Dataset Information

Combined Approach to Multi-Informant Data Using Latent Factors and Latent Classes: Trifactor Mixture Model.

ABSTRACT: Although collecting data from multiple informants is highly recommended, methods to model the congruence and incongruence between informants are limited. Bauer and colleagues suggested the trifactor model that decomposes the variances into common factor, informant perspective factors, and item-specific factors. This study extends their work to the trifactor mixture model that combines the trifactor model and the mixture model. This combined approach allows researchers to investigate the common and unique perspectives of multiple informants on targets using latent factors and simultaneously take into account potential heterogeneity of targets using latent classes. We demonstrate this model using student self-rated and teacher-rated academic behaviors (N = 24,094). Model specification and testing procedures are explicated in detail. Methodological and practical issues in conducting the trifactor mixture analysis are discussed.

SUBMITTER: Kim E

PROVIDER: S-EPMC8243203 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Combined Approach to Multi-Informant Data Using Latent Factors and Latent Classes: Trifactor Mixture Model.

Kim Eunsook E von der Embse Nathaniel N

Educational and psychological measurement 20201127 4

Although collecting data from multiple informants is highly recommended, methods to model the congruence and incongruence between informants are limited. Bauer and colleagues suggested the trifactor model that decomposes the variances into common factor, informant perspective factors, and item-specific factors. This study extends their work to the trifactor mixture model that combines the trifactor model and the mixture model. This combined approach allows researchers to investigate the common a ...[more]

PMID: 34267398

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Project description:MotivationThe statistical analysis of single-cell data is a challenge in cell biological studies. Tailored statistical models and computational methods are required to resolve the subpopulation structure, i.e. to correctly identify and characterize subpopulations. These approaches also support the unraveling of sources of cell-to-cell variability. Finite mixture models have shown promise, but the available approaches are ill suited to the simultaneous consideration of data from multiple experimental conditions and to censored data. The prevalence and relevance of single-cell data and the lack of suitable computational analytics make automated methods, that are able to deal with the requirements posed by these data, necessary.ResultsWe present MEMO, a flexible mixture modeling framework that enables the simultaneous, automated analysis of censored and uncensored data acquired under multiple experimental conditions. MEMO is based on maximum-likelihood inference and allows for testing competing hypotheses. MEMO can be applied to a variety of different single-cell data types. We demonstrate the advantages of MEMO by analyzing right and interval censored single-cell microscopy data. Our results show that an examination of censoring and the simultaneous consideration of different experimental conditions are necessary to reveal biologically meaningful subpopulation structures. MEMO allows for a stringent analysis of single-cell data and enables researchers to avoid misinterpretation of censored data. Therefore, MEMO is a valuable asset for all fields that infer the characteristics of populations by looking at single individuals such as cell biology and medicine.Availability and implementationMEMO is implemented in MATLAB and freely available via github (https://github.com/MEMO-toolbox/MEMO).Contactseva-maria.geissen@ist.uni-stuttgart.de or nicole.radde@ist.uni-stuttgart.deSupplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Combined Approach to Multi-Informant Data Using Latent Factors and Latent Classes: Trifactor Mixture Model.

Publications

Combined Approach to Multi-Informant Data Using Latent Factors and Latent Classes: Trifactor Mixture Model.

Similar Datasets

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