariates. This approach was demonstrated using the Traumatic Brain Injury Model System National Database.https://doi.org/10.5964/meth.9487factor mixture modellatent classmachine learningstructural equation model treescovariateinteraction |
| spellingShingle | Yan Wang Tonghui Xu Jiabin Shen Incorporating Machine Learning Into Factor Mixture Modeling: Identification of Covariate Interactions to Explain Population Heterogeneity Methodology factor mixture model latent class machine learning structural equation model trees covariate interaction |
| title | Incorporating Machine Learning Into Factor Mixture Modeling: Identification of Covariate Interactions to Explain Population Heterogeneity |
| title_full | Incorporating Machine Learning Into Factor Mixture Modeling: Identification of Covariate Interactions to Explain Population Heterogeneity |
| title_fullStr | Incorporating Machine Learning Into Factor Mixture Modeling: Identification of Covariate Interactions to Explain Population Heterogeneity |
| title_full_unstemmed | Incorporating Machine Learning Into Factor Mixture Modeling: Identification of Covariate Interactions to Explain Population Heterogeneity |
| title_short | Incorporating Machine Learning Into Factor Mixture Modeling: Identification of Covariate Interactions to Explain Population Heterogeneity |
| title_sort | incorporating machine learning into factor mixture modeling identification of covariate interactions to explain population heterogeneity |
| topic | factor mixture model latent class machine learning structural equation model trees covariate interaction |
| url | https://doi.org/10.5964/meth.9487 |
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