Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters

Lubke, Gitta H.; Muthén, Bengt

doi:10.1080/10705510709336735

Cited by 487 publications

(315 citation statements)

References 28 publications

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“…Note that relatively smaller residual variances will increase the separation between classes based on the manifest variables and relatively larger residual variances will decrease separation (Lubke & Muthén, 2007). For example, Table A3 illustrates how MD varies through changing only the residual variances-the manifest values for y1-y4 remain constant in this example.…”

Section: Appendixmentioning

confidence: 99%

Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation.

Depaoli

2013

Psychological Methods

114

147

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Growth mixture modeling (GMM) represents a technique that is designed to capture change over time for unobserved subgroups (or latent classes) that exhibit qualitatively different patterns of growth. The aim of the current article was to explore the impact of latent class separation (i.e., how similar growth trajectories are across latent classes) on GMM performance. Several estimation conditions were compared: maximum likelihood via the expectation maximization (EM) algorithm and the Bayesian framework implementing diffuse priors, "accurate" informative priors, weakly informative priors, data-driven informative priors, priors reflecting partial-knowledge of parameters, and "inaccurate" (but informative) priors. The main goal was to provide insight about the optimal estimation condition under different degrees of latent class separation for GMM. Results indicated that optimal parameter recovery was obtained though the Bayesian approach using "accurate" informative priors, and partial-knowledge priors showed promise for the recovery of the growth trajectory parameters. Maximum likelihood and the remaining Bayesian estimation conditions yielded poor parameter recovery for the latent class proportions and the growth trajectories.

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Section: Appendixmentioning

confidence: 99%

Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation.

Depaoli

2013

Psychological Methods

114

147

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“…This process can be facilitated by the reliance on a graphical depiction of these indicators (i.e., an elbow plot). Another useful indicator, which should not be used to select the optimal number of profiles, is the entropy (Lubke & Muthén, 2007). The entropy is typically used once the optimal number of profiles has been retained to describe the classification accuracy of the solution.…”

Section: The Present Studymentioning

confidence: 99%

Achievement goal profiles among adolescent males and females.

Litalien

Morin

McInerney

2017

Developmental Psychology

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Achievement goal theory has long been a dominant model in the study of student motivation. However, a relatively small number of researchers have investigated gender differences in achievement goals or have considered the possible role that social and extrinsic goals may play in student academic motivation. Adopting a person-centered multiple goals perspective based on personal investment theory, this longitudinal study investigated whether males and females shared similar goal profiles, and whether the predictors (facilitating conditions) and outcomes (learning processes, task perseverance, and future aspirations) of these profiles were equivalent across genders. Profiles were extracted from 8 types of academic goals, based on a large sample of Hong Kong high school students (N = 7,848). Findings revealed 5 distinctive profiles for both males and females. Although the relative size of these profiles differed across samples of male and female students, the results show that 4 of these profiles were mostly equivalent across genders. Predictors of membership into these profiles were also equivalent across genders, whereas their relative outcomes were specific to gender.

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“…In addition, other conditions may exist that affect power estimates. For example, power for factor mixture models, in which the structure among variables is specified to hold within each of the derived latent classes, is more complex and is influenced by a number of factors, including the mixing proportion and the separation among clusters (Lubke & Neal, 2006) and model size, covariates, and class-specific parameters in factor mixture models (Lubke & Muthén, 2007). to the eigenvalues of Σ. The following three components determine the geometric features of the observed data: λ parameterizes the volume of the observation, D indicates the orientation, and A represents the shape of the observation.…”

Section: Appendix Amentioning

confidence: 99%

Finding groups using model-based cluster analysis: Heterogeneous emotional self-regulatory processes and heavy alcohol use risk.

Mun¹,

Eye²,

Bates³

et al. 2008

Developmental Psychology

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Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of non-nested models using the Bayesian Information Criterion (BIC) to compare multiple models and identify the optimum number of clusters. The current study clustered 36 young men and women based on their baseline heart rate (HR) and HR variability (HRV), chronic alcohol use, and reasons for drinking. Two cluster groups were identified and labeled High Alcohol Risk and Normative groups. Compared to the Normative group, individuals in the High Alcohol Risk group had higher levels of alcohol use and more strongly endorsed disinhibition and suppression reasons for use. The High Alcohol Risk group showed significant HRV changes in response to positive and negative emotional and appetitive picture cues, compared to neutral cues. In contrast, the Normative group showed a significant HRV change only to negative cues. Findings suggest that the individuals with autonomic selfregulatory difficulties may be more susceptible to heavy alcohol use and use alcohol for emotional regulation. KeywordsModel-based Cluster Analysis; Mixture Model; Emotional Regulation; Heart Rate Variability (HRV); Alcohol Use Finding or classifying groups of individuals who are similar to one another within a group but sufficiently different from those of other groups has been a key interest in the behavioral sciences. Cluster analysis is a widely used approach for this purpose. It is empirically driven and exploratory in the sense that the number of groups and nature of the groups are unknown in advance (Everitt, Landau, & Leese, 2001;Webb, 2002). In the field of developmental psychology, a number of influential studies have found clusters of individuals based on their early temperamental and behavioral styles, and characterized their long-term patterns of behavioral outcomes, including the classic work on three temperamental styles from the New York Longitudinal Study (easy, difficult, slow to warm up;Thomas, Chess & Birch, 1968) and the more contemporary work from the Dunedin Multidisciplinary Health and Development (undercontrolled, inhibited, well-adjusted;Caspi, 2000;Caspi & Silva, 1995 NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptFurthermore, recent literature has increasingly postulated that qualitatively heterogeneous subpopulations exist in developmental pathways, and heterogeneous causal processes exist for subpopulations. Evidence of heterogeneity consisting of normative as well as atypical processes can be found in many behaviors, including depressive symptoms, aggressive behaviors, and substance use behaviors (for a brief review, see Mun, Windle, & Schainker, 2008). In addition, identifying heterogeneous subpopulations can be useful for determining individuals who may be at different stages of development. For example, in the domain of cognitive stage develo...

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Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters

Cited by 487 publications

References 28 publications

Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation.

Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation.

Achievement goal profiles among adolescent males and females.

Finding groups using model-based cluster analysis: Heterogeneous emotional self-regulatory processes and heavy alcohol use risk.

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