A New Perspective on the Effects of Covariates in Mixture Models

Stegmann, Gabriela; Grimm, Kevin J.

doi:10.1080/10705511.2017.1318070

Cited by 15 publications

(16 citation statements)

References 20 publications

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“…Specifically, we conducted 4 The focus of this study was to examine gender differences in distributions of the joint STVs trajectory profiles and explain how these patterns affect gender imbalance in STEM participation (as opposed to exploring whether different groups of students followed distinct trajectories of STVs while controlling for gender). Methodologically, adding covariates to the GMM generally hurt class recovery (i.e., participants were more accurately classified), except where the latent classes underlying the growth trajectories and covariates were strongly associated (Stegmann & Grimm, 2018). In this study, the latent trajectory classes (based on three-profile solution) were only weakly associated with GPAs (R 2 = 3%-4% by ANOVA) and not associated with SES (R 2 < .01%).…”

Section: Pattern-centered Approach To Stv Trajectoriesmentioning

confidence: 51%

Joint trajectories of task value in multiple subject domains: From both variable- and pattern-centered perspectives

Guo

Wang

Ketonen

et al. 2018

Contemporary Educational Psychology

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This study used variable-and pattern-centered approaches to better capture the impact of adolescents' joint developmental trajectories of subjective task values (STVs) in three domains (Finnish, math and science, and social subject) from grades 9 to 11 on science, technology, engineering, and mathematics (STEM) aspirations at four years postsecondary school and STEM participation at six years postsecondary school (N = 849 Finnish youth; 52.1% female; 99% native Finnish). Results showed that while adolescents' average STVs in different domains remained stable, three differential joint STV trajectories emerged across domains. Individual changes of STVs in one domain shaped STVs in other domains to form unique relative STV hierarchies within subgroups that impacted long-term STEM aspirations and participation. Gender differences in STV trajectory profile distributions partially explained the overall underrepresentation of women in STEM fields. This study is among the first to incorporate multiple domains and explore how STVs fluctuate over time in both homogeneous and heterogeneous fashions. These findings underscore the importance of examining heterogeneity in motivational trajectories across domains.

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Section: Pattern-centered Approach To Stv Trajectoriesmentioning

confidence: 51%

Joint trajectories of task value in multiple subject domains: From both variable- and pattern-centered perspectives

Guo

Wang

Ketonen

et al. 2018

Contemporary Educational Psychology

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“…S. Kim & Wang, 2018). Stegmann and Grimm (2018) found that including covariate effect on the latent class variable improved the correct class assignment in GMM only when the covariate effects were strong and class separation was large. When the covariate effects became weak or classes were less separated, the unconditional GMM performed better than the GMM with covariates in terms of correct class assignment.…”

Section: Inclusion Of Covariates In Factor Mixture Modelingmentioning

confidence: 99%

“…Inclusion of covariates has been discussed in various types of mixture modeling, such as LCA (Nylund-Gibson & Masyn, 2016), regression mixture modeling (M. Kim et al, 2016), FMM (Lubke & Muthén, 2005, 2007), GMM (e.g., Asparouhov & Muthén, 2014; Stegmann & Grimm, 2018; Tofighi & Enders, 2008), and mixture item response theory models (Lee & Beretvas, 2014; Maij-de Meij et al, 2010; Tay et al, 2011). This section briefly reviews results of previous studies regarding the inclusion of covariates in FMM, as well as GMM and mixture item response theory models where there is a measurement model comparable to that in FMM.…”

Section: Inclusion Of Covariates In Factor Mixture Modelingmentioning

confidence: 99%

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

Wang

Kim

Ferron

et al. 2020

Educational and Psychological Measurement

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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.

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“…Once the number of latent classes has been decided, covariates can be used to predict class membership. Although covariates can be included in the LGM component of the model, this can unintentionally cause classes to be formed based on the covariate distribution instead of the outcome distribution, resulting in a different grouping structure (Stegmann & Grimm, 2018). For example, including the covariate sex in the LGM portion of our model can lead to latent classes being formed primarily on the basis of sex instead of the weight trajectories, defeating the purpose of the analysis.…”

Section: Growth Mixture Modelmentioning

confidence: 99%

A comparison of three approaches for identifying correlates of heterogeneity in change

Serang

2021

New Directions for Child and Adolescent Development

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Longitudinal research is often interested in identifying correlates of heterogeneity in change. This paper compares three approaches for doing so: the mixed-effects model (latent growth curve model), the growth mixture model, and structural equation model trees. Each method is described, with special focus given to how each structures heterogeneity, attributes that heterogeneity to covariates, and the kinds of research questions each can be used to address. Each approach is used to analyze data from the National Longitudinal Survey of Youth to understand the similarities and differences between methods in the context of empirical data. Specifically, changes in weight across adolescence are examined, as well as how differences in these change patterns can be explained by sex, race, and mother's education. Recommendations are provided for how to select which approach is most appropriate for analyzing one's own data. K E Y W O R D S growth mixture model, latent growth curve model, longitudinal, mixed-effects model, structural equation model trees Longitudinal research is undertaken for a number of reasons. Baltes and Nesselroade (1979) highlighted five main rationales for using longitudinal research within developmental psychology: (1) identification of intraindividual change, (2) identification of interindividual differences in intraindividual change, (3) analysis of interrelationships in behavioral change, (4) analysis of causes (determinants) of intraindividual change, and (5) analyses of causes (determinants) of interindividual differences in intraindividual change. This paper will focus primarily on the fifth rationale, comparing three statistical approaches for implementing it.

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A New Perspective on the Effects of Covariates in Mixture Models

Cited by 15 publications

References 20 publications

Joint trajectories of task value in multiple subject domains: From both variable- and pattern-centered perspectives

Joint trajectories of task value in multiple subject domains: From both variable- and pattern-centered perspectives

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

A comparison of three approaches for identifying correlates of heterogeneity in change

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