<b>poLCA</b>: An<i>R</i>Package for Polytomous Variable Latent Class Analysis

Linzer, Drew A.; Lewis, Jeffrey B.

doi:10.18637/jss.v042.i10

Cited by 1,062 publications

(863 citation statements)

References 36 publications

Supporting

Mentioning

786

Contrasting

Unclassified

Order By: Relevance

“…The function returns results including the BIC, the AIC, the likelihood function, the 2 (Likelihood ratio/deviance statistic), the Chi-square statistic, number of estimated parameters, the estimated class-conditional response probabilities, and a matrix containing each observation's posterior class membership probabilities. The latent class model does not automatically determine the number of latent classes in a given data set; but it does offer a variety of parsimony and goodness of fit statistics that researchers may use to make a theoretically and empirically sound assessment (20). Generally, the goal is to select models that maximize the likelihood, minimize the BIC, Chi-square or 2 , while retaining a parsimonious model.…”

Section: Discussionmentioning

confidence: 99%

Cluster analysis of behavioural weight management strategies and associations with weight change in young women: a longitudinal analysis

et al. 2015

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Cluster analysis of behavioural weight management strategies and associations with weight change in young women: a longitudinal analysis

et al. 2015

View full text Add to dashboard Cite

“…Free programs are LEM (Vermunt, 1997) and various packages developed for R, a free software environment for statistical computing. These include the packages poLCA (Linzer & Lewis, 2011, 2013, MCLUST (Fraley, Raftery, Murphy, & Scrucca, 2012), depmixS4 (Visser & Speekenbrink, 2010), mixtools (Benaglia, Chauveau, Hunter, & Young, 2009), and BayesLCA (White & Murphy, 2014).…”

Section: Software Implementationmentioning

confidence: 99%

Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis

Hickendorff

Edelsbrunner

McMullen

et al. 2018

Learning and Individual Differences

185

118

View full text Add to dashboard Cite

Highlights• learning is often multidimensional, heterogeneous, and discontinuous• traditional statistical analyses are limited in capturing this complexity• latent class and latent profile models identify subgroups of learners• latent transition models characterize discontinuous, non-linear, learning paths• these models contribute to our understanding of learning and individual differences 3 Informative Tools for Characterizing Individual Differences in Learning: Latent Class, Latent Profile, and Latent Transition AnalysisThis article gives an introduction to latent class, latent profile, and latent transition models for researchers interested in investigating individual differences in learning and development. The models allow analyzing how the observed heterogeneity in a group (e.g., individual differences in conceptual knowledge) can be traced back to underlying homogeneous subgroups (e.g., learners differing systematically in their developmental phases). The estimated parameters include a characteristic response pattern for each subgroup, and, in the case of longitudinal data, the probabilities of transitioning from one subgroup to another over time. This article describes the steps involved in using the models, gives practical examples, and discusses limitations and extensions. Overall, the models help to characterize heterogeneous learner populations, multidimensional learning outcomes, nonlinear learning pathways, and changing relations between learning processes. The application of these models can therefore make a substantial contribution to our understanding of learning and individual differences.

show abstract

“…If the independence assumption is not relaxed, the model reduces to an independent finite mixture model, as stated above. An independent finite mixture model for categorical variables is also known as LCA (Agresti, 2014;Collins & Lanza, 2013;Hagenaars & McCutcheon, 2002;Linzer & Lewis, 2011;Vermunt & Magidson, 2004;Vermunt, Tran, & Magidson, 2008). LCA has been used in various social science applications including econometrics, behavioural psychology, social psychology, biometrics, and consumer behaviour, among others.…”

Section: Ignoring Temporal Dependencies In the Data: Hmm Versus Lcamentioning

confidence: 99%

A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies

2017

View full text Add to dashboard Cite

ABSTRACT:Interactive learning environments with body-centric technologies lie at the intersection of the design of embodied learning activities and multimodal learning analytics. Sensing technologies can generate large amounts of fine-grained data automatically captured from student movements. Researchers can use these fine-grained data to create a high-resolution picture of the activity that takes place during these student-computer interactions and explore whether the sequence of movements has an effect on learning. We present a use-case modelling of temporal data in an interactive learning environment with hand gestures, and discuss some validity threats if temporal dependencies are not accounted for. In particular, we assess how, if ignored, the temporal dependencies in the measurement of hand gestures might affect the goodness of fit of the statistical model and would affect the measurement of the similarity between elicited and enacted movement. Our findings show that accounting for temporality is crucial for finding a meaningful fit to the data. In using temporal analytics, we are able to create a high-resolution picture of how sensorimotor coordination correlates with learning gains in our learning system.

show abstract

poLCA: AnRPackage for Polytomous Variable Latent Class Analysis

Cited by 1,062 publications

References 36 publications

Cluster analysis of behavioural weight management strategies and associations with weight change in young women: a longitudinal analysis

Cluster analysis of behavioural weight management strategies and associations with weight change in young women: a longitudinal analysis

Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis

A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies

Contact Info

Product

Resources

About