scholarly journals Testing Measurement Invariance Across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling

2020 ◽  
pp. 001316442092512
Author(s):  
Yan Wang ◽  
Eunsook Kim ◽  
John M. Ferron ◽  
Robert F. Dedrick ◽  
Tony X. Tan ◽  
...  
Keyword(s):  
Measurement Invariance ◽  
Latent Class ◽  
Mixture Modeling ◽  
Population Heterogeneity ◽  
Covariate Effect ◽  
Invariance Testing ◽  
Covariate Effects ◽  
Factor Mixture Modeling ◽  
The Impact ◽  
Class Variable

Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed.

scholarly journals Investigating Approaches to Estimating Covariate Effects in Growth Mixture Modeling: A Simulation Study

2016 ◽  
Vol 77 (5) ◽  
pp. 766-791 ◽  
Author(s):  
Ming Li ◽  
Jeffrey R. Harring
Keyword(s):  
Latent Class ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Modeling Framework ◽  
Growth Mixture ◽  
Class Separation ◽  
Covariate Effects ◽  
One Step ◽  
The One ◽  
Class Variable

Researchers continue to be interested in efficient, accurate methods of estimating coefficients of covariates in mixture modeling. Including covariates related to the latent class analysis not only may improve the ability of the mixture model to clearly differentiate between subjects but also makes interpretation of latent group membership more meaningful. Very few studies have been conducted that compare the performance of various approaches to estimating covariate effects in mixture modeling, and fewer yet have considered more complicated models such as growth mixture models where the latent class variable is more difficult to identify. A Monte Carlo simulation was conducted to investigate the performance of four estimation approaches: (1) the conventional three-step approach, (2) the one-step maximum likelihood (ML) approach, (3) the pseudo class (PC) approach, and (4) the three-step ML approach in terms of their ability to recover covariate effects in the logistic regression class membership model within a growth mixture modeling framework. Results showed that when class separation was large, the one-step ML approach and the three-step ML approach displayed much less biased covariate effect estimates than either the conventional three-step approach or the PC approach. When class separation was poor, estimation of the relation between the dichotomous covariate and latent class variable was severely affected when the new three-step ML approach was used.

scholarly journals A Finite Mixture Model for Genotype and Environment Interactions: Detecting Latent Population Heterogeneity

2006 ◽  
Vol 9 (3) ◽  
pp. 412-423 ◽  
Author(s):  
Nathan A. Gillespie ◽  
Michael C. Neale
Keyword(s):  
Mixture Model ◽  
Variance Components ◽  
Latent Class ◽  
Latent Class Model ◽  
Latent Trait ◽  
Model Fitting ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Population Heterogeneity ◽  
The Impact

AbstractApproaches such as DeFries-Fulker extremes regression (LaBuda et al., 1986) are commonly used in genetically informative studies to assess whether familial resemblance varies as a function of the scores of pairs of twins. While useful for detecting such effects, formal modeling of differences in variance components as a function of pairs' trait scores is rarely attempted. We therefore present a finite mixture model which specifies that the population consists of latent groups which may differ in (i) their means, and (ii) the relative impact of genetic and environmental factors on within-group variation and covariation. This model may be considered as a special case of a factor mixture model, which combines the features of a latent class model with those of a latent trait model. Various models for the class membership of twin pairs may be employed, including additive genetic, common environment, specific environment or major locus (QTL) factors. Simulation results based on variance components derived from Turkheimer and colleagues (2003), illustrate the impact of factors such as the difference in group means and variance components on the feasibility of correctly estimating the parameters of the mixture model. Model-fitting analyses estimated group heritability as .49, which is significantly greater than heritability for the rest of the population in early childhood. These results suggest that factor mixture modeling is sufficiently robust for detecting heterogeneous populations even when group mean differences are modest.

scholarly journals Advances in Behavioral Genetics Modeling Using Mplus: Applications of Factor Mixture Modeling to Twin Data

2006 ◽  
Vol 9 (3) ◽  
pp. 313-324 ◽  
Author(s):  
Bengt Muthén ◽  
Tihomir Asparouhov ◽  
Irene Rebollo
Keyword(s):  
Latent Variable ◽  
Alcohol Use Disorder ◽  
Latent Class ◽  
Behavioral Genetics ◽  
Mixture Modeling ◽  
Twin Data ◽  
Class Membership ◽  
Twin Analysis ◽  
Factor Mixture Model ◽  
Factor Mixture Modeling

AbstractThis article discusses new latent variable techniques developed by the authors. As an illustration, a new factor mixture model is applied to the monozygotic–dizygotic twin analysis of binary items measuring alcohol-use disorder. In this model, heritability is simultaneously studied with respect to latent class membership and within-class severity dimensions. Different latent classes of individuals are allowed to have different heritability for the severity dimensions. The factor mixture approach appears to have great potential for the genetic analyses of heterogeneous populations. Generalizations for longitudinal data are also outlined.

Latent variable mixture modeling in psychiatric research – a review and application

2015 ◽  
Vol 46 (3) ◽  
pp. 457-467 ◽  
Author(s):  
J. Miettunen ◽  
T. Nordström ◽  
M. Kaakinen ◽  
A. O. Ahmed
Keyword(s):  
Latent Class Analysis ◽  
Mixture Models ◽  
Latent Variable ◽  
Latent Class ◽  
Mixture Modeling ◽  
Population Heterogeneity ◽  
Psychiatric Research ◽  
Class Analysis ◽  
Psychological Phenomena ◽  
Latent Variable Mixture Models

Latent variable mixture modeling represents a flexible approach to investigating population heterogeneity by sorting cases into latent but non-arbitrary subgroups that are more homogeneous. The purpose of this selective review is to provide a non-technical introduction to mixture modeling in a cross-sectional context. Latent class analysis is used to classify individuals into homogeneous subgroups (latent classes). Factor mixture modeling represents a newer approach that represents a fusion of latent class analysis and factor analysis. Factor mixture models are adaptable to representing categorical and dimensional states of affairs. This article provides an overview of latent variable mixture models and illustrates the application of these methods by applying them to the study of the latent structure of psychotic experiences. The flexibility of latent variable mixture models makes them adaptable to the study of heterogeneity in complex psychiatric and psychological phenomena. They also allow researchers to address research questions that directly compare the viability of dimensional, categorical and hybrid conceptions of constructs.

The Factor Structure and Measurement Invariance of Positive and Negative Affect

2016 ◽  
Vol 32 (4) ◽  
pp. 265-272 ◽  
Author(s):  
Mohsen Joshanloo ◽  
Ali Bakhshi
Keyword(s):  
Negative Affect ◽  
Measurement Invariance ◽  
Factor Structure ◽  
Factor Model ◽  
Positive And Negative Affect ◽  
Cultural Groups ◽  
Invariance Testing ◽  
The Usa ◽  
Scalar Invariance ◽  
Gender Groups

Abstract. This study investigated the factor structure and measurement invariance of the Mroczek and Kolarz’s scales of positive and negative affect in Iran (N = 2,391) and the USA (N = 2,154), and across gender groups. The two-factor model of affect was supported across the groups. The results of measurement invariance testing confirmed full metric and partial scalar invariance of the scales across cultural groups, and full metric and full scalar invariance across gender groups. The results of latent mean analysis revealed that Iranians scored lower on positive affect and higher on negative affect than Americans. The analyses also showed that American men scored significantly lower than American women on negative affect. The significance and implications of the results are discussed.

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