Latent Class Cluster Analysis

Vermunt, Jeroen K.; Magidson, Jay

doi:10.1017/cbo9780511499531.004

Cited by 1,486 publications

(1,477 citation statements)

References 23 publications

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“…LPA uses maximum likelihood estimation to probabilistically assign participants to latent classes (profiles) 1 . No restrictions were imposed on covariance between observable indicators because restricted models, which restrict inter-indicator covariance to 0, often overestimate the number of classes and provide less parsimonious solutions (Vermunt & Magidson, 2002). The fit of two-class through fiveclass unrestricted models was evaluated using three information criterion indices: the Akaike information criterion (AIC; Akaike, 1987), the Bayesian information criterion (BIC; Schwartz, 1978), and the sample-size adjusted BIC (SSABIC; Sclove, 1987).…”

Section: Statistical Analysesmentioning

confidence: 99%

“…However, this analysis is ambiguous because there is no consensus regarding reliable analytic techniques for class enumeration in cluster analysis, i.e., the determination of the optimal number of clusters in a sample. This renders cluster analysis inferior to finite mixture modeling techniques, such as LCA and latent profile analysis (LPA), which utilize strict criteria for class enumeration and are widely regarded as superior for the identification of latent classes (Vermunt & Magidson, 2002). Terhune and Cardeña (2010a) applied LPA to participants' spontaneous experiential responses to a hypnotic induction and found evidence for four response classes, two of which included HS participants.…”

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confidence: 99%

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Discrete response patterns in the upper range of hypnotic suggestibility: A latent profile analysis

Terhune

2015

Consciousness and Cognition

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Section: Statistical Analysesmentioning

confidence: 99%

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confidence: 99%

Discrete response patterns in the upper range of hypnotic suggestibility: A latent profile analysis

Terhune

2015

Consciousness and Cognition

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“…Although cluster analysis has been the most commonly used person-centered method, it has several shortcomings including the lack a clear benchmarks or statistics for determining how well the solution fits the data. As such, the number of classes in cluster analysis is somewhat arbitrary (Yang et al 2005;Vermunt and Magdison 2002). LCA, on the other hand, enables researchers to identify discrete latent variables that best group individuals based on their scores from two or more discrete observed variables (McCutcheon 1987).…”

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confidence: 99%

“…As such, the number of classes in cluster analysis is somewhat arbitrary (Yang et al 2005;Vermunt and Magdison 2002). LCA, on the other hand, enables researchers to identify discrete latent variables that best group individuals based on their scores from two or more discrete observed variables (McCutcheon 1987).…”

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confidence: 99%

Empirically Derived Subtypes of Child Academic and Behavior Problems: Co-Occurrence and Distal Outcomes

Reinke

Herman

Petras

et al. 2008

J Abnorm Child Psychol

141

152

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The aim of this study was to identify classes of children at entry into first grade with different patterns of academic and behavior problems. A latent class analysis was conducted with a longitudinal community sample of 678 predominantly low-income African American children. Results identified multiple subclasses of children, including a class with co-occurring academic and behavior problems. Gender differences were found in relation to the number of identified classes and the characteristics of academic and behavior problems for children. Several of the identified classes, particularly the co-occurring academic and behavior problems subclass for both genders, predicted negative long-term outcomes in sixth grade, including academic failure, receipt of special education services, affiliation with deviant peers, suspension from school, and elevated risk for conduct problems. The finding that subclasses of academic and behavior problems predict negative long-term outcomes validates the importance of the identified classes and the need to target interventions for children presenting with the associated class characteristics. Implications for early identification, prevention, and intervention for children at risk for academic failure and disruptive behavior problems are discussed. KeywordsAcademic underachievement; Behavior problems; Latent class analysis Academic underachievement and disruptive behavior are serious problems afflicting a large number of children and adolescents. Separately, academic and behavior problems have been associated with substantial costs to families and society. Much has been written about the deleterious outcomes associated with life course persistent or early starter pathways to antisocial behavior problems (Patterson et al. 1991;Moffitt 1993). In particular, children with early behavior problems have an elevated risk for academic failure, peer rejection, substance abuse, and delinquency. Research has also shown that early learning problems by themselves predict future academic underachievement, school drop out, and other negative NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript outcomes. Furthermore, learning deficits are costly in terms of expenses related to special education services and the personal suffering and frustration for children and families. Likewise, disruptive behavior problems contribute to loss of instruction time in the classroom (Arnold 1997), frustration for children and families, and considerable societal burden associated with antisocial acts, including delinquency and harm to others (Reid et al. 2002). Additionally, both behavioral and learning problems are quite refractory to typical interventions (see Hinshaw 1992b). Together academic and behavior problems are a significant public health burden, comprising major psychological, economic, and social problems.Understanding and identifying the academic and behavioral characteristics associated with children at increased risk for these problems early in development can inform preve...

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“…One can consider two distinct venues to approach the issue of heterogeneity in such multiple group models: Either heterogeneity (i.e., group membership) is assumed to be latent or assumed to be observed. If group membership is latent, researchers can rely on latent class cluster analysis (spanning latent profile analysis, latent class analysis, finite mixture models; see Vermunt & Magidson, 2002) which has the goal of finding similar data patterns when group membership is unobserved and probabilistic. That is, observed data are assumed to be drawn from a mixture of underlying probability distributions.…”

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confidence: 99%

Theory-guided exploration with structural equation model forests.

Brandmaier

Prindle

McArdle

et al. 2016

Psychological Methods

128

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Structural equation model (SEM) trees, a combination of SEMs and decision trees, have been proposed as a data-analytic tool for theory-guided exploration of empirical data. With respect to a hypothesized model of multivariate outcomes, such trees recursively find subgroups with similar patterns of observed data. SEM trees allow for the automatic selection of variables that predict differences across individuals in specific theoretical models, for instance, differences in latent factor profiles or developmental trajectories. However, SEM trees are unstable when small variations in the data can result in different trees. As a remedy, SEM forests, which are ensembles of SEM trees based on resamplings of the original dataset, provide increased stability. Because large forests are less suitable for visual inspection and interpretation, aggregate measures provide researchers with hints on how to improve their models: (a) variable importance is based on random permutations of the out-of-bag (OOB) samples of the individual trees and quantifies, for each variable, the average reduction of uncertainty about the model-predicted distribution; and (b) case proximity enables researchers to perform clustering and outlier detection. We provide an overview of SEM forests and illustrate their utility in the context of cross-sectional factor models of intelligence and episodic memory. We discuss benefits and limitations, and provide advice on how and when to use SEM trees and forests in future research.

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Latent Class Cluster Analysis

Abstract: Latent class cluster analysisVermunt, J.K.; Magidson, J.

Cited by 1,486 publications

References 23 publications

Discrete response patterns in the upper range of hypnotic suggestibility: A latent profile analysis

Discrete response patterns in the upper range of hypnotic suggestibility: A latent profile analysis

Empirically Derived Subtypes of Child Academic and Behavior Problems: Co-Occurrence and Distal Outcomes

Theory-guided exploration with structural equation model forests.

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